Mistral Large 3 675B Instruct 2512 NVFP4
Mistral Large 3 675B Instruct 2512 NVFP4 is an Apache-2.0 licensed, instruct post-trained multimodal granular Mixture-of-Experts model. It has 675B total parameters with 41B active parameters, comprising a 673B-parameter MoE language model with 39B active and a 2.5B vision encoder. It is an NVFP4 post-training-activation quantized checkpoint derived from Mistral-Large-3-675B-Instruct-2512 and created with llm-compressor in collaboration with vLLM and Red Hat. The model card reports support for vision, multilingual use, system prompts, agentic capabilities with native function calling and JSON output, and a 256k context window. It is described as deployable on-premises in NVFP4 on a single node of H100s or A100s, while the FP8 version is described as deployable on a single node of B200s or H200s.
At a glance
- Architecture
- unknown
- License
- apache-2.0
- Software
- vLLM
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Add my hardwareBest for
- Long document understanding
- Daily-driver AI assistants
- Agentic and tool-use applications
- Enterprise knowledge work
- General coding assistance
Tradeoffs
- Performance, compatibility, and numerical hardware requirements beyond the supplied source facts are not stated.
- The source does not state whether the model was tested, benchmarked, or validated in any deployment.
- The supplied source does not describe ethical, safety, bias, or misuse limitations.
- No supported inference frameworks, serving stacks, or deployment configurations are stated beyond vLLM as the library name.
- The source does not specify minimum GPU memory, quantization accuracy trade-offs, or out-of-scope use cases.
Ways to run it
vLLM · Linux
Repository-specific command in publisher documentation; confirm dependencies and hardware in the source.
vllm serve mistralai/Mistral-Large-3-675B-Instruct-2512-NVFP4 --max-model-len 262144 --tensor-parallel-size 8 --tokenizer_mode mistral --config_format mistral --load_format mistral --enable-auto-tool-choice --tool-call-parser mistralKeep exploring
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