Mistral Large 3 675B Instruct 2512 — Hardware Requirements & GPU Compatibility
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Mistral-Large-3-675B-Instruct-2512 is Mistral AI's flagship instruction-tuned model, a multimodal granular Mixture-of-Experts system pairing a roughly 673-billion-parameter, about 39-billion-active-parameter language backbone with a 2.5-billion-parameter vision encoder, for around 675 billion total and 41 billion active parameters overall. It handles vision alongside text, supports dozens of languages, and is built for agentic use with native function calling and JSON output, aimed at long-document understanding, coding, and enterprise knowledge work rather than dedicated step-by-step reasoning. This is a frontier-scale model that requires a full multi-GPU server node to run, even quantized. Context length is 262,144 tokens. It is released under the Apache 2.0 license, permitting unrestricted commercial and research use. It was published in November 2025, as the third generation of Mistral's Large model line, alongside FP8, NVFP4, and BF16 weight releases.
2.1K downloads
251 likesSpecifications
- Publisher
- Mistral AI
- Family
- Mistral
- Parameters
- 675B
- Release Date
- 2025-11-28
- License
- Apache 2.0
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HuggingFace
How Much VRAM Does Mistral Large 3 675B Instruct 2512 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 315.6 GB | — | 286.88 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 324.8 GB | — | 295.31 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 362.0 GB | — | 329.06 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 371.3 GB | — | 337.50 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 445.5 GB | — | 405.00 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 529.0 GB | — | 480.94 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 612.6 GB | — | 556.88 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 742.5 GB | — | 675.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Mistral Large 3 675B Instruct 2512?
Q4_K_M · 445.5 GBMistral Large 3 675B Instruct 2512 (Q4_K_M) requires 445.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 580+ GB is recommended. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Mistral Large 3 675B Instruct 2512?
Q4_K_M · 445.5 GB2 devices with unified memory can run Mistral Large 3 675B Instruct 2512, including NVIDIA DGX H100.
Decent
— Enough memory, may be tightWhere to Download Mistral Large 3 675B Instruct 2512
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Benchmarks
Benchmark details →Related Models
Frequently Asked Questions
- How much VRAM does Mistral Large 3 675B Instruct 2512 need?
Mistral Large 3 675B Instruct 2512 requires 445.5 GB of VRAM at Q4_K_M, or 1485 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 675B × 4.8 bits ÷ 8 = 405 GB
KV Cache + Overhead ≈ 40.5 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M445.5 GB- Can NVIDIA GeForce RTX 5090 run Mistral Large 3 675B Instruct 2512?
No — Mistral Large 3 675B Instruct 2512 requires at least 204.2 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for Mistral Large 3 675B Instruct 2512?
For Mistral Large 3 675B Instruct 2512, Q4_K_M (445.5 GB) offers the best balance of quality and VRAM usage. Q5_K_S (510.5 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 204.2 GB.
VRAM requirement by quantization
IQ2_XXS204.2 GBIQ3_XS306.3 GBQ3_K_L380.5 GBQ4_K_M ★445.5 GBQ5_K_S510.5 GBBF161485.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Mistral Large 3 675B Instruct 2512 on a Mac?
Mistral Large 3 675B Instruct 2512 requires at least 204.2 GB at IQ2_XXS, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Mistral Large 3 675B Instruct 2512 locally?
Yes — Mistral Large 3 675B Instruct 2512 can run locally on consumer hardware. At Q4_K_M quantization it needs 445.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- What's the download size of Mistral Large 3 675B Instruct 2512?
At Q4_K_M, the download is about 405.00 GB. The full-precision BF16 version is 1350.00 GB. The smallest option (IQ2_XXS) is 185.63 GB.
- Which GPUs can run Mistral Large 3 675B Instruct 2512?
No single consumer GPU has enough VRAM to run Mistral Large 3 675B Instruct 2512 at Q4_K_M (445.5 GB). Multi-GPU or professional hardware is required.
- Which devices can run Mistral Large 3 675B Instruct 2512?
3 devices with unified memory can run Mistral Large 3 675B Instruct 2512 at Q4_K_M (445.5 GB), including Mac Studio (M3 Ultra, 512GB), NVIDIA DGX A100 640GB, NVIDIA DGX H100. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.