Table of Contents
Quick Verdict
Using code-specialised models for document RAG is unconventional, but teams building technical documentation systems or code-repository search engines have good reason to try. DeepSeek Coder achieves 90.1% retrieval accuracy on technical documents versus CodeLlama’s 84.0%, a 6.1-point advantage that reflects stronger comprehension of structured technical content on a dedicated GPU server.
CodeLlama counters with 54% higher document throughput (214 versus 139 docs/min), making it better for bulk ingestion tasks where speed matters more than per-document accuracy.
Full data below. See the GPU comparisons hub for more.
Specs Comparison
Both models share 16K context windows and nearly identical VRAM footprints, making them interchangeable from a hardware perspective.
| Specification | CodeLlama | DeepSeek Coder |
|---|---|---|
| Parameters | 34B | 33B |
| Architecture | Dense Transformer | Dense Transformer |
| Context Length | 16K | 16K |
| VRAM (FP16) | 68 GB | 66 GB |
| VRAM (INT4) | 20 GB | 19 GB |
| Licence | Meta Community | MIT |
Guides: CodeLlama VRAM requirements and DeepSeek Coder VRAM requirements.
Document Processing Benchmark
Tested on an NVIDIA RTX 3090 with vLLM, INT4 quantisation, and continuous batching. Documents included API documentation, technical specifications, and code-heavy README files. See our tokens-per-second benchmark.
| Model (INT4) | Chunk Throughput (docs/min) | Retrieval Accuracy | Context Utilisation | VRAM Used |
|---|---|---|---|---|
| CodeLlama | 214 | 84.0% | 92.3% | 20 GB |
| DeepSeek Coder | 139 | 90.1% | 85.1% | 19 GB |
An interesting split: CodeLlama achieves higher context utilisation (92.3% versus 85.1%), meaning it extracts more from whatever it retrieves, while DeepSeek Coder retrieves more accurately in the first place. For most RAG systems, retrieval accuracy is the higher-leverage metric. Consult our best GPU for LLM inference guide.
See also: CodeLlama vs DeepSeek Coder for Chatbot / Conversational AI for a related comparison.
See also: DeepSeek 7B vs Qwen 2.5 7B for Multilingual Chat for a related comparison.
Cost Analysis
Near-identical hardware requirements mean cost efficiency is driven purely by throughput and your quality requirements.
| Cost Factor | CodeLlama | DeepSeek Coder |
|---|---|---|
| GPU Required (INT4) | RTX 3090 (24 GB) | RTX 3090 (24 GB) |
| VRAM Used | 20 GB | 19 GB |
| Est. Monthly Server Cost | £124 | £98 |
| Throughput Advantage | 0% faster | 4% cheaper/tok |
See our cost-per-million-tokens calculator.
Recommendation
Choose DeepSeek Coder if retrieval accuracy on technical documents is your primary concern. Its 6-point accuracy lead means fewer incorrect answers surfaced to users, which is critical for developer documentation search and code-aware knowledge bases.
Choose CodeLlama if you are building a high-volume document ingestion pipeline where throughput matters more than per-document accuracy — for example, bulk indexing of open-source repositories.
Deploy on dedicated GPU hosting for production RAG pipelines.
Deploy the Winner
Run CodeLlama or DeepSeek Coder on bare-metal GPU servers with full root access, no shared resources, and no token limits.
Browse GPU Servers
