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tly below Claude Opus 4.5), which is impressive for a relatively small open-weight model! Using the ollama version of Qwen3-Coder-Next locally, the model takes about 48.2 GB of storage space and 51 GB of RAM. Note that the architecture behind Qwen3-Coder-Next is exactly the same as Qwen3-Next 80B (in fact, the pre-trained Qwen3-Next 80B is used as a base model for further mid- and post-training). Figure 16 below shows the Qwen3-Next architecture next to a regular Qwen3 235B model for reference.

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sandbox, talk to the outside world through a control plane). We went with Pattern 2. The control plane holds all credentials and acts as a proxy for everything: LLM calls, file storage, billing. The sandbox receives three env variables and has no access to anything else. It runs as a Unikraft micro-VM in production and a Docker container in development and evals. Same image everywhere. The tradeoff is an extra network hop on every operation and three services to deploy instead of one. In practic

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Ramp uses Modal to power Ramp Inspect, an internal background coding agent that now writes over half of all merged pull requests at Ramp. Leveraging Modal Sandboxes, Ramp spins up full development environments in seconds, giving every builder at the company access to what is effectively unlimited parallel engineering capacity grounded by Ramp’s internal codebase, enterprise context, and developer tooling. The idea: A coding agent as fast as local, accessible to everyone Ramp's AI team saw an opp

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youts. This pattern could also result from repeated cycles of munmap followed by mmap , where memory is freed and reallocated but the total usage continues to grow, either due to fragmentation, leaks in custom allocators, or improper resizing logic. In our case, the changing addresses and growing size strongly suggested that memory was being reallocated but never properly released. This was our first concrete indication that the leak wasn’t in the heap but in anonymous memory regions being resiz

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em in the world—one that amplifies their productivity and minimizes the time from idea to product. Today, Replit Agent tackles more complex tasks than ever before. As a result, average session durations have increased and trajectories have grown longer, with the agent completing more work autonomously. But longer trajectories introduce a novel challenge: model-based failures can compound, and unexpected behaviors can surface. Every trajectory is unique, so static prompt-based rules often fail to

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anism powered by a lightning indexer, DeepSeek-V3.2-Exp achieves significant efficiency improvements in both training and inference, especially in long-context scenarios. As the paragraph above states, the main innovation here is the DeepSeek Sparse Attention (DSA) mechanism that they add to DeepSeek V3.1-Terminus before doing further training on that checkpoint. This DSA consists of (1) a lightning indexer and (2) a token-selector, and the goal is to selectively reduce the context to improve ef

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idden Unicode Tags programmatically could be helpful. Even reviewing code manually for malicious instructions carefully does not help to mitigate prompt injection attacks that leverage hidden Unicode Tags; Developers should consider alternative IDEs until these issues are addressed; Disable “Auto-Execute” (the default) and use manual approvals, and carefully enable only commands that you would entrust the AI via “Allow List Terminal Commands”; If your organization uses this IDE widely, it might

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ly show you the difference. The tweet you shared is from November 2022—right on the cusp of the modern AI boom. Back then, the “wow factor” was that an AI could write a coherent paragraph or a simple poem about a silly concept like a “candy-powered FTL drive escaping otters.” To demonstrate how far we’ve come, I’m not just going to write text about it. I have built you a fully interactive, playable Candy-Powered FTL Starship Simulator . In 2022, AI could describe the engine. In 2025, AI can code

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vLLM as the inference framework. We audited every single invocation of every kernel during the forward pass to make sure they are bitwise equivalent across the frameworks. We leveraged the forward pass kernels from vLLM’s recent batch invariance work and wrote simple backward passes for these ops. vLLM has many heavily optimized fused operations, such as the SiLU MLPs and RMSNorms (with added residuals). To maintain bitwise equivalence, we imported the exact operations for the forward passes. T

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000 training examples: Generate: Model produces initial clinical note from transcript; Evaluate: Lumina evaluators identify specific failures (e.g., “incorrect tooth notation in paragraph 3”); Refine: Model repairs its output based on structured feedback; Iterate: Repeat until all six evaluators pass; Train: Use these perfect outputs for supervised fine-tuning This process is fundamentally different from standard distillation. Instead of training on "what GPT-5 would say," we train on outputs th

