Mixtral 8x7BvsLlama 3.1 70B
Complete comparison between Mixtral 8x7B (47B parameters, Mistral AI) and Llama 3.1 70B (70B, Meta). VRAM required by quantization, tokens per second measured on 4 reference GPUs, use-case verdict, license, installation commands. All figures are calculated from catalog data—no copy-pasting between pages.
In brief
| Feature | Mixtral 8x7B | Llama 3.1 70B |
|---|---|---|
| Settings | 47B | 70B |
| Family | Mistral | Llama |
| Author | Mistral AI | Meta |
| Origin | FR | US |
| License | Apache 2.0 | Llama 3 Community |
| Context | 32 768 tokens | 131,072 tokens |
| Output | December 2023 | 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 | Mixtral 8x7B | Llama 3.1 70B |
|---|---|---|
| Q4_K_M (lightweight) | 26 GB | 40 GB |
| Q5_K_M (balanced) | 32 GB | 48 GB |
| Q8 (near-lossless) | 50 GB | 75 GB |
| FP16 (max quality) | 94 GB | 140 GB |
| CPU-only RAM | 48 GB | 64 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 | Mixtral 8x7B | Llama 3.1 70B |
|---|---|---|
| RTX 4090 (24 GB) | ✗ too heavy | ✗ too heavy |
| RTX 4080 (16 GB) | ✗ too heavy | ✗ too heavy |
| RTX 3060 12GB (12 GB) | ✗ too heavy | ✗ too heavy |
| Apple M4 Pro (48 GB) (36 GB) | 12 tok/s · Q5_K_M | ✗ too heavy |
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
Mixtral 8x7B
8-expert MoE. High quality, but VRAM-hungry.
- Quality far above the size ratio
- 32k context
- Completely free Apache 2.0
- Very good at coding and multilingual tasks
- 26 GB VRAM Q4 (like a dense 47B model)
- Outpaced by Qwen 3 and Llama 3.3 in 2025
Installation
Llama 3.1 70B
The in-house GPT-4. Reserved for serious rigs (2×3090 mini).
- Open-weight quality benchmark
- 128k context
- Very good at reasoning
- 40 GB of VRAM in Q4 (2×3090 minimum)
- Restricted commercial license >700M MAU
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Mixtral 8x7B or Llama 3.1 70B?
On a RTX 4090, Mixtral 8x7B does not fit in 24 GB—you need to offload part of it to the CPU; Llama 3.1 70B does not fit in 24 GB either. See the configurator to test your exact GPU.
Mixtral 8x7B or Llama 3.1 70B: which is better in French?
For formal French (writing, summarization, translation), Mixtral 8x7B 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, Mixtral 8x7B fits in 26 GB versus 40 GB for its rival—a 14 GB gap, significant if you are targeting an RTX 3060 or a 4060 Ti 8 GB.
Can these models be used in commercial production?
Mixtral 8x7B is under Apache 2.0 — free for commercial use. Llama 3.1 70B 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: Mixtral 8x7B (47B). More capable: Llama 3.1 70B (70B). If you are unsure, run the 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.