Modal

@modal

AI infrastructure that developers love 💚 Run inference, sandboxes, batch processing, training, and many other things on Modal

168 Following40K Followers1.8K Posts
  1. Modal@modal
    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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    Erik Bernhardsson @bernhardsson
    861061.2K327K
  2. Modal@modal
    Modal
    RT @GrayAreaorg: Last night’s opening of Scaling Intelligence! ✨💚 @modal We're open today from 2 – 6 PM, followed by Inference as Instrument at 7 PM, a conversation exploring AI as a creative instrument. Explore the exhibition through Sunday, Nov 1.
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  3. Modal@modal
    Modal
    RT @teenychairs: scaling intelligence at @GrayAreaorg sf opens tonight! on view till sun! come see what artists have built with @modal 🌱
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  4. Modal@modal
    Modal
    RT @charles_irl: @modal partnered with @GrayAreaorg to sponsor a group of artists exploring the possibilities and implications of abundant, malleable intelligence or: "here's how you scale taste"
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  5. Modal@modal
    Nova got Opus 4.8 to edge past Fable 5 on Terminal-Bench 2.1. Modal Sandboxes let the team decouple Nova’s agent loop and automatic review from tool execution, with fast startup and changes deployed in seconds to keep iteration quick. The result: 83.9%, up five percentage points from Opus 4.8’s Claude Code baseline and just ahead of Fable 5’s 83.8%.
    Carl Hasselskog @CarlHasselskog
    We made Opus 4.8 perform like Fable 5. Same model. No fine-tuning. Terminal-Bench 2.1: Opus 4.8 in Claude Code: 78.9% Opus 4.8 in Nova Agent OS: 83.9% Fable 5: 83.8% Built together with our friends at @modal. The frontier isn't only the model anymore.
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  6. Carl Hasselskog✓@CarlHasselskog
    We made Opus 4.8 perform like Fable 5. Same model. No fine-tuning. Terminal-Bench 2.1: Opus 4.8 in Claude Code: 78.9% Opus 4.8 in Nova Agent OS: 83.9% Fable 5: 83.8% Built together with our friends at @modal. The frontier isn't only the model anymore.
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  7. Modal@modal
    Posts @modal
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  8. Modal@modal
    Modal
    RT @DevenNavani: the future of inference feels increasingly bespoke: continuously optimized for each model*hardware*workload. agents are what make this level of specialization economically viable really impressive work from @DecagonAI! and cool to see @modal powering the experimentation here
    Cyrus @cyrusasg
    every inference endpoint presents a different optimization problem, shaped by its model architecture, traffic patterns, hardware, and latency constraints. we built an autoresearch loop to tackle these problems and carry lessons from one investigation into the next, with the broader goal of building systems that get better at research as they accumulate evidence.
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  9. Cyrus✓@cyrusasg
    every inference endpoint presents a different optimization problem, shaped by its model architecture, traffic patterns, hardware, and latency constraints. we built an autoresearch loop to tackle these problems and carry lessons from one investigation into the next, with the broader goal of building systems that get better at research as they accumulate evidence.
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  10. Modal@modal
    Modal
    RT @bernhardsson: Great to be back on Unsupervised Learning with @jacobeffron and @patrickachase! We talked about the day-to-day knife fighting of getting GPUs, why we contribute a lot to open source, and how our most experienced engineers are the biggest agent-maxxers.
