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Applied Compute

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The best AI is built, not bought. Our platform, Applied Compute Agent Cloud, is now in private beta. Book a demo below.

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You can now post-train a model inside your existing production harness with our platform, AC2. A production harness is a whole engineered system around the LLM, with its own context management, tools, sandboxing, and control flows. Porting that into a new training runtime can be expensive and could introduce train-test mismatch, where the policy is optimized against a simulated harness and then struggles in production. All you need to do is swap out the harness’ LLM response endpoint to one provided by AC2, and expose a lightweight protocol for AC2 to initiate and grade rollouts; the trainer handles the rest.

You can now post-train a model inside your existing production harness with our platform, AC2. A production harness is a whole engineered system around the LLM, with its own context management, tools, sandboxing, and control flows. Porting that into a new training runtime can be expensive and could introduce train-test mismatch, where the policy is optimized against a simulated harness and then struggles in production. All you need to do is swap out the harness’ LLM response endpoint to one provided by AC2, and expose a lightweight protocol for AC2 to initiate and grade rollouts; the trainer handles the rest.

182,296 次观看

A 35B open-weight model trained to search a precomputed index answers repo search questions at 100x lower cost than a frontier model. We partnered with turbopuffer to train Qwen3.6-35B-A3B to find code across ~9,000 repositories. It tops the needle-in-a-haystack task outright at 2-10x lower latency.

A 35B open-weight model trained to search a precomputed index answers repo search questions at 100x lower cost than a frontier model. We partnered with turbopuffer to train Qwen3.6-35B-A3B to find code across ~9,000 repositories. It tops the needle-in-a-haystack task outright at 2-10x lower latency.

48,409 次观看

At Applied Compute, we're not just training models, we're building a model factory. To help researchers scale, we built Ari, our in-house AI research agent. Ari diligently sifts through gigabytes of logs to find evidence of unhealthy behavior, remembers results the team has learned from past experiments, and produces styled research reports for the team to review.

At Applied Compute, we're not just training models, we're building a model factory. To help researchers scale, we built Ari, our in-house AI research agent. Ari diligently sifts through gigabytes of logs to find evidence of unhealthy behavior, remembers results the team has learned from past experiments, and produces styled research reports for the team to review.

42,116 次观看

As a supporter of the open weights ecosystem, we're proud to be a post-training partner for NVIDIA Nemotron. We post-train Nemotron models for customer use cases, de-risk mainline RL runs on our AC2 platform and training stack, and contribute aggregate workload statistics for inference benchmarking. This is how open models get better, and we're excited to keep working closely with NVIDIA AI.

As a supporter of the open weights ecosystem, we're proud to be a post-training partner for NVIDIA Nemotron. We post-train Nemotron models for customer use cases, de-risk mainline RL runs on our AC2 platform and training stack, and contribute aggregate workload statistics for inference benchmarking. This is how open models get better, and we're excited to keep working closely with NVIDIA AI.

25,683 次观看

We partnered with Harvey to post-train the state-of-the-art legal agent on their LAB benchmark. It surpasses Opus 4.8 Max and GPT-5.5 xhigh.

We partnered with Harvey to post-train the state-of-the-art legal agent on their LAB benchmark. It surpasses Opus 4.8 Max and GPT-5.5 xhigh.

27,802 次观看

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Kimi K3 full fine-tuning is live on AC2. Our memory optimizations reduced GPUs required per training replica by ~40%. At nearly 3T parameters, Kimi forced us to rethink how we manage memory, communication, rollouts, and checkpoints. The result is a much more efficient path to training frontier-scale open models.

Kimi K3 full fine-tuning is live on AC2. Our memory optimizations reduced GPUs required per training replica by ~40%. At nearly 3T parameters, Kimi forced us to rethink how we manage memory, communication, rollouts, and checkpoints. The result is a much more efficient path to training frontier-scale open models.

Applied Compute

235,450 次观看 • 1 个月前

Today, we're introducing AC2, the Applied Compute Agent Cloud, to enable every team to train, serve, and improve their own frontier models.

Today, we're introducing AC2, the Applied Compute Agent Cloud, to enable every team to train, serve, and improve their own frontier models.

