Meta-Llama-3-8B-Instruct vs Mistral-7B-Instruct-v0.2
Meta-Llama-3-8B-Instruct (8.0B parameters) and Mistral-7B-Instruct-v0.2 (7.2B parameters) side by side: the memory each needs at every precision, what it costs to run on a live GPU, and the context window, KV cache and license where they are published. Numbers are computed from the models' published specs; this page does not rank quality.
Side by side
| Fact | Meta-Llama-3-8B-Instruct | Mistral-7B-Instruct-v0.2 |
|---|---|---|
| Parameters | 8.0B | 7.2B |
| Architecture | Grouped-query attention | Grouped-query attention |
| Context length | 8,192 tokens | 32,768 tokens |
| License | – | – |
| Published precision | BF16 | BF16 |
| VRAM needed, As published | 17.9 GB | 16.2 GB |
| VRAM needed, FP8 | 9.0 GB | 8.1 GB |
| VRAM needed, INT4 | 4.5 GB | 4.0 GB |
| Cheapest live fit, As published | RTX A5000 · $0.176/hr | RTX A5000 · $0.176/hr |
| Cheapest live fit, FP8 | RTX 4070 Super · $0.121/hr | RTX 4070 Super · $0.121/hr |
| Cheapest live fit, INT4 | RTX 4070 Super · $0.121/hr | RTX 4070 Super · $0.121/hr |
| KV cache per token (16-bit) | 128 KB | 128 KB |
| KV cache at 32k tokens | 4.00 GB | 4.00 GB |
| KV cache at 128k tokens | 16.0 GB | 16.0 GB |
VRAM is the weight size at each precision times a flat 1.2 overhead; see the methodology. The FP8 and INT4 rows need a quantized checkpoint or an engine that quantizes on load. The fit is the lowest-priced single GPU type that holds the model at that precision, or the lowest-priced multi-GPU set (up to 8) when none does. KV cache is for one sequence at 16-bit, computed from each model's config where the attention layout is known.
Which to pick
- Mistral-7B-Instruct-v0.2 needs less VRAM at its published precision (16.2 GB against 17.9 GB), so it fits on a smaller GPU.
- Mistral-7B-Instruct-v0.2 lists the longer context window (32,768 tokens against 8,192).
These follow only from the facts in the table above. Whether either model does your task well is a separate question this page does not answer.
Keep reading
- Meta-Llama-3-8B-Instruct: full VRAM table and live GPU fit
- Mistral-7B-Instruct-v0.2: full VRAM table and live GPU fit
- The Llama model series
- The Mistral model series
- All models that fit in 24 GB
Other comparisons
- Meta-Llama-3-8B-Instruct vs Qwen3-8B
- Meta-Llama-3-8B-Instruct vs Qwen2.5-7B-Instruct
- Meta-Llama-3-8B-Instruct vs Qwen2.5-Coder-7B-Instruct
- Meta-Llama-3-8B-Instruct vs granite-4.1-8b
- Mistral-7B-Instruct-v0.2 vs Qwen3-8B
- Mistral-7B-Instruct-v0.2 vs Qwen2.5-7B-Instruct
- Mistral-7B-Instruct-v0.2 vs Llama-3.1-8B-Instruct
- Mistral-7B-Instruct-v0.2 vs Qwen2.5-Coder-7B-Instruct
