If you are building a self-hosted code assistant, the spec sheets suggest Qwen 2.5 7B should outperform LLaMA 3 8B — it has a higher HumanEval score, faster completions, lower latency, and uses less VRAM. And that is exactly what our benchmarks confirm. The real question is whether LLaMA has any remaining edge worth considering.
Head-to-Head Code Benchmarks
RTX 3090, vLLM, INT4 quantisation, continuous batching. Prompt set covering Python, TypeScript, SQL, and Go completions. Live speed data.
| Model (INT4) | HumanEval pass@1 | Completions/min | Avg Latency (ms) | VRAM Used |
|---|---|---|---|---|
| LLaMA 3 8B | 50.9% | 32 | 310 | 6.5 GB |
| Qwen 2.5 7B | 55.8% | 49 | 192 | 5.8 GB |
Qwen wins on every metric. 5 points higher on HumanEval, 53% more completions per minute, 38% lower latency, and 0.7 GB less VRAM. The performance gap is substantial enough that it is not just a benchmark artefact — you will feel the difference in a real IDE integration where every keystroke triggers a completion request.
Why Qwen Outperforms Here
| Specification | LLaMA 3 8B | Qwen 2.5 7B |
|---|---|---|
| Parameters | 8B | 7B |
| Architecture | Dense Transformer | Dense Transformer |
| Context Length | 8K | 128K |
| VRAM (FP16) | 16 GB | 15 GB |
| VRAM (INT4) | 6.5 GB | 5.8 GB |
| Licence | Meta Community | Apache 2.0 |
Qwen 2.5’s training data included a heavy emphasis on code, and its 128K context window means it can process entire files without chunking. For code generation specifically, that context length lets the model see all imports, type definitions, and utility functions before generating a completion — information LLaMA might miss if the relevant code sits beyond its 8K limit. See the LLaMA VRAM guide and Qwen VRAM guide.
Cost Comparison
| Cost Factor | LLaMA 3 8B | Qwen 2.5 7B |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 6.5 GB | 5.8 GB |
| Est. Monthly Server Cost | £99 | £104 |
| Throughput Advantage | 5% faster | 12% cheaper/tok |
Similar hardware costs, but Qwen’s higher throughput means the cost per completion is meaningfully lower. At 49 completions per minute versus 32, you serve 53% more developers from the same GPU. Model your savings at the cost calculator. More hardware guidance at best GPU for inference.
The Verdict
Qwen 2.5 7B is the better code generation model. Higher accuracy, faster completions, lower latency, less VRAM, and Apache 2.0 licensing. The only scenario where LLaMA remains preferable is if your development team works exclusively in English and you have existing infrastructure built around Meta’s model ecosystem. For everything else — especially multilingual codebases or polyglot teams — Qwen is the pick. Browse more at the comparisons hub.
Deployment walkthrough available in the self-host LLM guide.
See also: LLaMA 3 vs Qwen for Chatbots | LLaMA 3 vs DeepSeek for Code Generation
Power Your Code Assistant
Deploy Qwen 2.5 7B or LLaMA 3 8B on dedicated GPU hardware. No token limits, full root access.
Browse GPU Servers
