Why AI Infrastructure must evolve for Agent Experience — Akshat Bubna, Modal CTO

We’ve been running a bit of an Agent Cloud series surveying all the top inference/compute/cloud providers, from Databricks to Daytona to Railway and, even further back, E2B, but we’re excited to conclude this series returning to Modal, which has just raised a monster $355M Series C.

The cloud was built for developers. But agents are now changing that.

The old infra stack was designed for a human who could read docs, reason through YAML, and understand dashboards to figure out what they need when something broke. While this was painful for developers, it worked since they could fill in missing context in their heads.

However, agents don’t have that luxury. Now in this new era of agents, everything has to be tighter.

They need a place to write code, run it, inspect the output, change the environment, debug failures, and try again. Fast iteration and feedback loops with all the necessary context are crucial for agents to operate properly. Furthermore, sandboxes are a clear representation of this shift as agents can easily spin up isolated environments. This programmatic infra even extends to research:

Two years ago, we were one of the first to cover Modal with CEO Erik Bernhardsson and Alessio designed our favorite LS thumbnail of all time:

At the time, Modal was just a teeny little company with a $17M Series A.

Today, fresh off their $355M Series C, Modal is one of the clearest examples of the agent cloud future being built in real time: a cloud platform moving past traditional web app assumptions toward the workloads AI actually creates such as elastic inference, sandboxes, GPU burst, post-training, background agents, and infrastructure that agents themselves can operate.

In this episode, Modal CTO Akshat Bubna joins swyx and Vibhu to unpack why AI applications don’t fit traditional cloud assumptions, why Kubernetes was never designed for bursty compute-heavy workloads, and why Modal is now shifting from developer experience to agent experience.

We go deep on Modal’s AI infra stack: serverless functions, decorator-based infrastructure, elastic inference for custom models, GPU snapshotting, DeFlash, speculative decoding, Auto Endpoints, sandboxes, persistent storage, networked containers, private IPv6, RDMA, multi-node training, and Modal’s capacity pool across 17 cloud providers. Akshat also explains why RL rollouts can require 100,000 sandboxes, why production agents need hard guardrails, why observability may matter more than reading code, and why AI has made infrastructure exciting again.

We discuss:

* Why Kubernetes wasn’t built for bursty AI workloads

* How Modal started as a better runtime before becoming an AI cloud

* Why Modal added GPUs before ChatGPT

* The shift from developer experience to agent experience

* Why observability matters when agents are writing the code

* Elastic inference for custom models across audio, video, robotics, and comp bio

* GPU snapshotting, cold starts, and why inference workloads are so bursty

* Why RL rollouts can require 100,000 sandboxes

* DeFlash, speculative decoding, and frontier-level inference performance

* Auto Endpoints and making optimized inference easier to deploy

* What Modal adds beyond vLLM, SGLang, and raw GPU rental

* Modal’s 17-cloud capacity pool and supercloud strategy

* Networked sandboxes, sidecars, private IPv6, and RDMA

* Serverless multi-node training for post-training and research workloads

* Auto-research, model-guided sweeps, and agents launching GPU experiments

* Compute strategy, capacity planning, and batch tiers

* Why production agents need specialized sandboxes and hard guardrails

* Modal’s take on managed agents, CI, Gitpod/Ona, Python, TypeScript, and Modal Bench

Akshat Bubna

* LinkedIn: https://www.linkedin.com/in/akshat-bubna-188885103

* X: https://x.com/akshat_b

Modal

* Website: https://modal.com

Timestamps

00:00:00 Introduction

00:00:39 Modal’s origin and why Kubernetes wasn’t enough

00:04:32 Developer Experience → Agent Experience

00:06:21 Modal’s AI cloud primitives

00:09:14 Sandboxes, agent loops, and proto-Cognition

00:12:12 Elastic inference, GPU snapshotting, and 100,000 sandboxes

00:15:24 DeFlash, speculative decoding, and Auto Endpoints

00:19:59 Production-grade inference beyond raw GPUs

00:22:00 Background agents, Ramp Inspect, and the agent lifecycle

00:24:08 Modal’s 17-cloud supercloud strategy

00:26:40 Networked sandboxes, private IPv6, and RDMA

00:32:48 Multi-node training, post-training, and auto research

00:37:36 Compute strategy, capacity planning, and batch tiers

00:40:55 Open models, real-time AI, and production agent infra

00:43:06 Hard guardrails, managed agents, and specialized sandboxes

00:46:06 Why AI made infrastructure exciting again

00:48:30 Model APIs, differentiated products, and agentic video

00:51:50 CI, coding-agent infra, SDKs, and Modal Bench

00:57:28 Closing Thoughts

Transcript

Introduction: Modal, Series C, and the Art Party

Swyx [00:00:00]: We’re here with Akshat, CTO of Modal, together with Vibhu. Congrats on your Series C.

Akshat [00:00:10]: Thank you.

Swyx [00:00:11]: Your party yesterday was amazing.

Akshat [00:00:15]: Yeah.

Swyx [00:00:15]: From all the photos and all the swag.

Akshat [00:00:17]: We had a bunch of art installations, which was fun, seeing, like, our products on pedestals next to, like, Rodin.

Swyx [00:00:25]: Very nice. Very nice. When you started, it was not the GPU inference company. Maybe it was in your mind. Take us back to the origin story.

Modal’s Origin: A New Runtime Beyond Kubernetes

Akshat [00:00:39]: I first met Eric, who’s the CEO, through an investor. Back then Eric was already thinking about building, a new runtime, and he got there thinking through why are workflow orchestration products so hard to use. It’s because you have to run them on Kubernetes. Kubernetes is hard to manage. It’s not built for burstiness and, custom images,

Swyx [00:01:03]: Yeah

Akshat [00:01:03]: It has a terrible developer experience.

Swyx [00:01:05]: And I’ll, I’ll interject

Akshat [00:01:06]: Yeah

Swyx [00:01:07]: For listeners, who are new, we interviewed Eric two years ago, and there’s a bit more of the story there from Spotify and all those things.

Swyx [00:01:14]: And I came across Eric through Data Council because he did that talk on the serverless container stack that you guys did, which was like, that was my first like, “Okay, I need to take Modal very seriously” moment.

Akshat [00:01:26]: Yeah.

