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sive inference on GPU. Because it answers directly instead of reasoning, it returns the first token quickly, even on image-heavy inputs. We measure time to first token on a single NVIDIA H100 SXM5 GPU, one request at a time, across three input types: a single 512x512 image, a 512x512 image with a 1,024-token input text, and 5 256x256 5-frame video clip. LFM2.5-VL-3B keeps latency consistently low and pulls far ahead on multi-frame inputs, returning the first token in about 34 ms on a 5-frame vid

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model and the speculative decoding drafter. You can use AutoModelForMultimodalLM and AutoProcessor classes to load the model and the processor. The same snippet runs unchanged on NVIDIA (CUDA), AMD (ROCm) and Intel (XPU) GPUs, device map="auto" places the model on whichever accelerator is available. Text-only Inference After loading the model, you can do text-only inference with it as follows. Prompting the model with images and text We would need torchvision to be able to use images and text.

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ns/s on a phone, so a capable agent runs instantly and privately on your own device. GPU inference. We also measure output throughput (total output tokens / wall time) on a single NVIDIA H100 SXM5 GPU using a sustained-load setting: at each concurrency level, we continuously maintain the target number of in-flight requests, replacing each completed request immediately. We benchmark each model with SGLang 0.5.16, 1,024 input tokens, up to 256 output tokens, in BF16, averaging 3 runs per concurren

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ickly enough to control a physical machine. BFL says the FLUX-mimic backbone can be optimized to run from input to internal representation in less than 80 milliseconds on a single NVIDIA RTX 5090. Further optimization across the action decoder, the inter-process path between sensors, model, and actuators, and real-time chunking that overlaps prediction with execution brings the full system’s reaction time to 101 milliseconds. Audi Production Lab has been testing and deploying the system on real

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e nor knock. Look at the teaser, and at the use cases it shows off, and it’s built for global, high-end developers and users more than for domestic users. It’s trained entirely on Nvidia, and unlike Zhipu and DeepSeek, K3 isn’t optimized for domestic chips from day one. It doesn’t carry the national-champion pressure that DeepSeek or Zhipu shoulder, the obligation to prop up the domestic ecosystem. If Moonshot were founded in Silicon Valley or Singapore instead of Beijing, you’d won’t find anyth

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-to-any pipeline. You can use either the 16 bit "thinkingmachines/Inkling" on Hopper or later GPUs, or the quantized NVFP4 checkpoint "thinkingmachines/Inkling-NVFP4" on Blackwell Nvidia GPUs. Make sure to have the latest version of transformers (5.14.0 was released today) ( pip install -U transformers ). After initializing the pipeline, you can pass in the prompt as follows. Going one level lower, you can use Auto classes. For inference, you can use the AutoModelForMultimodalLM class for models

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n the inference side are cheap by construction. Engines are self-contained and hold no state beyond the current weights, so a dead engine costs only its in-flight sessions. We use NVIDIA Dynamo to manage the engine lifecycles and route inference: each agent sandbox has its own proxy that records tokens in and out, so if a replica goes down, we don’t lose the full trajectory, and Dynamo reroutes it to a different worker. When Dynamo reschedules the replica on healthy nodes, our weight controller

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while remaining competitive with larger models. As an early look at ongoing work, we deployed LFM2.5-230M on a Unitree G1 humanoid robot, running entirely on-device on its onboard NVIDIA Jetson Orin. Here the model acts as a skill-selection layer: it takes a single natural-language instruction and decomposes it into a sequence of tool calls that invoke pre-trained low-level skills provided by NVIDIA's SONIC framework. After a quick fine-tune for this task, the model turns a free-form command suc

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and unconstrained natural-language instructions without any environment-specific fine-tuning. Benchmarking Qwen-RobotNav Deployment Deployed zero-shot on a Unitree Go2 quadruped (NVIDIA Jetson Thor, 196ms latency) using only the built-in low-resolution camera. The robot executes step-by-step verbal instructions across multiple rooms in a previously unseen apartment. Instruction Following We evaluate a back-and-forth navigation task in an unseen exhibition hall: the robot first navigates 21.78 m

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optimize. Engineers must simultaneously handle multiple tightly coupled issues, including data layout, compute pipeline scheduling, and adaptation to hardware characteristics. On NVIDIA Hopper architecture GPUs, hand-writing a production-grade FP8 GEMM kernel typically requires one to two weeks of focused effort from an experienced team. We used this task to evaluate M3’s long-horizon autonomous iteration capability. We asked MiniMax M3 to optimize this kernel on NVIDIA Hopper architecture GPUs

