Mistral Large Instruct 2407 — Hardware Requirements & GPU Compatibility
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Mistral-Large-Instruct-2407, also known as Mistral Large 2, is Mistral AI's flagship dense instruction-tuned model of about 123 billion parameters, designed to run efficient single-node inference despite its size. It targets state-of-the-art reasoning, coding, and knowledge tasks with native function calling and JSON output for agentic use, and covers a dozen or more natural languages including French, German, Spanish, Chinese, Japanese, Russian, and Korean alongside more than 80 programming languages. Mistral reports reduced hallucination rates and stronger reasoning compared with the original Mistral Large. Even tuned for single-node deployment, a dense model of this size needs a multi-GPU workstation to run. Context length is 131,072 tokens (a 128k window). It is released under the Mistral AI Research License, a custom license restricting use to non-commercial research purposes; commercial deployment requires a separate license from Mistral AI. It was published in July 2024.
2.5K downloads
865 likesSpecifications
- Publisher
- Mistral AI
- Family
- Mistral
- Parameters
- 122.6B
- Release Date
- 2024-07-24
- License
- Other
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HuggingFace
How Much VRAM Does Mistral Large Instruct 2407 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 57.3 GB | — | 52.11 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 59.0 GB | — | 53.64 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 65.8 GB | — | 59.77 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 67.4 GB | — | 61.31 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 80.9 GB | — | 73.57 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 96.1 GB | — | 87.36 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 111.3 GB | — | 101.15 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 134.9 GB | — | 122.61 GB | 8-bit quantization, near-lossless |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Mistral Large Instruct 2407?
Q4_K_M · 80.9 GBMistral Large Instruct 2407 (Q4_K_M) requires 80.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 106+ GB is recommended. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Mistral Large Instruct 2407?
Q4_K_M · 80.9 GB18 devices with unified memory can run Mistral Large Instruct 2407, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, ASUS Ascent GX10.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightWhere to Download Mistral Large Instruct 2407
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 Instruct 2407 need?
Mistral Large Instruct 2407 requires 80.9 GB of VRAM at Q4_K_M, or 269.7 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 122.6B × 4.8 bits ÷ 8 = 73.6 GB
KV Cache + Overhead ≈ 7.3 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M80.9 GB- Can NVIDIA GeForce RTX 5090 run Mistral Large Instruct 2407?
No — Mistral Large Instruct 2407 requires at least 37.1 GB at IQ2_XXS, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for Mistral Large Instruct 2407?
For Mistral Large Instruct 2407, Q4_K_M (80.9 GB) offers the best balance of quality and VRAM usage. Q5_K_S (92.7 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 37.1 GB.
VRAM requirement by quantization
IQ2_XXS37.1 GBQ2_K57.3 GBQ4_067.4 GBQ4_K_M ★80.9 GBQ5_K_S92.7 GBBF16269.7 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Mistral Large Instruct 2407 on a Mac?
Mistral Large Instruct 2407 requires at least 37.1 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 Instruct 2407 locally?
Yes — Mistral Large Instruct 2407 can run locally on consumer hardware. At Q4_K_M quantization it needs 80.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Mistral Large Instruct 2407?
At Q4_K_M, Mistral Large Instruct 2407 can reach ~59 tok/s on AMD Instinct MI350X. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 80.9 × 0.65 = ~64 tok/s
Estimated speed at Q4_K_M (80.9 GB)
~64 tok/s~64 tok/s~59 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Mistral Large Instruct 2407?
At Q4_K_M, the download is about 73.57 GB. The full-precision BF16 version is 245.22 GB. The smallest option (IQ2_XXS) is 33.72 GB.
- Which GPUs can run Mistral Large Instruct 2407?
No single consumer GPU has enough VRAM to run Mistral Large Instruct 2407 at Q4_K_M (80.9 GB). Multi-GPU or professional hardware is required.
- Which devices can run Mistral Large Instruct 2407?
19 devices with unified memory can run Mistral Large Instruct 2407 at Q4_K_M (80.9 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.