Swyx [00:01:26]: But it was still very unclear, like, do I need all this for just my data pipelines?

Akshat [00:01:33]: Yeah. initially what we were thinking about was if we build a better runtime, it’s a very useful primitive in itself. It’s There’s a lot of things that, get solved by serverless functions, like you can do, ETL stuff, you can do job queues, you can do all this, like, bursty processing, which it turns out every company had needs for. but then we also were thinking about this as like, this is a primitive that we can build a whole collection of products on, which are very verticalized. So perhaps data engineering would’ve been the first one, but we were thinking about inference. Back then it was more classical inference, like computer vision stuff and running XGBoosts and whatnot. But we added GPUs to the product a year before ChatGPT came out.

From Serverless Containers to GPU Workloads

Swyx [00:02:19]: Nice.

Akshat [00:02:19]: We just didn’t think it would be that big of a deal.

Swyx [00:02:22]: Yeah, just like add A100.

Vibhu [00:02:23]: Was there any, like, early key problem that really sparked off why you built it?

Akshat [00:02:28]: Yeah. Primarily it’s just, none of the tooling that was out there was built for, one, a really great developer experience, and also there’s a general trend of, a lot of the workloads that we were seeing were very. I wish there was a better word for it, but compute-heavy. Like, they need, one, like, need a lot more resources, so you need to burst up and down a lot, versus like Kubernetes designed for, like, slow scaling and, more for, like, web server use cases. And also there’s just a lot more specialization in, like, what kinds of environments these workloads run in. Like, we had sometimes they need accelerators, sometimes they need different kinds of images, and this is just like a consistent thing that we saw across a lot of companies. That would be the next step.

Software-Defined Infrastructure and Decorator-Based DX

Swyx [00:03:13]: Yeah. Yeah. Be nice. I don’t know how much this factored into the early story, but I wrote a post when I was at Temporal about infrastructure, software-defined infrastructure or something like that.

Akshat [00:03:22]: Yeah, the self-provisioning

Swyx [00:03:23]: Self-provisioning.

Akshat [00:03:24]: Yeah.

Swyx [00:03:24]: Yeah. I can’t even remember my own post.

Swyx [00:03:26]: And then you put me on the landing page.

Akshat [00:03:28]: Yeah. We really like, the term and so we stole it.

Swyx [00:03:32]: Because you had the insight that everything can just be in decorators co-located with the code, right?

Akshat [00:03:37]: Yeah.

Swyx [00:03:37]: Was that a big part of the original

Akshat [00:03:39]: Yes

Swyx [00:03:39]: Story or it was just like a DX layer?

Akshat [00:03:41]: That was, really important because we really didn’t want people to spend, so much time, writing YAML, and it seemed like you could really condense the surface area of what you’re doing, put it in code so you can operate on it just like you operate on other code, and like build stuff that’s more expressive and dynamic. and so yeah, that was always a very important part.

Swyx [00:04:04]: Then the pushback is this is a DSL.

Akshat [00:04:07]: Yeah.

Swyx [00:04:07]: It’s you’re closed source. I am locked into Modal.

Akshat [00:04:11]: Yeah. We never really got pushback for that because the nice thing about Modal is you can bring whatever code you have, and sure, the DSL is at the configuration layer for, what hardware you’re using, how you’re scaling things up, but you still own the code.

Akshat [00:04:27]: And that’s, that’s been an important, part of our story, even as we do inference now.

Swyx [00:04:32]: Yeah.

Vibhu [00:04:32]: How much of do you think still stays the same today? Like if you were to build something today, DevX very important, but I feel like, a lot of this has been changed with just hook it up to an agent, have Claude Code, have Codex implement a tool. there’s very agent native primitives that are different than if I’m doing this myself, right?

Developer Experience → Agent Experience

Akshat [00:04:54]: We’ve changed our SDK team to think about agent experience instead of, developer experience and we think that the same benefits that apply for DX also apply for AX, which is why would you have an agent read through hundreds of Kubernetes files and like write YAML that’s not even typed when it can make a couple of changes in a decorator and it gets this self-provisioning runtime of, being able to see its changes live in action? yeah, it just seems from the customers we talk to, they find Modal is much faster for agents to use versus operating on a different substrate.

Swyx [00:05:34]: Yeah, because like you, again, you co-locate the infrastructure requirements to the code that runs it.

Akshat [00:05:38]: Yeah.

Swyx [00:05:38]: Well, the negative thesis now is that nobody’s looking at their code anymore, so there’s no point.

Akshat [00:05:44]: Yeah, people aren’t looking at code. one thing we still see is really important is observability.

Swyx [00:05:51]: Yeah.

Akshat [00:05:51]: Like how good is your dashboard? And of course, like we have, we push a lot of it to the CLI so the agents can do their own investigation, but you still need humans to go interpret what’s going on and, make judgment calls and whatnot. and that’s I feel like, Maybe more important now than looking at the code itself.

Swyx [00:06:11]: Yes, because like, you can try to treat the code as a black box and then use, see the observable action that comes out of it, and then just prompt a change.

What Modal Is For: AI Cloud Primitives

Akshat [00:06:21]: Yeah.

Swyx [00:06:22]: So I think it takes a bit of restraint to not specialize, to say, “I want to ship a new primitive,” and then just be general purpose.

Swyx [00:06:31]: People ask you, “What are you for?” You’re like, “ I don’t know. We can do this, we can do that.”

Vibhu [00:06:36]: Well, I’d be curious to see, like, okay, if we were to ask you, like, what is Modal for even at a high level? There’s a lot you guys do, sandboxes, GPUs, everything. How do you answer?

Akshat [00:06:46]: Modal is a cloud platform that’s built for, where we’ve built the primitives from scratch for AI applications. and right now it covers, inference, training, batch processing, and sandbox workloads.

Akshat [00:07:00]: But we’re building a lot more

Swyx [00:07:02]: I noticed you didn’t say web server, so there is still a role for, like, the always-on large-scale Kubernetes type things.

Akshat [00:07:09]: Yeah, absolutely. We’re, we’re not trying to compete with the renders of the world, because yeah, we think the differentiator for us is the, are the workloads that need specialized compute, need to scale up and down a lot. yeah, they’re, they’re, they’re just shaped differently.

