Running a RAG pipeline on a tight budget? Phi-3 Mini uses just 3.2 GB of VRAM at INT4 — leaving over 20 GB free on an RTX 3090 for your embedding model, vector index, and anything else you want to co-locate. That alone makes it attractive. But can a 3.8B model match LLaMA 3 8B on actual retrieval quality? We tested it.
RAG Benchmark Results
RTX 3090, vLLM, INT4, continuous batching. 10,000 document chunks, retrieval accuracy graded against ground truth. Live speed data.
| Model (INT4) | Chunk Throughput (docs/min) | Retrieval Accuracy | Context Utilisation | VRAM Used |
|---|---|---|---|---|
| LLaMA 3 8B | 256 | 91.2% | 86.5% | 6.5 GB |
| Phi-3 Mini | 231 | 92.3% | 85.1% | 3.2 GB |
Phi-3 edges LLaMA on retrieval accuracy: 92.3% versus 91.2%. Context utilisation is nearly tied at 85.1% versus 86.5%. The throughput gap favours LLaMA at 256 docs/min versus 231, a modest 11% lead. Considering Phi-3 achieves this with less than half the parameters and half the VRAM, its efficiency per parameter is remarkable.
The Co-Location Opportunity
| Specification | LLaMA 3 8B | Phi-3 Mini |
|---|---|---|
| Parameters | 8B | 3.8B |
| Architecture | Dense Transformer | Dense Transformer |
| Context Length | 8K | 128K |
| VRAM (FP16) | 16 GB | 7.6 GB |
| VRAM (INT4) | 6.5 GB | 3.2 GB |
| Licence | Meta Community | MIT |
With Phi-3 at 3.2 GB, you have ~20 GB free on a 24 GB card. That is enough to co-locate a large embedding model like bge-large-en-v1.5, a reranker, and still have headroom. With LLaMA at 6.5 GB, you have roughly 17 GB — still workable, but tighter for complex multi-model RAG pipelines. Phi-3’s 128K context window also means you can feed it far more retrieved chunks per query than LLaMA’s 8K allows. See the LLaMA VRAM guide and Phi-3 VRAM guide.
Cost Breakdown
| Cost Factor | LLaMA 3 8B | Phi-3 Mini |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 6.5 GB | 3.2 GB |
| Est. Monthly Server Cost | £152 | £162 |
| Throughput Advantage | 13% faster | 3% cheaper/tok |
If Phi-3 lets you consolidate your entire RAG stack onto one GPU instead of two, the monthly savings are substantial. That is the real cost advantage — not per-token pricing, but infrastructure consolidation. Model specifics at the cost calculator. Hardware options at best GPU for inference.
The Pick
Phi-3 Mini for budget-conscious RAG deployments. Higher accuracy, 128K context for more chunks, and a VRAM footprint that enables single-GPU pipeline consolidation. If you are building a knowledge base where every saved GPU matters, Phi-3 is the practical choice. See the comparisons hub for alternatives.
LLaMA 3 8B for throughput-heavy ingestion. If your bottleneck is processing millions of documents into your vector store overnight, LLaMA’s 11% throughput advantage adds up. Once ingestion is done, you could even switch to Phi-3 for query-time generation. Deployment help in the self-host guide.
See also: LLaMA 3 vs Phi-3 for Chatbots | LLaMA 3 vs DeepSeek for RAG
Build a RAG Pipeline on One GPU
Run Phi-3 Mini alongside your embedding model on a single dedicated server. Full root access, no limits.
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