Can the RTX 5090 Run Code Llama?
Yes — comfortably for the 7B and 13B variants at FP16. Code Llama 34B needs INT4 quantisation to fit the 5090’s 32 GB.
YesThe RTX 5090 (32 GB) runs Code Llama 13B at FP16 with 4 GB of VRAM headroom for KV cache and concurrent batching.
Detailed Breakdown
The RTX 5090 with 32 GB hosts Code Llama 13B at FP16 with comfortable headroom. Here’s the size-by-size breakdown:
- Code Llama 7B FP16 — 14 GB. Trivial fit. Plenty of room for context.
- Code Llama 13B FP16 — 26 GB. Comfortable fit with ~6 GB KV cache room.
- Code Llama 34B FP16 — 68 GB needed. Doesn’t fit.
- Code Llama 34B INT4 (AWQ) — 20 GB. Fits with comfortable headroom.
- Code Llama 70B — 140 GB FP16. Multi-GPU only.
For new code-completion deployments, also consider DeepSeek-Coder 6.7B — typically stronger per parameter than Code Llama 13B.
Frequently Asked Questions
The questions buyers actually ask before committing to a GPU server.
Throughput on a 5090?
Code Llama 13B FP16 — about 80 tok/s single-stream, ~600 tok/s aggregate.
Code Llama 34B INT4 vs Code Llama 13B FP16?
34B INT4 is generally higher quality but slower. 13B FP16 is the sweet spot for IDE integrations.
Tool use / function calling?
Limited — Code Llama is older and not natively tool-trained.
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