Working Alongside Frontier Startups

Vibhu [00:07:26]: I think you’re building a lot of it alongside the startups, right? They’re innovating quite a bit, even in your, like, latest blog post. Like, even in the series C, the customers that you mention here, the cognitions, technical ones, ramps and whatnot, they’re, they’re innovating with you, right? And that’s not something AWS is doing directly with.

Akshat [00:07:45]: Yeah, absolutely. I think, this is again classic. We’re a small team. We can move really fast. our engineers are working with our customers and figuring it out. Yeah.

Swyx [00:07:54]: So my first week at Cognition, I walked in, there was someone wearing a Modal shirt. I was like, “What are you doing here?” They’re like, “Yeah, I just. I am embedded inside of Cog.”

Akshat [00:08:05]: Yeah, I think that was Peyton. We sent him over

Swyx [00:08:07]: Yeah.

Akshat [00:08:07]: Because, the latency of communication was too high otherwise.

Swyx [00:08:12]: Yeah, distributed node, you have to - you have to place one and collocate.

Vibhu [00:08:16]: Yeah.

Swyx [00:08:16]: So I had a, I had direct personal experience, right? So I worked on smol developer three years ago. it was inspired by Claude 1. I think you onboarded me at some point, like, just before, and I was like, “Oh, like, I need some bursty compute. Like, I was just gonna try using Modal.” And it was a, it was a pretty pleasant experience. apparently, I showed up in the board meeting, like the analytics.

smol developer, Sandboxes, and Proto-Cognition

Akshat [00:08:39]: Yeah, you blew up on Hacker News and,

Swyx [00:08:41]: Yeah

Akshat [00:08:41]: We got a big traffic spike. I. I think the way you used smol developer was Modal functions for running stuff, which was. Like, the, that was a good use case. but then, yeah.

Swyx [00:08:53]: Yeah. That - So to me, that was proto-cognition.

Akshat [00:08:55]: Right.

Swyx [00:08:56]: If only I had, like, stuck to it.

Swyx [00:08:58]: Like, that was like, if - did you say draw the tech tree

Akshat [00:09:00]: Absolutely

Swyx [00:09:00]: You’re just like, “Yeah, like, probably this will happen.”

Akshat [00:09:02]: Yeah. Like, he was so close. You were just rebuilding upon us

Swyx [00:09:04]: I just didn’t realize.

Akshat [00:09:05]: But the funny story there is at the same time, we were talking to a bunch of customers who needed something like sandboxing.

Swyx [00:09:14]: Yeah.

Akshat [00:09:14]: This is like twenty-three.

Swyx [00:09:15]: Yeah.

Akshat [00:09:16]: So we built

Swyx [00:09:17]: You introduced a new API right after that.

Akshat [00:09:18]: Yeah.

Swyx [00:09:19]: Yes.

Akshat [00:09:19]: Like, we built sandboxes in May of twenty-three before anyone was even knew this was gonna be a thing. And the first example we published was, we took smol developer

Swyx [00:09:28]: Smol developer

Akshat [00:09:28]: And put it in a loop, so the agent can iterate on itself.

Swyx [00:09:33]: Loops are hot these days.

Vibhu [00:09:34]: It’s the looper.

Akshat [00:09:34]: Yeah.

Vibhu [00:09:35]: Loops in. When was this, twenty-three?

Akshat [00:09:38]: Yeah.

Vibhu [00:09:39]: A small check.

Akshat [00:09:39]: Yeah.

Swyx [00:09:39]: It’s like twenty-three. so the. the, those for listeners, like, the problem was the models are not built for any of this, right?

Swyx [00:09:46]: Like, you’re just trying to like. They’re not post-training to understand, like, looping and, like, self-correction and tool calling was there, but, like, also not that great.

Akshat [00:09:55]: Yeah.

Akshat [00:09:55]: I don’t remember if you used tool calling in this one, but yeah, the models would just diverge after like ten iterations and not produce anything meaningful.

Swyx [00:10:03]: Yeah. But like, then. So okay, like now talking to myself three years ago, the answer

Vibhu [00:10:08]: Of course they will get better

Swyx [00:10:09]: Collect all the failures, build benchmark, and then collect all the, examples, build the RL environment

Akshat [00:10:15]: Right

Swyx [00:10:15]: Sell it for like ten billion dollars to Meta.

Swyx [00:10:17]: And then also train a model and then sell that for sixty billion dollars to Elon. And this is

Akshat [00:10:23]: Yeah, of course

Swyx [00:10:23]: The funny machine. Like, it’s like, it’s about the hardware.

Akshat [00:10:28]: It’s hard to have that inherent conviction that the stuff will get that much better.

Swyx [00:10:33]: In retrospect, it’s so f*****g obvious.

Akshat [00:10:36]: Fair enough.

Swyx [00:10:37]: Like, what else were we doing back then? I don’t know. anyway. Yeah. So this. That was the start of your sandboxing journey, right? I feel like it didn’t blow up until, like, last year.

Akshat [00:10:49]: Yeah.

Swyx [00:10:50]: So there was like a couple years of quietness.

Akshat [00:10:52]: Exactly, yeah. We were

Vibhu [00:10:53]: I think very underrated product value. Like, my experience with Modal, Charles, before he had joined Modal, met this guy at a hackathon, and he really insisted we wanted to run some small model, not hosted anywhere, and he’s like, “ there’s this cool company, Modal. They’ll like spin up a GPU sandbox, we can throw it on there. They’ll take a Hugging Face link.” And like there’s so much value just right there, right? Like instant hosting, spin it up, spin it down. It’ll stay cold, but we run the demo a few days later, it’ll come back up and like all this stuff in retrospect, like it’s still what we needed like today.

Akshat [00:11:27]: Yeah, it’s still needed today. workload shapes have changed a lot as, we run stuff for people with really massive production scale and, there it’s it’s not about scaling from zero to one, but it’s how do we scale really elastically, from like thousand to fifteen hundred GPUs very quickly in a given region. It’s the same shape problem.

Elastic Inference, GPU Autoscaling, and Custom Models

Vibhu [00:11:50]: Okay. So you look at, say, Cursor Composer, right?

Akshat [00:11:53]: Yeah.

Vibhu [00:11:53]: They had a. “We’ll do RL on a model every couple hours.” you guys have a whole version of RL inference gym and whatnot.

