Qwen 2.5 Coder 32B Locally: The 20-22GB Q4 Footprint and the GPU Tier You Need

Running Qwen 2.5 Coder 32B locally at Q4_K_M requires roughly 20-22GB of VRAM, which sits at the edge of a 24GB card like the RTX 4090 and comfortably inside the RTX 5090's 32GB, while a 16GB card cannot hold it at usable quantisation. SA buyers can target this with a.

Daily Drop · 05 Jul 2026 · 4 min read · NexaForge ·

Which GPU Runs Qwen 2.5 Coder 32B at Q4?

Running Qwen 2.5 Coder 32B on your own machine is one of the few local setups that genuinely rivals a cloud coding assistant, but only if your graphics card has the memory to hold it. At the Q4_K_M quantisation most people use, the model weights land at roughly 20GB to 25GB of VRAM before you have even loaded a line of code into context. That single number decides which card you need and which ones will not cut it.

Quick Answer

Qwen 2.5 Coder 32B at Q4_K_M needs about 20GB to 25GB of VRAM for the weights, plus headroom for context. It fits a 24GB card like the RTX 4090 with little margin, and sits comfortably inside the RTX 5090's 32GB. A 16GB card cannot hold it at a usable quantisation.

What the VRAM Number Really Means

The Q4_K_M weights are the floor, not the ceiling. Once the model is loaded you still need room for the KV cache, which grows with your context window. Feed it a large file or a long chat history and that cache can add several gigabytes on top. This is why a 24GB card runs the model but leaves you rationing context, while the 32GB on an RTX 5090-class AI machine gives you breathing room for longer prompts and faster, more coherent completions without spilling into system RAM.

Spilling matters because the moment weights or cache overflow onto the CPU and main memory, generation speed collapses. A model that ran at a snappy conversational pace can drop to a crawl. Keeping everything resident on the GPU is the whole point of buying the right tier of card.

The GPU Tier You Actually Need

For South African buyers the practical split is clear. A 16GB card such as a 4060 Ti 16GB or 5060 Ti 16GB is excellent for smaller coding models in the 7B to 14B range, but it physically cannot hold the 32B at a quantisation worth running. Stepping down to a heavier-compressed 32B to force it onto 16GB sacrifices the code quality that made the model attractive.

A 24GB card is the entry point for the full 32B, ideal if you mostly work with focused files and shorter context. If you want long context, room to run other tools alongside it, or simply the fastest experience, the 32GB tier is the comfortable answer. Cards at this level routinely sit near the top of the graphics card best sellers list for exactly this reason. If you want to see what complete machines are selling alongside these cards, the best-selling PCs at Evetech give a useful sense of how buyers are speccing full builds around this GPU tier.

Frequently Asked Questions

Can I run Qwen 2.5 Coder 32B on 16GB of VRAM?

Not at a quantisation worth using. The Q4_K_M weights alone exceed 16GB once context is added. You would be forced into a heavily compressed version that loses the code accuracy the model is known for.

Does the RTX 5090 run it well?

Yes. With 32GB of VRAM the model loads at Q4_K_M with several gigabytes spare for KV cache and a long context window, which keeps generation fast and responses coherent on bigger prompts.

What happens if the model does not fit in VRAM?

It offloads to system RAM and CPU, and generation speed drops sharply. The benefit of a local model is that it stays entirely on the GPU, so avoiding overflow is the goal.

Is a 24GB card enough?

It runs the full 32B at Q4_K_M, but with limited room for long context. For everyday coding on focused files it works well. For long sessions and large context, the 32GB tier is the safer buy.

A 32B coding model rewards the right hardware. Compare in-Rand options in the AI PC and component range at Evetech and match the card to the context you actually work with.

Yes. With 32GB of VRAM the model loads at Q4_K_M with several gigabytes spare for KV cache and a long context window, which keeps generation fast and responses coherent on bigger prompts.

It offloads to system RAM and CPU, and generation speed drops sharply. The benefit of a local model is that it stays entirely on the GPU, so avoiding overflow is the goal.

It runs the full 32B at Q4_K_M, but with limited room for long context. For everyday coding on focused files it works well. For long sessions and large context, the 32GB tier is the safer buy.