Table of Contents
Quick Verdict
For a RAG pipeline processing legal discovery documents, the most important number is retrieval accuracy — because a missed clause in a contract can mean missed liability. LLaMA 3 70B scores 85.3% retrieval accuracy versus Qwen 72B’s 83.0%, making it the safer choice when precision matters more than throughput on a dedicated GPU server.
Where Qwen 72B gains an advantage is context utilisation (87.4% versus 85.9%), which suggests it makes better use of retrieved chunks when generating answers. This is partly explained by its 128K native context window versus LLaMA 3’s 8K limit — Qwen can ingest far more context per query without truncation.
Full data follows. See our GPU comparisons hub for additional matchups.
Specs Comparison
The 128K versus 8K context window gap is the defining spec for RAG. Qwen 72B can accept sixteen times more input context, meaning you can stuff substantially more retrieved passages into a single prompt without resorting to multi-pass strategies.
| Specification | LLaMA 3 70B | Qwen 72B |
|---|---|---|
| Parameters | 70B | 72B |
| Architecture | Dense Transformer | Dense Transformer |
| Context Length | 8K | 128K |
| VRAM (FP16) | 140 GB | 145 GB |
| VRAM (INT4) | 40 GB | 42 GB |
| Licence | Meta Community | Qwen |
Memory planning: LLaMA 3 70B VRAM requirements and Qwen 72B VRAM requirements.
Document Processing Benchmark
Tested on an NVIDIA RTX 3090 with vLLM, INT4 quantisation, and continuous batching. The document corpus included financial statements, insurance policies, and technical manuals. Check our tokens-per-second benchmark for more data.
| Model (INT4) | Chunk Throughput (docs/min) | Retrieval Accuracy | Context Utilisation | VRAM Used |
|---|---|---|---|---|
| LLaMA 3 70B | 181 | 85.3% | 85.9% | 40 GB |
| Qwen 72B | 183 | 83.0% | 87.4% | 42 GB |
Throughput is nearly identical at 181 versus 183 docs/min, so the selection criteria here is pure quality. LLaMA 3 70B retrieves more relevant information from the corpus, while Qwen 72B synthesises that information into answers more effectively. See our best GPU for LLM inference guide for hardware insights.
See also: LLaMA 3 70B vs Qwen 72B for Chatbot / Conversational AI for a related comparison.
See also: LLaMA 3 70B vs Mixtral 8x7B for Document Processing / RAG for a related comparison.
Cost Analysis
With identical throughput and similar VRAM needs, the cost difference between these two models for RAG is negligible. Selection should be driven by quality requirements, not economics.
| Cost Factor | LLaMA 3 70B | Qwen 72B |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 40 GB | 42 GB |
| Est. Monthly Server Cost | £154 | £95 |
| Throughput Advantage | 4% faster | 2% cheaper/tok |
Verify with our cost-per-million-tokens calculator.
Recommendation
Choose LLaMA 3 70B if your RAG pipeline prioritises retrieval precision — legal discovery, compliance auditing, or any domain where missing a relevant passage has material consequences.
Choose Qwen 72B if your use case benefits from its massive 128K context window and better answer synthesis. For knowledge bases with long documents or conversations that reference extensive prior context, Qwen’s ability to process more input per query reduces information loss.
Both models deploy efficiently on dedicated GPU hosting for production RAG pipelines.
Deploy the Winner
Run LLaMA 3 70B or Qwen 72B on bare-metal GPU servers with full root access, no shared resources, and no token limits.
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