Meta-Llama-3-8B-Instruct vs Qwen2.5-7B-Instruct
Meta-Llama-3-8B-Instruct (8.0B parameters) and Qwen2.5-7B-Instruct (7.6B 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 | Qwen2.5-7B-Instruct |
|---|---|---|
| Parameters | 8.0B | 7.6B |
| Architecture | Grouped-query attention | Grouped-query attention |
| Context length | 8,192 tokens | 131K tokens full window (32K by default, extendable via YaRN); 8K max generation |
| License | – | Apache 2.0 |
| Published precision | BF16 | BF16 |
| VRAM needed, As published | 17.9 GB | 17.0 GB |
| VRAM needed, FP8 | 9.0 GB | 8.5 GB |
| VRAM needed, INT4 | 4.5 GB | 4.3 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 | 56 KB |
| KV cache at 32k tokens | 4.00 GB | 1.75 GB |
| KV cache at 128k tokens | 16.0 GB | 7.00 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
- Qwen2.5-7B-Instruct needs less VRAM at its published precision (17.0 GB against 17.9 GB), so it fits on a smaller GPU.
- Qwen2.5-7B-Instruct lists the longer context window (32,768 tokens against 8,192).
- Qwen2.5-7B-Instruct caches less per sequence at 32k tokens (1.8 GB against 4.0 GB), leaving more memory for batching.
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
- Qwen2.5-7B-Instruct: full VRAM table and live GPU fit
- The Llama model series
- The Qwen 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-Coder-7B-Instruct
- Meta-Llama-3-8B-Instruct vs Mistral-7B-Instruct-v0.2
- Meta-Llama-3-8B-Instruct vs granite-4.1-8b
- Qwen2.5-7B-Instruct vs Llama-3.1-8B-Instruct
- Qwen2.5-7B-Instruct vs Mistral-7B-Instruct-v0.2
- Qwen2.5-7B-Instruct vs granite-4.1-8b