Vibhu [00:12:01]: When you look at workloads like that, you’re doing train runs where you need to scale up, scale down every hour thousands of GPUs, right? That’s the example for we do need it, right?

Akshat [00:12:12]: Yeah. Well, so I’ll, I’ll take a step back and, maybe talk about like how people use Modal today. because our biggest use case is, elastic inference. And the thing we first found product market fit, with was inference for custom models. So we stayed away from the LLM space, and we were serving companies like Suno for audio, Runway for video, robotics, comp bio companies that train their own model elsewhere. But Modal is the best black box that for deployment, scaling to however many GPUs you need as your traffic pattern changes. And we saw all of them like have a very unpredict- predict- predictable, traffic pattern. it’s like diurnal. It’s Some days, like the company will do a launch and, they’ll need like, way more. And it’s not just one model that they deploy. They-- all these companies deploy, lots of different models in different regions, and so the autoscaling problem becomes even harder because then you have to scale within a certain region, and those cycles are offset. So different times you scale up in different regions.

Akshat [00:13:20]: So that’s like our sort

Vibhu [00:13:22]: And that

Akshat [00:13:22]: Yeah

Vibhu [00:13:22]: That in and of itself is a huge category. There’s a bunch of inference providers which, provide this fireworks, does this as a service together, whatnot, Base10. that’s carved into its own niche for language models, at least right now.

Akshat [00:13:36]: Yeah. the thing that we have specialized in is the autoscaling aspect.

Vibhu [00:13:41]: Yeah.

Akshat [00:13:41]: Because we found that it’s not universally true that everyone else can autoscale, and we’ve gone deeper into it on the tech side by, we’ve incorporated GPU snapshotting into the product so we can take the GPU state, like your torch.compile model, snapshot it, and the next cold start is way faster. And so going back to your question, it’s That’s why you need a lot of burstiness for inference. But then people also do a lot of demand training, like for RL stuff, your rollouts are bursty, as you said. People also do a lot of batch jobs. So we’ll see, a lot of companies, before they have a training run, they’ll need thousands of GPUs to run encoding or something like that. And I think those things are much more bursty than. I agree that agents are not that bursty. sandboxes are, except when you’re doing RL. RL is just

RL, Batch Jobs, and 100,000 Sandboxes

Vibhu [00:14:28]: Or commerce

Akshat [00:14:28]: Insanely bursty.

Vibhu [00:14:29]: Yeah.

Akshat [00:14:30]: Yeah. Like when you’re doing, rollouts, you sometimes need a hundred thousand sandboxes in your sandboxes.

Vibhu [00:14:37]: Yeah. I’m curious if you’ve seen early sparks of continual learning. There are some people, like our friends, ngram, recently announced this

Akshat [00:14:45]: Yeah

Vibhu [00:14:45]: They’re, they’re trying to do training. That also seems like a different workload, right? If you’re doing training twenty-four/seven per se, there’s a very weird dynamic of how you’re using GPUs between people and whatnot, but seems like something you guys would work for.

Akshat [00:15:00]: As you said, we’re, we’re fortunate to work with a number of, customers at the frontier and grab some of our customers. and they are taking the primitives we have, and trying to use them in very interesting ways, like continual learning. It’s possible as the stuff gets better, some of that will be part of, our offering as well if, more people need it. but we’re, we’re just waiting to see

Vibhu [00:15:23]: Yeah

Akshat [00:15:23]: How it shakes out.

Vibhu [00:15:24]: Is there a primitive that you added after sandboxing that was the next step in the story?

LLM Inference, DeFlash, and Speculative Decoding

Akshat [00:15:32]: I guess we’ve been going much deeper into LLM inference

Vibhu [00:15:35]: Yeah

Akshat [00:15:35]: Because we realized that some of the advantages we have with like autoscaling, again, especially in different regions and whatnot, are, not present elsewhere. and the place where we had a gap was we weren’t, working on the model layer itself. Like we were a black box. And, we realized that, we can get to frontier-level model performance, with, by having great people who work on this. And, we’ve been open sourcing a lot of our work, in terms of, Recently, we, shared our work on DeFlash, which is a block-based, speculator, and we’ve open sourced, all of it. So, you can - By using open source DeFlash, you can get the same performance as you would with one of the proprietary providers. And the next thing we’re thinking about here

Vibhu [00:16:23]: I thought this was

Akshat [00:16:24]: Yeah

Vibhu [00:16:24]: An interesting blog post as well, right? Like, I think in here you make a claim that. Not a claim, just that how effective speculative deco-decoding really just get to.

Akshat [00:16:33]: Yeah.

Vibhu [00:16:33]: Anything you wanna point out from this around, what people should know?

Akshat [00:16:39]: Yeah, absolutely. the high-level summary is, it would help to describe what speculative decoding is.

Vibhu [00:16:44]: Yes.

Akshat [00:16:44]: I will, yes.

Vibhu [00:16:45]: I think, like

Akshat [00:16:46]: Yeah

Vibhu [00:16:46]: So we’ve covered like Eagle and all this

Akshat [00:16:47]: Yeah

Vibhu [00:16:47]: Like Hydra and all those things, but it was like two years ago.

Akshat [00:16:51]: Yeah.

Vibhu [00:16:51]: I think it doesn’t hurt, right?

Akshat [00:16:52]: Yeah. Speculative decoding is you have a smaller model, called a draft model, predict tokens ahead of the bigger model, and then you have the bigger model, verify all of this, all the tokens are predicted. And the reason it’s faster is if you’re predicting, one token at once, you’re bound by memory bandwidth. But if you can batch the verification of, the draft model, then you’re much more efficient using compute, and it’s faster, and as long as your draft model is producing a lot of tokens that can get accepted, which is called the accept length, you can get a speed up that’s, multiple times of, the original model speed. and well, that’s what we highlight here. It’s Like people talk a lot about we made these kernels faster and whatnot, but improving kernel will only give you like few percentage points of improvement, and, increasing accept length, literally is a multiplicative decrease

Vibhu [00:17:47]: Like two to four X.

Akshat [00:17:48]: Yeah, exactly.

Vibhu [00:17:48]: Without much head-on performance.