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nto this category.) Since I talked and wrote about transformer-based LLMs so many times, I assume you are familiar with the broad idea and architecture. If you’d like a deeper coverage, I compared the architectures listed above (and shown in the figure below) in my The Big LLM Architecture Comparison article. (Side note: I could have grouped Qwen3-Next and Kimi Linear with the other transformer-state space model (SSM) hybrids in the overview figure. Personally, I see these other transformer-SSM

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(MCP) is an open standard for connecting AI agents to external systems. Connecting agents to tools and data traditionally requires a custom integration for each pairing, creating fragmentation and duplicated effort that makes it difficult to scale truly connected systems. MCP provides a universal protocol—developers implement MCP once in their agent and it unlocks an entire ecosystem of integrations. Since launching MCP in November 2024, adoption has been rapid: the community has built thousands

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ed: Process state, allocator instance, CUDA graphs, and compiled JIT kernels all intact; No recompilation needed: Kernels were compiled during initial warmup and remain cached; Average per switch: 2.6s (see benchmark data above) No Sleep Mode Reality: Weight reload from SSD (same as Level 2); Everything else rebuilt: Process restart + allocator init + graph re-capture; JIT kernels: Full compilation + explicit warm-up routine (kernel warmup() + dummy runs); Average per switch: 48s (see benchmark

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ipeline - from custom kernels (attention, GEMM, MoE) to high-level scheduling and overhead removal. This blog provides a detailed breakdown of these optimizations and how they leverage Blackwell’s architectural features into production performance gains. Overview of InferenceMax SemiAnalysis InferenceMax is a benchmark framework designed for automated, recurring tests on LLM serving performance, with results updated daily to reflect software performance changes. This approach narrows the gap bet

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e balls into 2 indistinguishable boxes? The model prompt, as shown above, provides the model with a list of the different answer choices and ends with an “Answer: “ text that encourages the model to generate the correct answer. While it is not strictly necessary, it can sometimes also be helpful to provide additional questions along with the correct answers as input, so that the model can observe how it is expected to solve the task. (For example, cases where 5 examples are provided are also kno

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zone between two common failure modes. At one extreme, we see engineers hardcoding complex, brittle logic in their prompts to elicit exact agentic behavior. This approach creates fragility and increases maintenance complexity over time. At the other extreme, engineers sometimes provide vague, high-level guidance that fails to give the LLM concrete signals for desired outputs or falsely assumes shared context. The optimal altitude strikes a balance: specific enough to guide behavior effectively,

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ls at the right time. We have found selecting between prefix- and suffix-based namespacing to have non-trivial effects on our tool-use evaluations. Effects vary by LLM and we encourage you to choose a naming scheme according to your own evaluations. Agents might call the wrong tools, call the right tools with the wrong parameters, call too few tools, or process tool responses incorrectly. By selectively implementing tools whose names reflect natural subdivisions of tasks, you simultaneously redu

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ecode workers read from it. This isolates long, bursty prefill from steady, latency-sensitive decode. How does this work in vLLM? For clarity, the example below relies on SharedStorageConnector, a debugging connector implementation used to illustrate the mechanics. Note: Connector is vLLM's abstraction for handling the exchange of KVs between instances. Connector interface is not yet stable, there are some near-term improvements planned which will involve changes, some potentially breaking. We l

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ses, you can use the CUDA COREDUMP FILE environment variable to specify a file path template for the coredump file. For example, you can store the coredump file in a persistent storage directory: CUDA COREDUMP FILE="/persistent dir/cuda coredump %h.%p.%t", where %h is the hostname, %p is the process ID, and %t is the timestamp of the coredump; By default, the coredump procedure saves the entire GPU context. For programs like large model inference that occupy almost all GPU memory, a full coredum

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2 paper (Table 9), which found that for a 9B parameter architecture, a wider setup is slightly better than a deeper setup. Across 4 benchmarks, the wider model achieved a 52.0 average score, and the deeper model achieved a 50.8 average score. 3.2 Few Large Versus Many Small Experts As shown in Figure 14 above, it's also noteworthy that gpt-oss has a surprisingly small number of experts (32 instead of 128), and only uses 4 instead of 8 active experts per token. However, each expert is much large

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