Model → hardware
Qwen2.5-Coder 32B
Plan for 24 GB of dedicated GPU memory at Q4_K_M and a 4,000-token context. 32 GB gives more headroom under the same settings.
Larger dense coding 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 |
|---|---|---|---|
| 24 GB | Smallest listed tier that fits22.0 GB + 0.72 GB reserve | Exceeds this budget23.6 GB + reserve | RTX 4090 24 GB |
| 32 GB | More headroom22.0 GB + 0.96 GB reserve | Fits the estimate23.6 GB + reserve | RTX 5090 32 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 24 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 | 20.50 GB | 22.0 GB | 23.6 GB |
| Q5_K_M | 25.00 GB | 26.5 GB | 28.1 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 Coder version is this?
Use Qwen2.5-Coder-32B-Instruct, not the base checkpoint. Qwen provides GGUF files under Apache 2.0. Keep the Instruct chat template and download all shards for the chosen quantisation.
When should I choose the smaller Coder?
Try the 14B Coder if 32B leaves too little memory or responds too slowly. Model size does not establish which writes better code for your repository. Compare the same task and run the generated code’s tests.
Does 128K context fit this hardware?
The publisher describes 131,072-token support with YaRN; the default configuration is 32,768. Our 4K and 16K calculations do not verify the full window. Longer inputs increase cache memory and prompt-processing work.
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 3 October 2026.
These comparisons are planning estimates. No reviewed or independently reproduced measurements are available yet. About test evidence.