Akshat [00:17:50]: Yeah. I think it may - you are running a second model, right? So it may be something more expensive in the compute,

Vibhu [00:17:57]: I meant quality performance

Akshat [00:17:58]: Probably not by much

Vibhu [00:17:58]: But yeah. I think

Akshat [00:17:59]: So there’s no drop in quality performance

Vibhu [00:18:01]: Yeah

Akshat [00:18:01]: Because you’re always. You’re never accepting a token that the big model

Vibhu [00:18:04]: It’s strictly better

Akshat [00:18:05]: Yeah

Vibhu [00:18:05]: Or it’s same.

Akshat [00:18:06]: Exactly.

Vibhu [00:18:07]: Right. Yeah.

Akshat [00:18:08]: And so we’ve been working a bunch on DeFlash, which is a block-based speculator. so it’s instead of predicting, one token at a time, it’s predicting a block. And we’ve been open sourcing our work with it. The next thing for us here is for helping people train speculators and custom models. it’s it’s something that traditionally is very forward-deployed engineering driven, support deployed, engineer driven, like you work with customers and help them do that. And our vision for. This is why we launched Auto Endpoints, is we want to make frontier-level performance available to everyone. And so, we mentioned this in the announcement, we teased it. The next thing we’re, we’re launching is, as you run an auto endpoint, we shadow traffic

Auto Endpoints and Frontier-Level Performance

Vibhu [00:18:54]: Do you want to explain what auto endpoints are?

Akshat [00:18:57]: Yeah.

Vibhu [00:18:57]: I lovely, yeah.

Akshat [00:18:58]: Yeah. So, this is, I guess, going back to your Modal is you touch the code, but, sometimes people don’t wanna touch the code, and they wanna get started with an endpoint that works and has all the great performance and, scalability that Modal has. So we’ve made that easier with, a way to create an endpoint from our UI, from the CLI, that has all of our optimizations that we talked about, like the DeFlash stuff already baked in, and there’s full transparency. So we give you the code, you can go run it yourself, and if you want, you can eject out into the full Modal experience, which we see as people get sophisticated, they do wanna tweak the models, they wanna, fine-tune stuff. You can still do all of that. It’s it’s not a black box. And yeah, the next thing, as we teased later in the post, is how do we give you value even beyond this in terms of having your draft models evolve as your data distribution evolves, again, without having to talk to a person and, yeah.

Vibhu [00:19:59]: I guess just to understand it directly, you have the GPUs, you have an endpoint that’s compatible, you serve open model. If someone was to do this themselves, what’s the delta that you guys provide? So you do a lot of open source great work on effective inference. how does it compare to, say, I take the same model, 5.2 FP8, take shelf inference engine, vLLM, SGLang, get compute of similar capacity, similar cost. What’s the delta that plugging into something this, like this offers outside of the benefit of, scaling?

Production Inference Beyond Raw GPUs

Akshat [00:20:34]: It’s interesting because we’ve taken the approach of open sourcing our contributions and upstreaming them. we work closely with the SGLang team. We want the improvements that our team, comes up with to be, there in open source for others to use, even outside of Modal. The benefit to us is we have a team that has significant expertise in terms of if you do have something that is not there, our team can help you get that performance, first. the other thing is with these endpoints, we are way more elastic, as you said, than, anyone else, and you have true scaling to zero. you have true, burstiness, and in practice, that matters a lot more to people than just finding, the GPU and, running Modal code on something.

Vibhu [00:21:20]: Yeah. And I will say it’s not that straightforward to just. like what I said is easier said than done, right?

Akshat [00:21:26]: Yeah.

Vibhu [00:21:27]: It’s I think still for the average person, still hard to just gut check using different. There’s, there’s quite a bit of combinations you can make there. the trade-offs aren’t really known at face value.

Akshat [00:21:40]: Yeah. it’s it’s not just that. I think it’s it’s that running production-grade inference is a hard infer problem.

Vibhu [00:21:49]: Yeah

Akshat [00:21:49]: Even if you subtract out the autoscaling

Vibhu [00:21:50]: Yeah

Akshat [00:21:51]: Is controlling things like tail latency and, making sure every, request is delivered at least once and whatnot.

The Model and Agent Lifecycle

Vibhu [00:22:00]: There’s a lot of innovation that you can do here. I think, it’s very interesting that you’re starting to encroach on, like as you become a full cloud, you’re starting to encroach on other people’s turf.

Vibhu [00:22:09]: What will you not do?

Akshat [00:22:13]: Well, we wanna follow our users and, make sure they get like a platform that has everything that works well together. so right now we’re focused on the model lifecycle and the agent, lifecycle. so both like going from data prep to training to inference, and then also if I want to deploy a background agent, let’s say, sandbox, do persistent storage, a whole bunch of other stuff.

Vibhu [00:22:38]: We talked to Cole, who did, OpenInspect. Yeah.

Akshat [00:22:42]: Yeah.

Vibhu [00:22:42]: And RealInspect also is on Modal.

Akshat [00:22:44]: Yeah. So Ramp Inspect was a great example of a background agent that was really successful because they, were able to use some of the primitives like snapshotting and fast scaling to just have something that feels really reactive and works well.

Ramp Inspect and Background Agents

Vibhu [00:23:02]: Yeah. That’s the new CTO of, Ramp right there.

Akshat [00:23:05]: Yeah, Rahul.

Vibhu [00:23:08]: It was really fun. yeah, okay, I think, all very bullish. Like, one of my reflections was also I did not originally. So when I met you guys

The Inference Inflection: CPU, GPU, and Co-Location

Vibhu [00:23:19]: You weren’t that much in the GPU game, and now you’re all about, inference. And one of the points that I hinged on for Jensen’s keynote at GTC this year was, what we’re calling like the inference inflection, right? That let’s say in AI workloads or machine learning workloads, it used to be like, let’s call it eight to one GPU to CPU, and now it’s more like one to one, which is like a interesting. Like, - because of how much agents are blocked or call out to this, to CPU heavy stuff the actual, like, limiting factor, like, swings back and forth from GPU to CPU a lot more than it used to be all GPU and then occasional CPU.

Akshat [00:24:01]: Yeah.

Vibhu [00:24:02]: GPU, CPU. And now it’s like just constantly, and you just have to locate everything.

Seventeen Clouds and the Supercloud Strategy

Akshat [00:24:08]: Yeah. And that’s one of the things that, again, we see as, something appealing about Modal, which is we’ve built this capacity pool that spans, 17 cloud providers, so we’re, we’re very good at Running on various kinds of cloud capacity across the world

Swyx [00:24:24]: You don’t have your own data centers?