Applied Compute

205,218 次观看 • 1 个月前

Your data is your edge, but only if your AI is built on it. Rent a generic model and so can your competitor. The companies with an edge are deploying custom models that they own and improve over time. Our co-founder Rhythm Garg recently stopped by to share how companies are owning their intelligence with Applied Compute.

Your data is your edge, but only if your AI is built on it. Rent a generic model and so can your competitor. The companies with an edge are deploying custom models that they own and improve over time. Our co-founder Rhythm Garg recently stopped by to share how companies are owning their intelligence with Applied Compute.

Applied Compute

119,306 次观看 • 4 个月前

$We partnered with Harvey to train a model for Review Table, achieving state-of-the-art accuracy at a fraction of the cost and latency of frontier alternatives. Review Table is one of Harvey’s highest inference volume products. Training an open source model for production traffic required building a representative eval and understanding how real lawyers use the product. Our teams discuss the research process.$

We partnered with Harvey to train a model for Review Table, achieving state-of-the-art accuracy at a fraction of the cost and latency of frontier alternatives. Review Table is one of Harvey’s highest inference volume products. Training an open source model for production traffic required building a representative eval and understanding how real lawyers use the product. Our teams discuss the research process.

Applied Compute

34,094 次观看 • 1 个月前

We used RL to train models that create curated context from long documents for downstream use by agents. The models sometimes learn to invent their own abbreviations and shorthand. Optimizing with RL for downstream use produces very different artifacts from ordinary summaries: shorter, denser, creatively concise. We call these neural cheat-sheets.

We used RL to train models that create curated context from long documents for downstream use by agents. The models sometimes learn to invent their own abbreviations and shorthand. Optimizing with RL for downstream use produces very different artifacts from ordinary summaries: shorter, denser, creatively concise. We call these neural cheat-sheets.

Applied Compute

37,265 次观看 • 3 个月前

"Everyone's talking about continual learning. That's entirely where this space is going to go." The Applied Compute platform is architected around that premise: build memory and intuition from fragmented data across your entire org, train reasoning models directly on top of it, and close the loop. A model is just one piece. An agent is where it runs, what tools it has, how permissions and auth are handled, how humans guide and instruct it, and the observability around it all. Every interaction should be treated as a training signal so the system can compound over time. Thanks for having us TBPN

"Everyone's talking about continual learning. That's entirely where this space is going to go." The Applied Compute platform is architected around that premise: build memory and intuition from fragmented data across your entire org, train reasoning models directly on top of it, and close the loop. A model is just one piece. An agent is where it runs, what tools it has, how permissions and auth are handled, how humans guide and instruct it, and the observability around it all. Every interaction should be treated as a training signal so the system can compound over time. Thanks for having us TBPN

Applied Compute

64,309 次观看 • 7 个月前

“50% of DoorDash’s agentic restaurant orders are going to places users have never ordered from before.” Andy Fang tells our CEO Yash Patil what happens when agents become the discovery layer. If models increasingly decide what gets surfaced and bought, companies have a strong reason to train and own that intelligence.

“50% of DoorDash’s agentic restaurant orders are going to places users have never ordered from before.” Andy Fang tells our CEO Yash Patil what happens when agents become the discovery layer. If models increasingly decide what gets surfaced and bought, companies have a strong reason to train and own that intelligence.

Applied Compute

11,733 次观看 • 1 个月前

“Until you actually see things operationally, it’s going to be hard to build DoorDash from scratch.” Our CEO Yash Patil sat down with Andy Fang on why cheaper software doesn’t erase years of operating advantage. DoorDash’s moat is its proprietary data, edge cases, and hard-won knowledge, and increasingly, the models trained on top of it.

“Until you actually see things operationally, it’s going to be hard to build DoorDash from scratch.” Our CEO Yash Patil sat down with Andy Fang on why cheaper software doesn’t erase years of operating advantage. DoorDash’s moat is its proprietary data, edge cases, and hard-won knowledge, and increasingly, the models trained on top of it.

Applied Compute

12,263 次观看 • 1 个月前

The most useful data comes from real user interactions, feedback, and traces. With self-distillation, your model can learn from production traces even when the original environment can’t be replayed.

The most useful data comes from real user interactions, feedback, and traces. With self-distillation, your model can learn from production traces even when the original environment can’t be replayed.

