Model → hardware
Qwen2.5-Coder 14B
Plan for 12 GB of dedicated GPU memory at Q4_K_M and a 4,000-token context. 16 GB gives more headroom under the same settings.
Coding with a larger model. Memory fit is an estimate, not a speed or quality guarantee.
Hardware starting points
Planning estimatesQ4_K_M · 32 GB system RAM · one active request · full GPU residency
| Dedicated GPU memory | At 4,000 tokens | At 16,000 tokens | Example from these guides |
|---|---|---|---|
| 12 GB | Smallest listed tier that fits10.2 GB + 0.36 GB reserve | Fits the estimate11.3 GB + reserve | RTX 3060 12 GB |
| 16 GB | More headroom10.2 GB + 0.48 GB reserve | Fits the estimate11.3 GB + reserve | RTX 4060 Ti 16 GB |
The smallest tier fits the calculation; it is not a verified minimum. Cache precision and runtime buffers can change the fit. “More headroom” means at least 2 GB remains after the estimate and reserve.
Use the publisher’s Instruct / chat template in a current GGUF-capable runtime. The 12 GB tier can become tight when display use, buffers or context grow. CPU offloading needs adequate system RAM and changes performance; it is not included in these full-GPU estimates.
Compare quantisation and context
| File profile | Weight input¹ | GPU estimate at 4K | GPU estimate at 16K |
|---|---|---|---|
| Q4_K_M | 8.99 GB | 10.2 GB | 11.3 GB |
| Q5_K_M | 10.51 GB | 11.8 GB | 12.8 GB |
¹ Catalogue planning weights. These can differ from an exact downloaded file; runtime memory also includes cache and buffers.
Keep the exact file, runtime version, cache type and context limit consistent when comparing. Q5 uses more memory; compare its answers on your own tasks before accepting the trade-off.
Setup and common questions
Which checkpoint should I use?
Choose Qwen/Qwen2.5-Coder-14B-Instruct for conversational coding. The base model is a different checkpoint. Use the publisher’s GGUF files and the correct Instruct chat template.
Can I use longer context?
Qwen advertises up to 131,072 tokens, while the standard configuration starts at 32,768. Longer input needs appropriate scaling, runtime support and sufficient cache memory. See context length.
How should I check its output?
Review generated code, run tests and check dependencies. Compare the 7B version on the same task if memory is tight. The publisher lists Apache 2.0 licensing; check the licence for your intended use.
How are these recommendations chosen?
We compare dedicated GPU memory tiers using the main Finder’s memory calculation at the stated quantisation and context. These tiers describe memory fit, not speed or output quality.
GPU memory includes weights, context cache and a 0.9 GB runtime allowance, plus a 3% reserve (0.15–1.5 GB). Cache format and runtime buffers can change the fit. See the calculation assumptions.
Sources and related guides
Specification sources checked 2 October 2026.
These comparisons are planning estimates. No reviewed or independently reproduced measurements are available yet. About test evidence.