Best GPU for Code Llama Hosting
Code Llama is the original code-specialised LLM, derived from Llama 2 by Meta. The 13B variant is the production sweet spot — 26 GB FP16, fits comfortably on a 24 GB+ card with INT8 or INT4.
The short answer: the RTX 3090 is the best GPU for self-hosting Code Llama 13B on a dedicated server. It has the right VRAM (24 GB) for the model, modern tensor cores, and the best cost-per-token in our catalogue for this workload.
Ranking — Best to Worst for This Workload
From best to worst for this specific workload, with the reason in plain English.
RTX 3090 Top Pick
24 GB needs FP8 or INT8 for 13B. Cheapest practical card.
24 GB · Ampere · from £179/mo
RTX 5090 Best Quality
32 GB fits Code Llama 13B FP16 with comfortable context. Plus headroom.
32 GB · Blackwell · from £359/mo
RTX 6000 Pro 96 GB Code Llama 34B
96 GB runs Code Llama 34B FP16 single-card.
96 GB · Blackwell · from £1099/mo
RTX 5080 INT4 Pick
16 GB needs INT4 for 13B. Workable for low-volume.
16 GB · Blackwell · from £189/mo
RTX 4060 7B Only
8 GB fits Code Llama 7B INT4 only.
8 GB · Ada Lovelace · from £109/mo
Background & Sizing
Code Llama (Meta) and DeepSeek-Coder are the two open-weight code-specialised LLMs most teams pick. Code Llama has wider tooling support and longer history; DeepSeek-Coder is generally smaller and stronger per parameter.
For new deployments we usually recommend evaluating DeepSeek-Coder 6.7B first — it tends to outperform Code Llama 13B on most coding benchmarks while needing less VRAM. Code Llama remains relevant for IDE integrations and tooling that explicitly target the model family.
Frequently Asked Questions
The questions buyers actually ask before committing to a GPU server.
Code Llama vs DeepSeek-Coder?
DeepSeek-Coder 6.7B usually beats Code Llama 13B on benchmarks at half the VRAM. Use Code Llama for ecosystem compatibility, DeepSeek for raw quality.
Code Llama 34B — worth the hardware?
Marginally better than 13B on most tasks. The jump from 13B to 34B is smaller than the jump in hardware cost.
Tool use / function calling?
Limited — Code Llama is older and not natively tool-trained. Wrap it in a structured-output harness if you need that.
How fast is it on a 5090?
Code Llama 13B FP16 — about 80 tok/s single-stream on a 5090, ~600 tok/s aggregate.
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