Akshat [00:24:25]: We don’t have our own data centers. We just run across a lot of neo clouds

Swyx [00:24:29]: Yeah. Are

Akshat [00:24:30]: Metal providers.

Swyx [00:24:30]: Yeah. Question mark.

Swyx [00:24:31]: Yeah. You’re, you’re running the math, and you’re like, “What’s the cutover point where you’re like.”

Akshat [00:24:36]: Yeah, it’s a good question. part of it is we see our differentiator in the software layer, and, being capital light and focusing on the software helps us move really fast. so far it’s worked out well because there are so many other people building data centers that we’re able to work effectively with them, and again, focus on what makes us, special.

Swyx [00:24:55]: Yeah.

Swyx [00:24:56]: 17 gets you into, like, the local providers sometimes. Like

Akshat [00:25:00]: The,

Swyx [00:25:01]: Which was the most interesting one?

Akshat [00:25:02]: There are a lot more neo clouds than you expect, and they all have various degrees of, various levels of reliability. And, that’s why it’s something we’ve invested a lot of time in, is building our own reliability layer on top. so if the GPU falls off the bus or something happens, we user workloads are not affected, and that lets us use a lot more capacity than,

Swyx [00:25:30]: Yeah

Akshat [00:25:30]: You as a user would be able to.

Swyx [00:25:32]: It’s a useful thing to have because like now everyone knows, like, what layer you are and, like, you optimize for being the super cloud of all clouds.

Akshat [00:25:41]: Yeah. That’s, that’s, that’s the idea. and so I guess when you mentioned colocation, that’s, that’s another interesting thing where, one thing we’ve seen is people come to us when they want, very specifically located, CPUs or GPUs, like they want

Swyx [00:25:57]: Oh, they pin it in like

Akshat [00:25:58]: Yeah

Swyx [00:25:58]: EU?

Akshat [00:25:59]: Exactly. Or EU, US.

Swyx [00:26:01]: Right. Data resiliency

Akshat [00:26:02]: Australia

Swyx [00:26:02]: Locality thing or performance or what?

Akshat [00:26:04]: It’s either data locality or latency, yeah.

Swyx [00:26:07]: Yeah.

Akshat [00:26:07]: Like, you want your. They’re running sandboxes and model. They want them to be right next to a

Swyx [00:26:10]: Yeah, it’s easy then

Akshat [00:26:11]: Yeah

Swyx [00:26:12]: To. That is important in all those things. and so, like, you’ve accidentally, I don’t know if it’s accident, but, like, you’ve built the perfect primitive for agents to express themselves. And then, like, it’s almost very funny how every extra development just involves more file system, just involves more CPU.

Akshat [00:26:30]: Yeah.

Swyx [00:26:31]: Just like the things that you already have. I don’t know much about, if there’s any, like, networking usages that are interesting, but you’ve also done some good work on networking.

Networking, Sidecars, Private IPv6, and Sandboxes

Akshat [00:26:40]: Yeah, that’s exactly right. Like, we’re just taking compute storage and networking and building stuff on that layer, for, again, the stuff people need.

Swyx [00:26:49]: Yeah

Akshat [00:26:50]: We see a few interesting networking things coming up. one is people want networked sandboxes. so we have

Swyx [00:26:57]: For like a Docker cluster type thing.

Akshat [00:26:59]: Yeah.

Swyx [00:26:59]: Sorry, Docker Swarm. Oh, f**k. What is it called?

Akshat [00:27:02]: Compose.

Swyx [00:27:03]: Compose type thing.

Akshat [00:27:04]: Yeah. So if you want Docker Compose, our sandboxes now support, this thing called sidecars. So you can. A sandbox is a pod of containers, and you can run multiple containers in, a sandbox. also useful because, going back to networking, people want a lot of control over, outbound networking from a sandbox.

Swyx [00:27:23]: Yeah.

Akshat [00:27:23]: Like, they might wanna run a middle proxy for, like, maybe logging stuff for RL or, controlling how egress can happen to a domain, injecting credentials. and yeah. So we’ve, we’ve had to build a lot of that stuff ourselves.

Swyx [00:27:38]: Yeah.

Akshat [00:27:39]: But then also sometimes people want, sandboxes spanning multiple nodes to talk to each other, which is an emerging thing we’re seeing. We have support for that for a different reason, and yeah, we’ll see if that becomes stable.

Swyx [00:27:52]: Like, just an open socket. It’s a. This is directly like mTLS.

Akshat [00:27:56]: We do support that, which is you can, expose a tunnel inside a sandbox.

Swyx [00:28:01]: Yeah.

Akshat [00:28:01]: And then you can either expose it to public internet or it can be, you can add like a HTTP, auth layer above it. But we have this thing called I6PN, which we haven’t talked about, which is this, like, overlay network using IPv6 addresses. so if Modal containers, within the same workspace, when this is enabled, can address each other using this private IPv6 address, and no one else can.

Akshat [00:28:28]: So it’s like private networking, for containers. We built it because we needed it as a primitive for our distributed training product. so we have this other feature, which is you can add a decorator to a function, and you get a cluster of GPUs. and they have RDMA networking. so you can run a distributed training job, that’s truly serverless. and we did the overlay network for that. But then we’ve seen that people are using it for other reasons, and, I’m intrigued to yeah, what would people do with it.

Swyx [00:28:59]: Build primitives and let people figure it out, right?

Akshat [00:29:01]: Yeah, exactly.

Swyx [00:29:02]: You put out a pretty interesting

Akshat [00:29:03]: They’re like, they read the docs webpage. Let me use that

Swyx [00:29:06]: Yeah

Akshat [00:29:06]: Something they never intended to work. This is literally not even in our docs page. People somehow found it, and they’re using it.

RDMA, Memory Movement, and Distributed Training

Swyx [00:29:12]: Huh.

Swyx [00:29:14]: The way you portrayed it with, like, RDMA versus TCP, like, very well laid out, but just the transfer speed change at scale for RL, like yeah, you have it, you have it built in. I’m sure someone found it. It’s found it to be a lot more efficient before you made a thing out of it, right?

Akshat [00:29:32]: Yeah. And not to split hairs, I guess the overlay network is the TCP overlay network.

