Mistral Nemo 12B Instruct

Positioning

A NVIDIA-Mistral collaboration that targets the 12B class with European multilingual strength and a 128K context. The right pick when you specifically need Apache license + 12B-class capability + non-English performance.

Strengths

• Apache 2.0 license — cleanest in this size tier.
• Strong European multilingual — French, German, Spanish, Italian, Portuguese are all near-native quality.
• 128K context with reasonable recall — better than Llama 3.1 8B at the same advertised window.

Limitations

• Quality lags Qwen 2.5 14B for similar VRAM at Q4.
• Knowledge breadth is narrower than Llama 3.1 8B on English long-tail facts.
• No thinking-mode option — straight dense model.

Real-world performance on RTX 4090

• Q4_K_M (7.5 GB): 78–95 tok/s decode, TTFT ~95 ms
• Q5_K_M (8.7 GB): 68–82 tok/s
• Q8_0 (13.0 GB): 50–62 tok/s

Should you run this locally?

Yes, for Apache-licensed European multilingual workloads, or as a strict upgrade from Mistral 7B v0.3 in existing pipelines. No, for English-only tasks where Qwen 2.5 14B or Llama 3.1 8B are stronger.

How it compares

• vs Mistral 7B v0.3 → Nemo replaces 7B v0.3 in the modern Mistral lineup; same Apache license, materially stronger.
• vs Llama 3.1 8B → close call. Llama wins on English instruction polish; Nemo wins on multilingual + license simplicity.
• vs Qwen 2.5 14B → Qwen 2.5 14B is stronger absolute capability; Nemo has cleaner license.
• vs Pixtral 12B → Pixtral is the multimodal sibling; pick Pixtral if you need vision, Nemo if text-only.

Run this yourself

ollama pull mistral-nemo:12b-instruct-q4_K_M
ollama run mistral-nemo:12b-instruct-q4_K_M

Settings: Q4_K_M GGUF, 32768 ctx, llama.cpp/CUDA, RTX 4090