Qwen Coder 14B sits between the 7B and 32B variants in capability. On the RTX 5060 Ti 16GB it hosts comfortably at AWQ with decent concurrency on our hosting.
Contents
Fit
| Precision | Weights | KV Cache Room |
|---|---|---|
| FP16 | ~28 GB | Does not fit |
| FP8 | ~14 GB | Tight |
| AWQ INT4 | ~8 GB | ~8 GB – comfortable |
Deployment
python -m vllm.entrypoints.openai.api_server \
--model Qwen/Qwen2.5-Coder-14B-Instruct-AWQ \
--quantization awq \
--max-model-len 32768 \
--gpu-memory-utilization 0.92 \
--enable-prefix-cachingPerformance
| Metric | AWQ |
|---|---|
| Batch 1 decode | ~42 t/s |
| Batch 4 aggregate | ~150 t/s |
| Batch 8 aggregate | ~235 t/s |
| TTFT 1k prompt | ~290 ms |
Comfortable for 8-12 concurrent autocomplete sessions with 32k context.
vs Variants
| Model | HumanEval | Card |
|---|---|---|
| Qwen Coder 7B | ~70 | 5060 Ti comfortable |
| Qwen Coder 14B | ~80 | 5060 Ti AWQ fits |
| Qwen Coder 32B | ~85 | Needs 24 GB+ card |
14B scores meaningfully higher on code benchmarks than 7B. Worth the upgrade for serious coding workloads. For the 32B variant see Qwen Coder 32B deployment.
Mid-Size Coding Model Hosting
Qwen Coder 14B fits 16GB comfortably. UK dedicated hosting.
Order the RTX 5060 Ti 16GBSee also: coding assistant use case, Qwen Coder 7B.
