Mistral Nemo 12B InstructvsLlama 3.1 8B
Complete comparison between Mistral Nemo 12B Instruct (12B parameters, Mistral AI) and Llama 3.1 8B (8B, Meta). VRAM required by quantization, tokens per second measured on 4 reference GPUs, verdict by use case, license, installation commands. All figures are calculated from catalog data—no copy-pasting between pages.
In brief
| Feature | Mistral Nemo 12B Instruct | Llama 3.1 8B |
|---|---|---|
| Settings | 12B | 8B |
| Family | Mistral | Llama |
| Author | Mistral AI | Meta |
| Origin | FR | US |
| License | Apache 2.0 | Llama 3 Community |
| Context | 128,000 tokens | 131,072 tokens |
| Output | July 2024 | July 2024 |
Memory footprint
Approximate VRAM required for inference with an average context window. The winner (in green) is the model that uses less — smaller models have the edge.
| Quantization | Mistral Nemo 12B Instruct | Llama 3.1 8B |
|---|---|---|
| Q4_K_M (lightweight) | 7 GB | 6 GB |
| Q5_K_M (balanced) | 9 GB | 7 GB |
| Q8 (near-lossless) | 13 GB | 10 GB |
| FP16 (max quality) | 24 GB | 18 GB |
| CPU-only RAM | 16 GB | 10 GB |
Estimated speed (tokens/second)
Estimates based on the best quantization that can fit on each GPU. Actual figures depend on the prompt, context, and engine (llama.cpp, vLLM, MLX). Methodology.
| Reference GPU | Mistral Nemo 12B Instruct | Llama 3.1 8B |
|---|---|---|
| RTX 4090 (24 GB) | 70 tok/s · FP16 | 80 tok/s · FP16 |
| RTX 4080 (16 GB) | 25 tok/s · Q8 | 30 tok/s · Q8 |
| RTX 3060 12GB (12 GB) | 8 tok/s · Q5_K_M | 10 tok/s · Q8 |
| Apple M4 Pro (48 GB) (36 GB) | 25 tok/s · FP16 | 30 tok/s · FP16 |
Verdict by use case
For each common use case, we indicate which of the two is better suited based on its tags, size, and specialization.
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Strengths and weaknesses
Mistral Nemo 12B Instruct
Co-developed with NVIDIA. 128k ctx, Tekken tokenizer, strong in European multilingual.
- 128k context
- Strong in European multilingual performance
- Completely free Apache 2.0
- Excellent 12B compromise
- Worse than Small 3.1 at reasoning
- No vision
Installation
Llama 3.1 8B
The open-weight benchmark. 128k context, strong across the board.
- 128k context
- Excellent quality-to-size ratio
- Very good at coding and following instructions
- Meta license <700M MAU
- Outperformed by Qwen 3 8B on most benchmarks
- No vision
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Mistral Nemo 12B Instruct or Llama 3.1 8B?
On an RTX 4090, Mistral Nemo 12B Instruct runs at FP16 (~70 tok/s), Llama 3.1 8B in FP16 (~80 tok/s). In pure throughput, Llama 3.1 8B wins. See the configurator to test your exact GPU.
Mistral Nemo 12B Instruct or Llama 3.1 8B: which is better in French?
For formal French (writing, summarization, translation), Mistral Nemo 12B Instruct has the edge—trained on a dense French-language corpus. The other remains usable, but for less demanding use cases.
Which uses the least VRAM?
In Q4_K_M, Llama 3.1 8B fits in 6 GB versus 7 GB for its rival — a 1 GB gap, significant if you're targeting an RTX 3060 or a 4060 Ti 8 GB.
Can these models be used in commercial production?
Mistral Nemo 12B Instruct is under Apache 2.0 — free for commercial use. Llama 3.1 8B is under Llama 3 Community — also check the terms. For SaaS, prefer Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Llama 3.1 8B (8B). More capable: Mistral Nemo 12B Instruct (12B). If you're unsure, run it configurator with your GPU and use case — it will decide based on both.
Alternatives to consider
If neither works for you, here are some neighboring models you could explore.