Mistral Nemo Instruct 2407 — Hardware Requirements & GPU Compatibility
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Mistral Nemo Instruct 2407 is a 12.2B-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 8.07 GB of VRAM — see which GPUs and Macs can run it below.
558.3K downloads
1.7K likesSpecifications
- Publisher
- Mistral AI
- Family
- Mistral
- Parameters
- 12.2B
- Architecture
- MistralForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 131,072
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Mistral Nemo Instruct 2407 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q4_K_M | 4.80 | 8.1 GB | 34.5 GB | 7.35 GB | 4-bit medium quantization — most popular sweet spot |
| Q6_K | 6.60 | 10.8 GB | 37.3 GB | 10.10 GB | 6-bit quantization, very good quality |
Which GPUs Can Run Mistral Nemo Instruct 2407?
Q4_K_M · 8.1 GBMistral Nemo Instruct 2407 (Q4_K_M) requires 8.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 11+ GB is recommended. Using the full 131K context window can add up to 26.4 GB, bringing total usage to 34.5 GB. 28 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Mistral Nemo Instruct 2407?
Q4_K_M · 8.1 GB27 devices with unified memory can run Mistral Nemo Instruct 2407, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (4)
Frequently Asked Questions
- How much VRAM does Mistral Nemo Instruct 2407 need?
Mistral Nemo Instruct 2407 requires 8.1 GB of VRAM at Q4_K_M, or 10.8 GB at Q6_K. Full 131K context adds up to 26.4 GB (34.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 12.2B × 4.8 bits ÷ 8 = 7.3 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 27.2 GB (at full 131K context)
VRAM usage by quantization
Q4_K_M8.1 GBQ4_K_M + full context34.5 GB- What's the best quantization for Mistral Nemo Instruct 2407?
For Mistral Nemo Instruct 2407, Q4_K_M (8.1 GB) offers the best balance of quality and VRAM usage. Q6_K (10.8 GB) provides better quality if you have the VRAM.
VRAM requirement by quantization
Q4_K_M ★8.1 GBQ6_K10.8 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Mistral Nemo Instruct 2407 on a Mac?
Mistral Nemo Instruct 2407 requires at least 8.1 GB at Q4_K_M, 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 Nemo Instruct 2407 locally?
Yes — Mistral Nemo Instruct 2407 can run locally on consumer hardware. At Q4_K_M quantization it needs 8.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Mistral Nemo Instruct 2407?
At Q4_K_M, Mistral Nemo Instruct 2407 can reach ~361 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~81 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 8.1 × 0.55 = ~361 tok/s
Estimated speed at Q4_K_M (8.1 GB)
~361 tok/s~81 tok/s~270 tok/s~223 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Mistral Nemo Instruct 2407?
At Q4_K_M, the download is about 7.35 GB. The full-precision Q6_K version is 10.10 GB.
- Which GPUs can run Mistral Nemo Instruct 2407?
28 consumer GPUs can run Mistral Nemo Instruct 2407 at Q4_K_M (8.1 GB). Top options include AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 6900 XT, AMD Radeon RX 6700 XT. 17 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Mistral Nemo Instruct 2407?
27 devices with unified memory can run Mistral Nemo Instruct 2407 at Q4_K_M (8.1 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.