Mistral Large Instruct 2411 — Hardware Requirements & GPU Compatibility
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Mistral Large Instruct 2411 is a 122.6B-parameter open language model from Mistral AI in the Mistral family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 74.60 GB of VRAM — see which GPUs and Macs can run it below.
6.6K downloads
266 likesSpecifications
- Publisher
- Mistral AI
- Family
- Mistral
- Parameters
- 122.6B
- Architecture
- MistralForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 32,768
- Release Date
- 2024-11-14
- License
- Other
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HuggingFace
How Much VRAM Does Mistral Large Instruct 2411 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 53.1 GB | 99.7 GB | 52.11 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 54.7 GB | 101.2 GB | 53.64 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 60.8 GB | 107.3 GB | 59.77 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 74.6 GB | 121.1 GB | 73.57 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 88.4 GB | 134.9 GB | 87.36 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 102.2 GB | 148.7 GB | 101.15 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 123.7 GB | 170.2 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 2411?
Q4_K_M · 74.6 GBMistral Large Instruct 2411 (Q4_K_M) requires 74.6 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 97+ GB is recommended. Using the full 131K context window can add up to 46.5 GB, bringing total usage to 121.1 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Mistral Large Instruct 2411?
Q4_K_M · 74.6 GB18 devices with unified memory can run Mistral Large Instruct 2411, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightWhere to Download Mistral Large Instruct 2411
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 2411 need?
Mistral Large Instruct 2411 requires 74.6 GB of VRAM at Q4_K_M, or 246.3 GB at BF16. Full 131K context adds up to 46.5 GB (121.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 122.6B × 4.8 bits ÷ 8 = 73.6 GB
KV Cache + Overhead ≈ 1 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 47.5 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M74.6 GBQ4_K_M + full context121.1 GB- Can NVIDIA GeForce RTX 5090 run Mistral Large Instruct 2411?
No — Mistral Large Instruct 2411 requires at least 53.1 GB at Q2_K, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- What's the best quantization for Mistral Large Instruct 2411?
For Mistral Large Instruct 2411, Q4_K_M (74.6 GB) offers the best balance of quality and VRAM usage. Q5_K_S (85.3 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 53.1 GB.
VRAM requirement by quantization
Q2_K53.1 GBQ3_K_L63.9 GBQ4_K_M ★74.6 GBQ5_K_S85.3 GBQ6_K102.2 GBBF16246.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Mistral Large Instruct 2411 on a Mac?
Mistral Large Instruct 2411 requires at least 53.1 GB at Q2_K, 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 2411 locally?
Yes — Mistral Large Instruct 2411 can run locally on consumer hardware. At Q4_K_M quantization it needs 74.6 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Mistral Large Instruct 2411?
At Q4_K_M, Mistral Large Instruct 2411 can reach ~64 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 ÷ 74.6 × 0.65 = ~70 tok/s
Estimated speed at Q4_K_M (74.6 GB)
~70 tok/s~70 tok/s~64 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 2411?
At Q4_K_M, the download is about 73.57 GB. The full-precision BF16 version is 245.22 GB. The smallest option (Q2_K) is 52.11 GB.
- Which GPUs can run Mistral Large Instruct 2411?
No single consumer GPU has enough VRAM to run Mistral Large Instruct 2411 at Q4_K_M (74.6 GB). Multi-GPU or professional hardware is required.
- Which devices can run Mistral Large Instruct 2411?
19 devices with unified memory can run Mistral Large Instruct 2411 at Q4_K_M (74.6 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.