    Jacob Effron @jacobeffron
    Is the compute market still the Wild West? What will happen to GPU prices over the next few years? And how will the inference market evolve? @bernhardsson, co-founder and CEO of @Redpoint portfolio company @modal, was the perfect person to discuss this and more. Modal has crossed a $1B revenue run rate and recently raised $750M at a $15.75B valuation. A few takeaways from my conversation with Erik, alongside @patrickachase: ▪️ Why compute won’t become a commodity GPU capacity varies in disk, networking, location, and especially reliability. That variation makes a true exchange hard to build, so much of the market still runs on late-night WhatsApp groups and hallway deals at events like GTC. Erik expects it to get somewhat more efficient over time, but to stay structurally messy. ▪️ NeoClouds, NeoLabs, and the cost of capital Erik disagrees with the idea that NeoClouds are going to die, and he sees them as sound businesses built on leasing hard assets against locked-in contracts. Their real disadvantage is cost of capital, which is why the successful hyperscalers all sit inside cash-rich public companies. NeoLabs carry a different risk. GPU deals often require only about 30% upfront, so a lab that raises $1B can commit to far more compute than it can pay for today. If funding tightens, Erik expects some NeoLabs to resell capacity and start to look more like NeoClouds. ▪️ Where durable advantage lives in inference Erik sees pure token serving as a race to the bottom, with routers already commoditizing the underlying providers. Better kernels and smarter KV caching still matter, but those techniques spread quickly. He believes the more durable edge comes from offering a cluster of adjacent building blocks. ▪️ Who is actually adopting coding agents Erik expected junior engineers to adopt agents first, but he has seen the opposite. For senior engineers, agents are like having 10 interns. Give one a full POSIX test suite and it can write a new file system in three days. He also sees training data creating a new kind of incumbency in software, with developers defaulting to frameworks like React because agents already know them well. ▪️ Why GPU prices may go up Erik changed his mind on this 48 hours before we recorded. After reading Anthropic’s S1, he concluded that the largest buyers of compute are financially strong enough to keep acquiring capacity, which points to higher prices over the next 12 months. Full Unsupervised Learning Episode 👇 0:00 Intro 1:26 The Compute Market Today 5:16 Modal’s Path to LLM Inference 10:02 Building the Hard Infrastructure 12:32 Unsolved Problems in Inference 16:07 Chips & Model Sizes 21:21 What Wins in Inference 24:56 Prioritizing a Multi-Product Roadmap 30:20 Agents as Modal’s Users 33:19 The Next Big Bet After Sandboxes 38:58 Robotics & Multimodal Inference 41:04 Scaling From $2M to $1B 42:51 NeoClouds & NeoLabs 48:15 RL as a Service 51:40 Quickfire YouTube: Spotify: Apple:
    040173
  11. Modal@modal
    Modal
    RT @jacobeffron: Is the compute market still the Wild West? What will happen to GPU prices over the next few years? And how will the inference market evolve? @bernhardsson, co-founder and CEO of @Redpoint portfolio company @modal, was the perfect person to discuss this and more. Modal has crossed a $1B revenue run rate and recently raised $750M at a $15.75B valuation. A few takeaways from my conversation with Erik, alongside @patrickachase: ▪️ Why compute won’t become a commodity GPU capacity varies in disk, networking, location, and especially reliability. That variation makes a true exchange hard to build, so much of the market still runs on late-night WhatsApp groups and hallway deals at events like GTC. Erik expects it to get somewhat more efficient over time, but to stay structurally messy. ▪️ NeoClouds, NeoLabs, and the cost of capital Erik disagrees with the idea that NeoClouds are going to die, and he sees them as sound businesses built on leasing hard assets against locked-in contracts. Their real disadvantage is cost of capital, which is why the successful hyperscalers all sit inside cash-rich public companies. NeoLabs carry a different risk. GPU deals often require only about 30% upfront, so a lab that raises $1B can commit to far more compute than it can pay for today. If funding tightens, Erik expects some NeoLabs to resell capacity and start to look more like NeoClouds. ▪️ Where durable advantage lives in inference Erik sees pure token serving as a race to the bottom, with routers already commoditizing the underlying providers. Better kernels and smarter KV caching still matter, but those techniques spread quickly. He believes the more durable edge comes from offering a cluster of adjacent building blocks. ▪️ Who is actually adopting coding agents Erik expected junior engineers to adopt agents first, but he has seen the opposite. For senior engineers, agents are like having 10 interns. Give one a full POSIX test suite and it can write a new file system in three days. He also sees training data creating a new kind of incumbency in software, with developers defaulting to frameworks like React because agents already know them well. ▪️ Why GPU prices may go up Erik changed his mind on this 48 hours before we recorded. After reading Anthropic’s S1, he concluded that the largest buyers of compute are financially strong enough to keep acquiring capacity, which points to higher prices over the next 12 months. Full Unsupervised Learning Episode 👇 0:00 Intro 1:26 The Compute Market Today 5:16 Modal’s Path to LLM Inference 10:02 Building the Hard Infrastructure 12:32 Unsolved Problems in Inference 16:07 Chips & Model Sizes 21:21 What Wins in Inference 24:56 Prioritizing a Multi-Product Roadmap 30:20 Agents as Modal’s Users 33:19 The Next Big Bet After Sandboxes 38:58 Robotics & Multimodal Inference 41:04 Scaling From $2M to $1B 42:51 NeoClouds & NeoLabs 48:15 RL as a Service 51:40 Quickfire YouTube: Spotify: Apple:
    040185
  12. Modal@modal
    Modal
    RT @NYSE: Following a $750M Series D, @modal CEO @bernhardsson explains how each shift in software changes both the AI applications that can be created, and the infrastructure needed to build them.
