Mistral Nemo 12B Instruct

Overview

Mistral AI and NVIDIA's co-developed 12B instruct model with 128k context, the Tekken tokenizer, and strong European multilingual coverage.

When to pick this model

• Multilingual chat across European languages
• Long-context summarization and RAG
• Replacing Mistral 7B with a noticeable quality bump
• Apache 2.0 commercial deployments on a single 24GB GPU
• NVIDIA-tuned inference stacks

VRAM requirements by quantization

Quantization	VRAM required
Q4_K_M (recommended)	7 GB
Q5_K_M	9 GB
Q8_0	13 GB
FP16 (no quantization)	24 GB

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

In practice, Mistral Nemo 12B Instruct fits an 8 GB consumer card at Q4_K_M (7 GB). Stepping up to Q8_0 nearly doubles the footprint to 13 GB, and unquantized FP16 weights take 24 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.

Without a GPU, Mistral Nemo 12B Instruct needs roughly 16 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 8 tokens/sec on entry-level GPUs, on the order of 25 tokens/sec on a mid-range card, and up to 70 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.

What hardware do you need

The table below matches Mistral Nemo 12B Instruct to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.

GPU memory	Example cards	Best fit for Mistral Nemo 12B Instruct
8 GB	RTX 5070 Laptop, RTX 5060, RTX 5060 Ti 8GB	Q4_K_M (7 GB used)
12 GB	RTX 5070, RTX 5070 Ti Laptop, RTX 4080 Laptop	Q5_K_M (9 GB used)
16 GB	RTX 5080, RTX 4080 Super, Radeon RX 9070 XT	Q8_0 (13 GB used)
24 GB	RTX 4090, Radeon RX 7900 XTX, RTX 5090 Laptop	FP16 (24 GB used)
32 GB	RTX 5090	FP16 (24 GB used)

Which GPU should you buy to run Mistral Nemo 12B Instruct?

To run Mistral Nemo 12B Instruct locally at Q4, you need ~7 GB of VRAM. The best value for this is a RTX 5060 (8 GB VRAM).

Published benchmark scores

Benchmark	Score
MMLU	68
HellaSwag	83.5
Winogrande	76.8

Scores published by the model author or aggregated from public leaderboards. Re-measured monthly by our editorial team.

To put Mistral Nemo 12B Instruct in context: its MMLU score of 68 ranks #24 of the 34 catalog models with a published MMLU result (catalog median 73.4). Scores are author-published and measured under different harnesses, so treat gaps of a few points as noise rather than a verdict.

Strengths

• 128k context window
• Strong European multilingual performance
• Apache 2.0 license
• Efficient Tekken tokenizer reduces token counts

Limitations

• Reasoning trails Mistral Small 3.1
• No vision
• Eclipsed by Small 3 on most general benchmarks

Typical workloads

In our catalog grid, Mistral Nemo 12B Instruct is filed under Multilingual Chat, Long Context, Tools/Agents — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multilingual workloads; French-language output where quality matters.

The 125k-token context window covers long chats and mid-sized documents, though very large retrieval workloads will need chunking. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.

Architecture & training

Architecture: Dense Transformer · GQA · Tekken tokenizer (131k vocab)

Training: Co-trained by Mistral × NVIDIA. European multilingual corpus.

Quick start

ollama run mistral-nemo:12b

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.

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Frequently asked questions

How much VRAM does Mistral Nemo 12B Instruct need?

At the recommended Q4_K_M quantization, Mistral Nemo 12B Instruct needs about 7 GB of VRAM. Q8_0 takes 13 GB, and unquantized FP16 weights take 24 GB.

Can Mistral Nemo 12B Instruct run without a GPU?

Yes — with roughly 16 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.

What context window does Mistral Nemo 12B Instruct support?

Mistral Nemo 12B Instruct supports a 125k-token context window (128,000 tokens).

Can I use Mistral Nemo 12B Instruct commercially?

Yes. Mistral Nemo 12B Instruct is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.

How fast is Mistral Nemo 12B Instruct on consumer hardware?

Our compatibility engine estimates on the order of 25 tokens/sec on a mid-range GPU and up to 70 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.

Which quantization of Mistral Nemo 12B Instruct should I download first?

Start with Q4_K_M (7 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It already fits an 8 GB card at Q4_K_M.