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nference. We support inference via vLLM and SGLang via active contributions to these codebases. We measure output throughput (total output tokens divided by wall time) on a single NVIDIA H100 SXM5 GPU using a sustained-load setting: at each concurrency level, we continuously maintain the target number of in-flight requests, replacing each completed request immediately. We benchmark each model with SGLang 0.5.12, 1,024 input tokens, up to 256 output tokens, in BF16, averaging 3 runs per concurren

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at $1.5 per million input tokens and $7.5 per million output tokens. Open weights are on Hugging Face under a modified MIT license. It is also available for prototyping, hosted on NVIDIA GPU-accelerated endpoints on build.nvidia.com and as a scalable containerized inference microservice, NVIDIA NIM. Build the future of agentic systems with us. We're hiring across research, engineering, and product to push agentic systems further. See our open roles. Mistral Medium 3.5 Modified MIT Merged dense m

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ntarily ended the session. In addition to achieving strong kernel generation results on PPUs, Qwen3.7-Max also generates high-quality, production-grade kernels across a variety of NVIDIA GPUs. For example, on KernelBench L3, Qwen3.7-Max is able to produce accelerated kernels for 96% of the scenarios, compared to 98% for Opus-4.6, 78% for GLM 5.1, 80% for Kimi K2.6, 54% for DeepSeek V4 Pro, and 48% for Qwen3.6-Plus. This result highlights two properties of Qwen3.7-Max as a foundation model poweri

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models ahead — stay tuned. Download Model Card As tested on April 13, 2026. Compared to MAI-Image-2 when normalized by latency and GPU usage. Throughput per GPU vs MAI-Image-2 on NVIDIA H100 at 1024×1024; measured with optimized batch sizes and matched latency targets. Results vary with batch size, concurrency, and latency constraints; As tested on April 13, 2026. Compared to Gemini 3.1 Flash (high reasoning), Gemini 3.1 Flash Image and Gemini 3 Pro Image: Measured at p50 latency via AI Studio

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both text and image inputs, making it versatile for a wide range of applications. With Mistral Small 4, we reaffirm our commitment to open-source models and are proud to join the NVIDIA Nemotron Coalition as a founding member, advancing collaboration and innovation in AI development. Key architectural details Mixture of Experts (MoE): 128 experts, with 4 active per token, enabling efficient scaling and specialization; 119B total parameters, with 6B active parameters per token (8B including embe

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. Acknowledgements These models were trained using compute provided through the IndiaAI Mission, under the Ministry of Electronics and Information Technology, Government of India. Nvidia collaborated closely on the project, contributing libraries used across pre-training, alignment, and serving. We're also grateful to the developers who used earlier Sarvam models and took the time to share feedback. We're open-sourcing these models as part of our ongoing work to build foundational AI infrastruct

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ross the most popular inference frameworks, including llama.cpp, MLX, vLLM, and ONNX Runtime. All frameworks support both CPU and GPU acceleration across Apple, AMD, Qualcomm, and Nvidia hardware. You can find the entire family of LFM2.5 models here: To ensure the LFM2.5 family runs efficiently wherever you need it, we are rapidly expanding our hardware and software ecosystem by welcoming Qualcomm Technologies, Inc., Ollama, FastFlowLM, and Cactus Compute as new launch partners. “Qualcomm Techno

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oints enable deployment across diverse accelerators and runtimes, from cloud to edge devices. All frameworks support both CPU and GPU acceleration across Apple, AMD, Qualcomm, and Nvidia hardware. Launch Partnerships We are also partnering with AMD and Nexa AI to bring the LFM2.5 family to NPUs. These optimized models are available through our partners, enabling highly efficient on-device inference. These capabilities unlock new deployment scenarios across various devices, including vehicles, mo

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so you can use it directly inside your IDE. Devstral 2 is optimized for data center GPUs and requires a minimum of 4 H100-class GPUs for deployment. You can try it today on build.nvidia.com . Devstral Small 2 is built for single-GPU operation and runs across a broad range of NVIDIA systems, including DGX Spark and GeForce RTX. NVIDIA NIM support will be available soon. Devstral Small runs on consumer-grade GPUs as well as CPU-only configurations with no dedicated GPU required. For optimal perfo

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ins the ranks of frontier instruction-fine-tuned open-source models. Mistral Large 3 is one of the best permissive open weight models in the world, trained from scratch on 3000 of NVIDIA’s H200 GPUs. Mistral Large 3 is Mistral’s first mixture-of-experts model since the seminal Mixtral series, and represents a substantial step forward in pretraining at Mistral. After post-training, the model achieves parity with the best instruction-tuned open-weight models on the market on general prompts, while

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