Akshat [00:29:39]: The reason we have that is you need that to do the key exchange for RDMA before you set up the RDMA network on top of that. but then people found the TCP part.

Swyx [00:29:48]: Can I tell you, this is like a big aha moment for me because

Akshat [00:29:51]: Yeah

Swyx [00:29:51]: So I review 2,200 submissions for the World’s Fair.

Akshat [00:29:56]: Yeah.

Swyx [00:29:57]: And then I got this from John Osterhout

Akshat [00:29:58]: Huh

Swyx [00:29:59]: Who I don’t know if. Do John Osterhout by name?

Akshat [00:30:01]: The name sounds familiar.

Swyx [00:30:02]: He published a. He’s a well-known professor, published a lot of interesting software design books, and this is the talk he chose to submit, is on RDMA at Inference. And I’m like, you wouldn’t think that this guy, who is like operating systems guy, would care about RDMA.

Akshat [00:30:20]: I, it makes sense to me because I,

Swyx [00:30:24]: This is the cloud, right? Yeah

Akshat [00:30:25]: Like, the way you move around your KV cache and how efficiently you can do it, how efficiently you move, your weights from your training GPUs to your inference GPUs in RL is there’s a lot of degrees of freedom, and it is a systems problem

Swyx [00:30:41]: Yeah

Akshat [00:30:41]: Moving memory around

Swyx [00:30:42]: Yeah

Akshat [00:30:43]: Scheduling.

Swyx [00:30:44]: This shows you how primitive my understanding of networking stuff is.

Swyx [00:30:46]: Is this like the domain of WireGuard as well?

Akshat [00:30:50]: Not quite.

Swyx [00:30:51]: It’s adjacent?

Swyx [00:30:53]: Explain everything.

Akshat [00:30:54]: Sure.

Swyx [00:30:56]: How do we move memory around GPUs?

Akshat [00:30:58]: Well, so sorry. Yeah, that is memory. Sorry, I was talking more, and maybe I was talking like five minutes back, about the private IPv6, addressing that you’ve set up.

Swyx [00:31:09]: Yeah.

Akshat [00:31:09]: Is it like it’s a VPN?

Swyx [00:31:10]: Yeah, it is like a VPN, and yeah, WireGuard is, yeah, you’re right. It is,

Akshat [00:31:16]: Right. Yeah, you already moved on to new topics

Swyx [00:31:17]: A similar

Akshat [00:31:18]: Okay

Swyx [00:31:19]: In the same space, WireGuard is, encrypted and this is,

Akshat [00:31:23]: And you don’t need encryption.

Swyx [00:31:23]: Yeah.

Akshat [00:31:24]: Yeah.

Swyx [00:31:24]: This is not encrypted. that’s the main difference. This is TCP and we have eBPF programs that will reject or allow the TCP connection based on whether you’re allowed to do it.

Akshat [00:31:35]: Used to involve a full sidecar, but now you have eBPF in the Linux kernel.

Swyx [00:31:39]: Yeah.

Akshat [00:31:40]: Yeah. I don’t know if this is a natural follow-on to the topic of like my skepticism on distributed training is that while, like, people spend a lot of money on, like, cables to hook up GPUs, and even that is not, like, fast enough, and that’s the bottleneck, is your networking fast enough?

Swyx [00:31:59]: Yeah. So I guess you’re talking about fully distributed training like, Dialog or something which is like cross data center

Akshat [00:32:06]: That would be, yes.

Swyx [00:32:07]: That’s the extreme.

Akshat [00:32:08]: Yeah.

Swyx [00:32:08]: You’re in the middle, and then other people would have like the Mellanox cables up in, like, their actual data center.

Akshat [00:32:14]: When you run multi-node training on Modal, RDMA, I think Mellanox, is, or InfiniBand is like a, is all seen as RDMA. but it’s a way to bypass the TCP networking stack and, transfer, stuff much faster, between one node, to the other. And we have I think like 3 terabit per second, internal networking

Swyx [00:32:40]: Okay

Akshat [00:32:40]: Which is the standard that’s needed.

Swyx [00:32:42]: Okay. So I misunderstood what

Akshat [00:32:43]: 50

Swyx [00:32:43]: What part of the stack you were

Akshat [00:32:44]: 50 gigs over

Swyx [00:32:45]: Yeah

Akshat [00:32:45]: If you went

Swyx [00:32:45]: Yeah

Akshat [00:32:46]: RDMA.

Swyx [00:32:46]: Okay.

Swyx [00:32:48]: Yeah. I, very impressive work.

Multi-Node Training, Post-Training, and Auto Research

Swyx [00:32:52]: So effectively you’re extending like the model philosophy to the training cluster, like, yeah.

Akshat [00:32:59]: Yeah. And we’re, we’re not going for like large scale training runs. the thing that we’ve built multi-node training for is, we see a lot of, smaller scale post-training. like, people are post-training like medium sized fund models, so they can, get higher quality on inference. this is a perfect fit, for something like that.

Swyx [00:33:21]: Yeah. That is my impression of how a lot of these labs explore branches in post-training and then eventually merge whatever they find in.

Akshat [00:33:31]: Yeah. The other use case we’ve seen for multi-node training is even if you have a big cluster, your researchers are still doing small runs

Swyx [00:33:38]: Yes

Akshat [00:33:39]: Having elasticity there

Swyx [00:33:40]: Right, sure

Akshat [00:33:40]: Matters a lot more.

Swyx [00:33:41]: Yeah. the, like, this is like the current limiting factor for auto research, which is like you need to give your model some GPUs in order for it to completely run.

Akshat [00:33:51]: We have a blog post on auto resource and model is,

Swyx [00:33:55]: Yeah

Akshat [00:33:56]: Yeah, like, turns out to be pretty good substrate for that.

Swyx [00:33:59]: So my impression is auto research means many things, like

Akshat [00:34:01]: Yeah

Swyx [00:34:01]: Anything that Andrej coins. Right now it’s still science fair, right? Like not like, I don’t know how many people are doing this.

Akshat [00:34:08]: We’re having a golf.

Swyx [00:34:08]: Yeah.

Akshat [00:34:09]: I thought the same thing.

Swyx [00:34:11]: Yeah, you would know.