Applied Compute

12,952 次观看 • 1 个月前

"Our approach is to build products and conduct research that are in service of accelerated AI deployments. Our platform team builds tools and context primitives that enable faster deployment. Our research team builds frontier systems, including a state-of-the-art RL stack. We then take that research and product and forward-deploy with our customers to help deliver real value." Thanks Founders You Should Know for having us. Open roles at:

"Our approach is to build products and conduct research that are in service of accelerated AI deployments. Our platform team builds tools and context primitives that enable faster deployment. Our research team builds frontier systems, including a state-of-the-art RL stack. We then take that research and product and forward-deploy with our customers to help deliver real value." Thanks Founders You Should Know for having us. Open roles at:

Applied Compute

45,197 次观看 • 6 个月前

RL is a powerful mechanism for training company-specific models on their unique work and data. This is what we do at Applied Compute. A key challenge is how to make RL efficient, because we need runs to be fast (delivered in days), cheap (scalable unit economics), and predictable (not just fast, but reliably fast). Here are some takeaways: • Synchronous RL is wasteful with time and compute. • Asynchronous RL is more efficient but introduces staleness, which causes learning instabilities. • Modeling and simulations can help analytically solve for what configuration leads to optimal efficiency. This allows us to rapidly prototype training configurations, without burning expensive compute cycles on trial runs. Two of our co-founders, Rhythm Garg and Linden Li, discussed some of this research at AI Engineer recently, with a focus on the following subproblem: what is the highest throughput way to do RL given a maximum staleness and compute budget?

RL is a powerful mechanism for training company-specific models on their unique work and data. This is what we do at Applied Compute. A key challenge is how to make RL efficient, because we need runs to be fast (delivered in days), cheap (scalable unit economics), and predictable (not just fast, but reliably fast). Here are some takeaways: • Synchronous RL is wasteful with time and compute. • Asynchronous RL is more efficient but introduces staleness, which causes learning instabilities. • Modeling and simulations can help analytically solve for what configuration leads to optimal efficiency. This allows us to rapidly prototype training configurations, without burning expensive compute cycles on trial runs. Two of our co-founders, Rhythm Garg and Linden Li, discussed some of this research at AI Engineer recently, with a focus on the following subproblem: what is the highest throughput way to do RL given a maximum staleness and compute budget?

Applied Compute

45,652 次观看 • 10 个月前

"Every company is going to build their own frontier AI unique to their secret sauce. That's exactly what the top law firms do. We're starting to talk with a law firm and their client and ask: how could we build you a joint model?" Thanks Gabe Pereyra and Harvey for joining us at our Private Frontier all-hands to share how Harvey is defining the future of Specific Intelligence for legal agents.

"Every company is going to build their own frontier AI unique to their secret sauce. That's exactly what the top law firms do. We're starting to talk with a law firm and their client and ask: how could we build you a joint model?" Thanks Gabe Pereyra and Harvey for joining us at our Private Frontier all-hands to share how Harvey is defining the future of Specific Intelligence for legal agents.

Applied Compute

19,710 次观看 • 4 个月前

Enterprise AI deployments today are frozen in time. Model capabilities stagnate in production. The problem compounds because companies aren’t static either. Every time your company improves, the model falls further behind. The bottleneck is continual learning. How does a model do something once and improve from feedback? The future of enterprise AI is Specific Intelligence: custom models teams own, trained on a company’s choices, interaction by interaction, using internal knowledge general models cannot access. Applied Compute helps companies train, serve, own, and improve custom models. Thanks Apoorv Agrawal for having Yash Patil at MS&E 435 to talk about the future of model training.

Enterprise AI deployments today are frozen in time. Model capabilities stagnate in production. The problem compounds because companies aren’t static either. Every time your company improves, the model falls further behind. The bottleneck is continual learning. How does a model do something once and improve from feedback? The future of enterprise AI is Specific Intelligence: custom models teams own, trained on a company’s choices, interaction by interaction, using internal knowledge general models cannot access. Applied Compute helps companies train, serve, own, and improve custom models. Thanks Apoorv Agrawal for having Yash Patil at MS&E 435 to talk about the future of model training.

Applied Compute

12,871 次观看 • 4 个月前

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