CodeLlama 13B remains a solid coding LLM in 2026 despite newer alternatives. On the RTX 5060 Ti 16GB via our hosting it hosts comfortably at AWQ.
Contents
Fit
- FP16: ~26 GB – does not fit
- FP8: ~13 GB – tight
- AWQ INT4: ~8 GB – comfortable
Deployment
python -m vllm.entrypoints.openai.api_server \
--model TheBloke/CodeLlama-13B-Instruct-AWQ \
--quantization awq \
--max-model-len 16384 \
--gpu-memory-utilization 0.92Performance
- AWQ batch 1 decode: ~52 t/s
- AWQ batch 8 aggregate: ~280 t/s
vs Newer Coding Models
| Model | HumanEval | Licence |
|---|---|---|
| CodeLlama 13B | ~45 | Llama 2 (restrictive) |
| Qwen Coder 7B | ~70 | Qwen Research |
| Qwen Coder 14B | ~80 | Qwen Research |
| StarCoder 2 15B | ~65 | OpenRAIL-M |
| Codestral 22B | ~75 | Mistral non-production |
Qwen Coder 7B surpasses CodeLlama 13B at smaller size – better output quality, lower VRAM, Blackwell FP8 native.
When CodeLlama Still Makes Sense
- Teams with existing Llama-ecosystem fine-tunes
- Specific domain fine-tunes on CodeLlama base
- Meta licence preference for commercial clarity
For new deployments in 2026 prefer Qwen Coder 7B or Qwen Coder 14B.
See also: coding assistant use case.