Akshat [00:34:12]: We, like, our internal both training and inference teams use this the general shape of this quite a bit. like we have this one internal repo called auto inference, which essentially we’ve automated our own forward-deployed engineering efforts using, this harness, which is, the agent will just spin up a sweep of different things. It’ll even run like, NVIDIA inside profiler and it’ll like tweak configs and it’ll arrive the right thing. it’ll change your GPUs both from H200 to B200, and works really well.

Swyx [00:34:47]: Nice.

Akshat [00:34:47]: So yeah.

Swyx [00:34:48]: By the way, I enjoy that your forward-deployed engineering is so technical that you have to do these things.

Swyx [00:34:52]: It’s very different from forward-deployed engineering from other people.

Akshat [00:34:54]: Yeah. For our forward-deployed engineering team is, essentially they’re like applied inference researchers or applied training researchers.

Swyx [00:35:02]: Someone told me like they have to be able to build, but they also have to be able to sell. do they have to sell or are they like they’re good, they’re just like post-sale type of thing?

Akshat [00:35:09]: It does, being able to talk to a customer and engage effectively with them

Swyx [00:35:13]: Yeah

Akshat [00:35:13]: Matters a lot.

Swyx [00:35:14]: They want the same thing.

Akshat [00:35:15]: Yeah.

Swyx [00:35:15]: ?

Akshat [00:35:15]: But it’s it’s not really a sales, thing. We pair them with-- We have solution architects as well that are more on the sales side.

Swyx [00:35:23]: Okay. Let’s spend a bit more time on auto research. This is a big focus for for this year. Where does this go? like, have people explored enough? Like, there’s all these beautiful charts of like improve and then level off a bit and then you find the next thing. Is this one abstraction up from normal training? Is that how we think about it, or do you think about it differently? Like model level training versus high, like driven hyperparameter search.

Auto Inference and Modal Bench

Akshat [00:35:51]: Yeah, like,

Swyx [00:35:51]: Someone, some people call it like neural architecture search or whatever, right? Like.

Akshat [00:35:54]: Yeah, - So the stuff I’ve seen people do with it is nowhere on the architecture level. It’s pretty much tweaking parameters, but it’s it’s a hyperparameter sweep that’s guided by some model intuition, so it’s like much more efficient than, whatever other, sweep you would have.

Swyx [00:36:12]: Yeah, it’s just, it’s just a question of where you want to spend your compute?

Akshat [00:36:16]: Right.

Swyx [00:36:16]: ‘Cause yeah, you can just throw infinite amounts of money on this and somehow you’ll bang out Shakespeare?

Akshat [00:36:22]: Yeah, infinite monkey.

Swyx [00:36:24]: Yeah, so like the very good for model. and I think it’s also very important that agents can spin up other agents, can spin up their infrastructure. Like very good for you. how good is our LLMs at generating model code? Like the benefit of existing LLMs is that you are in the data.

Akshat [00:36:42]: Yeah. They’re, they’re surprisingly good. I think like pre Cloud 4 they were not, and then now they’re able to shot, stuff out of the box. But we’re playing around with releasing like a Modal Bench for like the harder

Swyx [00:36:55]: Yeah

Akshat [00:36:55]: Things, that the LLMs cannot do yet and maybe

Swyx [00:36:59]: What’s an example of that?

Akshat [00:37:01]: I think the things that- Sometimes agents struggle with, without right guidance and a skill is, how to, use the rest of our observability. Like how to. Something is failing, like how do you look at the logs and then update the right thing? It’s reasoning about that. But they’re able to shot, like

Swyx [00:37:23]: Yeah. You can just add a skill to it?

Compute Strategy and Capacity Planning

Akshat [00:37:26]: Yeah. So we have a Modal skill now that. Which is why we built this Modal Bench. It’s to find things like that, so we can address them in our tool.

Swyx [00:37:35]: Tune a skill. Yeah.

Akshat [00:37:36]: Yeah.

Swyx [00:37:36]: No. it’s it’s good. are you facing any shortages? like we talk a lot about GPU shortages, but also CPU, also memory.

Swyx [00:37:44]: Yeah.

Akshat [00:37:45]: We have had a lot of growth, which means that, there’s - we’ve had to be much better about

Swyx [00:37:53]: Planning

Akshat [00:37:54]: Proactive capacity planning.

Swyx [00:37:55]: Yeah.

Akshat [00:37:55]: So we have,

Swyx [00:37:57]: Which by the way, like it’s like a MBA’s like dream

Akshat [00:38:00]: Yes

Swyx [00:38:00]: Is like just planning this stuff. I think last time you and I talked about something maybe about this.

Akshat [00:38:03]: Yeah. we have a really competent team of people that we call, The role is called compute strategy. so yeah, if anyone listening here or wants to work on that

Swyx [00:38:13]: Compute strategy?

Akshat [00:38:13]: Yeah.

Swyx [00:38:14]: I think,

Akshat [00:38:14]: I feel like,

Swyx [00:38:15]: I think the normies call it FP&A or something.

Akshat [00:38:18]: Well, it’s more It’s it’s not FP&A. It’s it’s There’s a lot of interesting financial questions of like what is the blend between one year and three-year reservations? how do we forecast our own capacity? how do we. especially since our capacity is very fungible across different GPU types and different regions, like you have to model a lot of it. and you also have to have an opinion on how the supply chain is gonna evolve, and then you have to like, take bets,

Swyx [00:38:49]: Yeah

Akshat [00:38:49]: Based on that.

Swyx [00:38:50]: Tokenomics.

Akshat [00:38:50]: Yeah.

Swyx [00:38:51]: This is like probably a not a real point, but, I was trying to think about like what other industries. I was trying to think about like, we cannot be first to like these kinds of problems.

Akshat [00:38:59]: Yeah.

Swyx [00:39:00]: And what other industries have had this? And I was like, airlines with fuel and like they have to hedge their fuel and like, I think for a long time Southwest because they made like a hero fuel bet, they like were like super low cost because

Akshat [00:39:12]: Oh

Swyx [00:39:12]: Compared to everyone else.

Akshat [00:39:14]: Yeah. I hadn’t thought about that.

Vibhu [00:39:16]: We’re at a fun time too?

Akshat [00:39:18]: Yeah. It’s. A lot of the compute business in general, for us is also about being very good about capacity management. That is how you have great unit, economics. but also ove