A 128K context window on a 7B parameter model. That is Qwen 2.5 7B‘s headline feature, and for RAG pipelines it is a game-changer. Where LLaMA 3 8B can fit maybe three or four retrieved chunks into its 8K window, Qwen can pack in dozens. We tested whether that theoretical advantage translates to real retrieval accuracy on dedicated GPU hardware.
RAG Pipeline Results
RTX 3090, vLLM, INT4, continuous batching. 10,000 mixed-format document chunks, graded retrieval against ground truth. Live data.
| Model (INT4) | Chunk Throughput (docs/min) | Retrieval Accuracy | Context Utilisation | VRAM Used |
|---|---|---|---|---|
| LLaMA 3 8B | 228 | 89.2% | 89.6% | 6.5 GB |
| Qwen 2.5 7B | 262 | 92.9% | 89.9% | 5.8 GB |
Qwen leads on every dimension. It processes 15% more documents per minute, scores 3.7 points higher on retrieval accuracy, and achieves comparable context utilisation despite having a vastly larger context window to fill. The accuracy gap is the standout: 92.9% versus 89.2% means roughly one fewer wrong answer in every thirty queries.
Why Context Length Dominates in RAG
| 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 |
Both models use dense transformer attention — no sliding window tricks. The critical difference is window size. With 128K tokens, Qwen can ingest many more retrieved chunks per query, which directly reduces the chance of the answer falling outside the model’s visible context. LLaMA has to be more aggressive about chunk selection, and any retriever mistakes become answer mistakes. Sizing details in the LLaMA VRAM guide and Qwen VRAM guide.
Pipeline Economics
| 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 | £155 | £86 |
| Throughput Advantage | 3% faster | 10% cheaper/tok |
Qwen is both faster and more accurate, making it strictly cheaper per correct answer. The 10% per-token cost advantage compounds across a high-volume RAG pipeline. Model your specifics at the cost calculator. Hardware options at best GPU for inference.
Our Take
Qwen 2.5 7B is the superior RAG model. Faster, more accurate, less VRAM, longer context, and Apache 2.0 licensed. The only scenario where LLaMA makes more sense is if your entire pipeline is already built around LLaMA-specific tooling and a migration is not worth the engineering effort. For new deployments, Qwen is the default choice. See the comparisons hub for more pairings.
Setup instructions in the self-host LLM guide.
See also: LLaMA 3 vs Qwen for Chatbots | LLaMA 3 vs DeepSeek for RAG
Scale Your RAG Pipeline
Run Qwen 2.5 7B or LLaMA 3 8B on bare-metal GPUs. No shared tenancy, no usage caps, full root access.
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