    050196
  13. Modal@modal
    Students shouldn’t have to scale down their research ideas because they can’t get access to compute. The @generalcatalyst Research Fellowship will support 20 undergraduates with up to $25K in compute from Modal to power their most ambitious ideas. We're excited to see what you build!
    Daisy Zhao @Daisy4ai
    Introducing @generalcatalyst Research Fellows - a program for undergraduates ready to pursue their most ambitious ideas. We’re supporting approximately 20 exceptional undergraduate builders from leading schools. In partnership with @Modal, each fellow will receive up to $25K in compute for a 90-day project of their own. For the bold, the curious, and those building what they wish existed: applications are open now. Tell us what you want to build!
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  14. Daisy Zhao✓@Daisy4ai
    Introducing @generalcatalyst Research Fellows - a program for undergraduates ready to pursue their most ambitious ideas. We’re supporting approximately 20 exceptional undergraduate builders from leading schools. In partnership with @Modal, each fellow will receive up to $25K in compute for a 90-day project of their own. For the bold, the curious, and those building what they wish existed: applications are open now. Tell us what you want to build!
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    91320229K
  15. Modal@modal
    Posts @modal
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  16. Modal@modal
    We recently announced that Modal has crossed $1B in annualized run rate, as well as raised a $750M Series D at a $15.75B valuation. Our CEO and co-founder @bernhardsson stopped by @NYSE to share more about what's being built on Modal today and what's next for the company post-fundraise...
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    12513611.3K
  17. Modal@modal
    Posts @modal
    Check out the full interview:
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  18. Modal@modal
    DeepSeek v4.1 Flash 1017 from @deepseek_ai is now available on Modal. An update of DeepSeek v4.1 Flash, it improves substantially on reasoning, coding, agentic tasks, and visual understanding.
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    2581137.1K
  19. Modal@modal
    Posts @modal
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  20. Modal@modal
    .@markovrobotics is teaching robots to tackle complex manipulation tasks with fewer than ten hours of robot data. Their bet: video already contains most of the physical reality a robot needs to act. They use Modal for video processing, world model training, and remote inference.
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    58434.7K
  21. Modal@modal
    Posts @modal
    Using Modal Functions, Markov fans out preprocessing across thousands of CPU and GPU containers, reaching upwards of 500 GPUs at once on the heaviest stages. Work that could take a full day on traditional setups completes in 20-30 minutes on Modal, thanks to the ability to parallelize work over on-demand compute. Read the full post:
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  22. Modal@modal
    .@WeAreLegora runs 2,000 agent evals in parallel on Modal Sandboxes, testing a legal agent that works for hours across thousands of documents. Every run boots the entire application, pinned to a specific commit, so engineers can measure the impact of their changes in a realistic environment.
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    2881098.8K
  23. Modal@modal
    Posts @modal
    Legora chose Modal for scalability, low startup latency, and the flexibility to run its large, complex app. With Modal providing reproducible environments at scale, Legora’s agents can now create cases, launch experiments, and analyze results autonomously. With engineers managing the process, Legora used this approach to cut agent cost by 30% through smarter compaction and custom tool search. Read the full post:
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  24. Modal@modal
    Posts @modal
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