Today we're announcing that Modal has crossed $1B in annualized run rate, as well as raised a $750M Series D at a $15.75B valuation, led by @Accel.
Since our Series C, usage has tripled and we’ve grown more than 15x year-over-year.
We are deeply grateful to the developers and companies building on Modal. There's still so much more to build, and we can't think of a better time to be building software.
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Training large models with RL typically runs on a fixed homogeneous cluster of the latest accelerators for numerical stability and faster throughput. This throws away inference optimization work done for other vendors and generations, limits the capacity you can utilize, and is unnecessarily expensive.
So we trained a model on agentic coding tasks using fully asynchronous RL and mixed-precision, mixed-accelerator samplers. With off-policy correction, training stayed stable through convergence, on par with homogeneous samplers, at a lower cost.
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The Runtime keynote is live.
Updates to Inference, Training, and Sandboxes including:
11:20 — Endpoint Candidates
16:30 — Modal Clusters
21:48 — VM Sandboxes
22:40 — Sandbox Sidecars
And more
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Runtime is this week at The Midway in San Francisco.
Thanks to our partners for helping us put it together: @nvidia, @pydantic, @radixark, @sgl_project, @temporalio, and @inferact, @vllm_project 💚
Join us to learn from the engineers behind these tools.
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Excited to be speaking at Modal Runtime alongside an insanely stacked group of speakers!
Will be talking about how we're making agents on faster, cheaper, and more reliable using @modal!
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. @modal moves mountains to make models manifest!
Our man in pink @CompleteSkeptic will be speaking at Runtime, you won't want to miss it.
modal x neo next week @ runtime
100% @modal 👑 arming the jevolution with GPUs. Y'all have been the best this week.
If you’re wondering how Jev is scaling to meet insane demand they could not possibly have predicted, the answer appears to be @modal — one of only 4 subprocessors listed on
We've partnered with Anthropic to contribute up to $250K in Modal credits for researchers pushing protein design forward in the open 🧬
To show what these optimizations make possible, we’re partnering with Adaptyv Bio on a protein design competition. Together, we’ll be experimentally validating over 5,000 designs.
We're providing up to $1 million in Claude credits plus funding alongside Adaptyv for experimental validation. Modal is contributing up to $250,000 in compute and Twist Bioscience is providing DNA.
Learn more on Adaptyv’s Proteinbase:
And sign up for the competition here:
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come to to see something cool here
So much of our alpha is in the infra layer anyway. So we’d rather work with open source projects and contribute back. Thanks @sgl_project for building amazing stuff!
I connected devin to some @modal H100s and then left it alone to run its own experiment loop against the GPUs. it found a way to reduce peak memory by up to 46% and latency by up to 52% on a popular OS training-kernel repo.
since the triton kernel was already near peak HBM bandwidth, it focused on other things, removing a 2GB allocation that was happening on every chunk of the backward pass. then it ran the benchmarks to confirm the speedup 🤯
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Runtime speaker lineup is live!
We're bringing together experts covering AI infrastructure, applications of AI in science and robotics, the future of software engineering, and more.
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Frontier models are simply too expensive and slow for the majority of use cases, so we see models like SWE-2 becoming the daily driver for most.
Training trillion-parameter coding agents at scale isn't easy though: typically, each step launches thousands of rollouts, each with its own isolated environment.
Cognition uses Modal's sandbox infrastructure for the rollouts behind SWE-2. Congrats on the launch!
More on how we scale Sandboxes:
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Introducing SWE-2, our closest model yet to the frontier.
On leading evals, it scores on par with recent frontier models – at up to 70% lower cost.
We scaled RL to multiple trillions of parameters, with a refined recipe that pushes the Pareto curve on both capabilities & cost.
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Run your agent on OpenAI. Run compute on Modal with isolated, secure sandboxes.
Your sandbox, your call.
Bring your own sandbox or connect a sandbox provider.
Choose the environment that fits your workload:
• CPU, GPU, and memory options
• Fully managed environments or deployments within your VPC
• File and secret storage options
We offer first-class integrations with @blaxelAI, @CloudflareDev, @daytonaio, @digitalocean, @e2b, @modal, @OracleCloud, @RunloopAI, and @vercel.
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DeepSeek v4.1-Flash, a new multimodal model from @deepseek_ai, is now available on Modal.
v4.1-Flash uses DeepSeek's Causal Encoder-Decoder architecture, activating 16B parameters for input and 8B parameters for output to improve cost efficiency for input-heavy agentic workloads.
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Run Codex on Modal.
We're teaming up with @AnthropicAI and @modal to host a Computational Biology Hackathon. Bring a protein sequence, gene set, or microscopy dataset — leave with an agentic tool built on Modal x Claude Science.
Sept 22 | 9AM–7:30PM | Cambridge, MA
RSVP:
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