Most RAG benchmarks test models in isolation. Real RAG pipelines care about something different: can the model synthesise an accurate answer from three retrieved chunks while keeping latency under a second? We tested LLaMA 3 8B and Mistral 7B under exactly those conditions on dedicated GPU hardware.
Retrieval Performance Head to Head
RTX 3090, vLLM, INT4 quantisation, continuous batching. Mixed-format document corpus, 512-token chunks, graded retrieval accuracy against ground truth. Live speed data.
| Model (INT4) | Chunk Throughput (docs/min) | Retrieval Accuracy | Context Utilisation | VRAM Used |
|---|---|---|---|---|
| LLaMA 3 8B | 173 | 90.0% | 93.4% | 6.5 GB |
| Mistral 7B | 128 | 87.9% | 85.2% | 5.5 GB |
LLaMA wins on every dimension here. It processes documents 35% faster, retrieves answers 2 points more accurately, and uses 8 percentage points more of the available context effectively. That last metric — context utilisation — is particularly telling. It measures how well the model actually uses the chunks you feed it rather than ignoring them and hallucinating. LLaMA’s dense attention architecture pays off here: it genuinely reads the full context window rather than letting older tokens fade through a sliding window.
Why Architecture Matters for RAG
| Specification | LLaMA 3 8B | Mistral 7B |
|---|---|---|
| Parameters | 8B | 7B |
| Architecture | Dense Transformer | Dense Transformer + SWA |
| Context Length | 8K | 32K |
| VRAM (FP16) | 16 GB | 14.5 GB |
| VRAM (INT4) | 6.5 GB | 5.5 GB |
| Licence | Meta Community | Apache 2.0 |
Mistral technically has a 32K context window, four times LLaMA’s 8K. But its sliding window attention means that tokens near the start of the context gradually lose influence. In RAG, the most important information often appears in the first retrieved chunk — and that is exactly where SWA starts to degrade. LLaMA’s shorter but fully-attended window gives every chunk equal weight. For sizing, see the LLaMA VRAM guide and Mistral VRAM guide.
Cost Comparison
| Cost Factor | LLaMA 3 8B | Mistral 7B |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 6.5 GB | 5.5 GB |
| Est. Monthly Server Cost | £178 | £116 |
| Throughput Advantage | 5% faster | 7% cheaper/tok |
Mistral’s lower VRAM footprint saves about 1 GB on the card, which can help if you are co-locating an embedding model on the same GPU. Use the cost calculator to model your specific pipeline economics. More hardware guidance in the best GPU for inference breakdown.
The Pick
LLaMA 3 8B is the stronger RAG model. Higher accuracy, better context utilisation, faster document processing. It treats every token in its context window with equal attention, which is precisely what RAG demands. The only concession is a slightly larger VRAM footprint, which is irrelevant on a 24 GB card. Full deployment walkthrough in the self-host guide.
Choose Mistral only if you need to co-host the LLM alongside a large embedding model on a single GPU and every megabyte of VRAM counts. See more at the comparisons hub.
See also: LLaMA 3 vs Mistral for Chatbots | LLaMA 3 vs DeepSeek for RAG
Build Your RAG Pipeline
Deploy LLaMA 3 8B on bare-metal GPUs. No shared tenancy, no token limits, full root access.
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