Llama 3.1 8BvsQwen 2.5 7B
Complete comparison between Llama 3.1 8B (8B parameters, Meta) and Qwen 2.5 7B (7B, Alibaba). 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 | Llama 3.1 8B | Qwen 2.5 7B |
|---|---|---|
| Settings | 8B | 7B |
| Family | Llama | Qwen |
| Author | Meta | Alibaba |
| Origin | US | CN |
| License | Llama 3 Community | Apache 2.0 |
| Context | 131,072 tokens | 131,072 tokens |
| Output | July 2024 | September 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 | Llama 3.1 8B | Qwen 2.5 7B |
|---|---|---|
| Q4_K_M (lightweight) | 6 GB | 5 GB |
| Q5_K_M (balanced) | 7 GB | 6 GB |
| Q8 (near-lossless) | 10 GB | 9 GB |
| FP16 (max quality) | 18 GB | 16 GB |
| CPU-only RAM | 10 GB | 8 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 | Llama 3.1 8B | Qwen 2.5 7B |
|---|---|---|
| RTX 4090 (24 GB) | 80 tok/s · FP16 | 90 tok/s · FP16 |
| RTX 4080 (16 GB) | 30 tok/s · Q8 | 35 tok/s · FP16 |
| RTX 3060 12GB (12 GB) | 10 tok/s · Q8 | 12 tok/s · Q8 |
| Apple M4 Pro (48 GB) (36 GB) | 30 tok/s · FP16 | 35 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
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
Qwen 2.5 7B
Excellent at multilingual tasks and reasoning. 128k context.
- 128k context
- The best 7B of its era
- Apache 2.0
- Very strong at multilingual tasks and coding
- Outperformed by Qwen 3 8B in 2025
- Worse than Qwen2.5-Coder for specialized code
Installation
Frequently asked questions
Which model runs best on RTX 4090 (24 GB): Llama 3.1 8B or Qwen 2.5 7B?
On an RTX 4090, Llama 3.1 8B runs at FP16 (~80 tok/s), Qwen 2.5 7B in FP16 (~90 tok/s). In pure throughput, Qwen 2.5 7B wins. See the configurator to test your exact GPU.
Which uses the least VRAM?
In Q4_K_M, Qwen 2.5 7B fits in 5 GB versus 6 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?
Llama 3.1 8B is under Llama 3 Community—check the restrictions, especially the monthly user thresholds. Qwen 2.5 7B is under Apache 2.0 — also free to use. For SaaS, prefer Apache 2.0 or MIT.
Which one should you choose in 2026?
Depends on your primary constraint. Smaller / faster: Qwen 2.5 7B (7B). More capable: Llama 3.1 8B (8B). If you’re unsure, launch 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.