RAG pipelines live and die by two numbers: how many documents per minute you can process during ingestion, and how accurately the model answers when it retrieves the right chunk. LLaMA 3 8B and DeepSeek 7B take opposite sides of that trade-off, and the right choice depends entirely on where your bottleneck sits.
Ingestion Speed vs Answer Quality
Tested on an RTX 3090, INT4 quantisation, vLLM with continuous batching. Document set: 10,000 mixed-format chunks averaging 512 tokens each. Retrieval evaluation used a held-out question set graded against ground-truth answers. Live numbers available on the benchmark tool.
| Model (INT4) | Chunk Throughput (docs/min) | Retrieval Accuracy | Context Utilisation | VRAM Used |
|---|---|---|---|---|
| LLaMA 3 8B | 259 | 87.0% | 90.4% | 6.5 GB |
| DeepSeek 7B | 181 | 90.5% | 83.1% | 5.8 GB |
LLaMA chews through documents 43% faster — 259 docs/min versus 181. That gap is enormous during initial corpus ingestion when you are processing hundreds of thousands of chunks overnight. But DeepSeek answers 3.5 percentage points more accurately when those chunks are retrieved at query time. It also has a critical advantage that the throughput table does not capture: a 32K context window versus LLaMA’s 8K.
The Context Window Advantage in RAG
| Specification | LLaMA 3 8B | DeepSeek 7B |
|---|---|---|
| Parameters | 8B | 7B |
| Architecture | Dense Transformer | Dense Transformer |
| Context Length | 8K | 32K |
| VRAM (FP16) | 16 GB | 14 GB |
| VRAM (INT4) | 6.5 GB | 5.8 GB |
| Licence | Meta Community | MIT |
With 32K tokens of context, DeepSeek can ingest more retrieved chunks per query. Where LLaMA tops out at roughly three to four chunks before hitting its context ceiling, DeepSeek can pack in twelve or more. That directly explains the retrieval accuracy gap — more context means less information lost. See our LLaMA 3 VRAM guide and DeepSeek VRAM guide for deployment planning.
The Economics
| Cost Factor | LLaMA 3 8B | DeepSeek 7B |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 6.5 GB | 5.8 GB |
| Est. Monthly Server Cost | £105 | £174 |
| Throughput Advantage | 13% faster | 5% cheaper/tok |
Same hardware, same rental cost. The effective price difference comes down to how you use the GPU. LLaMA’s throughput advantage makes ingestion cheaper per document. DeepSeek’s accuracy advantage means fewer follow-up queries and less human review. Model your own workload with the cost-per-million-tokens calculator.
Our Recommendation
Pick LLaMA 3 8B if your RAG pipeline runs on short documents where 8K context is plenty — think FAQ databases, product catalogues, or standardised form responses. The throughput advantage makes nightly re-indexing dramatically faster. Explore more matchups in the comparison index.
Pick DeepSeek 7B if you are building a knowledge base over long-form documents — legal contracts, technical manuals, research papers — where stuffing more chunks into context directly improves answer quality. The accuracy lift is worth the slower ingestion. For setup guidance, see the self-hosted LLM guide and best GPU for inference.
See also: LLaMA 3 vs DeepSeek for Chatbots | LLaMA 3 vs Mistral for RAG
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