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Mistral Nemo 12B Instruct

Mistral Nemo 12B Instruct needs roughly 9.8 GB VRAM at Q4_K_M quantization (28.8 GB at FP16). 99 GPUs we track can run it fully in VRAM at 8k context.

99 GPUs run this natively · 5 with CPU offload

Mistral AI12.2B params125k contextApache 2.0Commercial use ok

Mistral Nemo 12B Instruct is a 12.2B parameter dense model developed by Mistral AI. July 2024 12B model with 128K context and multilingual support.

To run Mistral Nemo 12B Instruct locally: Q5_K_M ~8-9GB — fits on 12GB GPUs.

Apache 2.0 with strong multilingual capabilities — good balance of size and quality.

VRAM at each quantization

Mistral Nemo 12B Instruct natively supports a longer context window, but the table below is capped at 8k for comparability — KV cache grows linearly with context length.

QuantWeightsKV cacheTotal
FP3248.8 GB1.34 GB56.2 GB
BF1624.4 GB1.34 GB28.8 GB
FP1624.4 GB1.34 GB28.8 GB
Q8_013.0 GB1.34 GB16.0 GB
Q6_K10.0 GB1.34 GB12.7 GB
Q5_K_Mrec8.7 GB1.34 GB11.2 GB
Q4_K_M7.4 GB1.34 GB9.8 GB
Q3_K_M5.9 GB1.34 GB8.1 GB
Q2_K4.7 GB1.34 GB6.7 GB
NVFP4cuda6.1 GB1.34 GB8.3 GB

KV cache figures assume 8k context at FP16. NVFP4 quantization requires a CUDA-capable GPU. Enable TurboQuant in the calculator to see reduced KV cache estimates.

Benchmarks

MMLU-Pro
35.6
IFEval
63.8
MATH
55

GPUs that run Mistral Nemo 12B Instruct natively (99)

Plus 5 GPUs that run it with CPU offload (slower)
Hugging Face ↗Ollama ↗Released 2024-07-18

Compare Mistral Nemo 12B Instruct with other models

How to run Mistral Nemo 12B Instruct locally

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Q5_K_M needs 11.2 GB — fits a single high-end consumer GPU (24 GB).

Ollama

ollama run mistral-nemo:12b

llama.cpp

./llama-cli -m mistral-nemo-12b-instruct.Q5_K_M.gguf -c 8192 -ngl 99

LM Studio: Search for 'Mistral Nemo' in LM Studio. The Q5_K_M variant fits on a 12 GB GPU and supports up to 128K context.

Why this quantization? At 12.2B dense parameters with 8 KV heads, Q5_K_M requires about 8 GB for weights and fits within a 12 GB GPU with room for moderate context. The model was co-developed by Mistral and NVIDIA, with a focus on multilingual capability and long context. Q5 preserves the model's strengths better than Q4, and the VRAM savings of dropping lower are minimal at this model size.

Who is Mistral Nemo 12B Instruct for?

Multilingual users with 12-16 GB GPUs who need long-context support up to 128K tokens. A good choice for non-English workloads under the Apache 2.0 license, especially if you need a balance between capability and hardware requirements.

Best for

  • Multilingual chat and content generation across European and Asian languages
  • Long-document analysis leveraging the 128K context window
  • Function calling and tool-use applications under Apache 2.0
  • Serving as a capable all-rounder on mid-range GPUs

Not ideal for

  • Math and science reasoning -- newer 12B models like Gemma 3 12B and Phi-4 14B outperform significantly
  • Code generation where specialized models are much stronger
  • Tasks where English-only performance is all that matters and benchmark scores are the priority

Continue reading

vram-guides9 min
Best LLMs for 16 GB VRAM GPUs (2026)
vram-guides8 min
Best LLMs for 12 GB VRAM (2026)
vram-guides9 min
Mistral & Mixtral Family: Complete VRAM Reference
hardware10 min
How to Choose Your First GPU for Local LLMs
hardware9 min
Best GPUs Under $500 for LLMs (2026)
hardware10 min
NVIDIA RTX 40 Series for LLMs: Complete VRAM & Performance Guide

Frequently asked questions

What are the VRAM requirements for Mistral Nemo 12B Instruct?
Mistral Nemo 12B Instruct requires approximately 9.8 GB of VRAM at Q4_K_M quantization, 16.0 GB at Q8, and 28.8 GB at FP16. These numbers assume 8k context window; VRAM scales linearly with context length due to the KV cache.
How many parameters does Mistral Nemo 12B Instruct have?
Mistral Nemo 12B Instruct has 12.2 billion parameters.
How capable is Mistral Nemo 12B Instruct?
Mistral Nemo 12B Instruct has an MMLU-Pro score of 35.6, making it well-suited for lightweight tasks, prototyping, and resource-constrained environments.
Can Mistral Nemo 12B Instruct run on a 16 GB GPU?
Yes. Mistral Nemo 12B Instruct needs 9.8 GB at Q4_K_M, which fits in a 16 GB GPU like the RTX 4080 or RTX 4070 Ti Super.
What is the smallest quantization for Mistral Nemo 12B Instruct that fits in 24 GB of VRAM?
At NVFP4, Mistral Nemo 12B Instruct needs 8.3 GB — the highest-quality quantization that fits in 24 GB of VRAM.
What GPU do I need to run Mistral Nemo 12B Instruct locally?
A 16 GB GPU is enough. At Q4_K_M, Mistral Nemo 12B Instruct needs 9.8 GB VRAM. Good options: RTX 4080 (16 GB), RTX 4070 Ti Super (16 GB).