Qwen2.5-Coder-32B locally: a private Copilot on a 24GB card

If you want a coding assistant that never sends your code anywhere, Qwen2.5-Coder-32B is still one of the best ways to get there on hardware you can actually own. It's a year old now, but for the specific job of a private, capable Copilot on a single 24GB card, it holds up — and the maths is friendly.

What Qwen2.5-Coder-32B actually is

From the Qwen2.5-Coder-32B model card (read 26 Sep 2026): 32.5B parameters (31.0B non-embedding), a 32K native context — the figure our VRAM engine uses — extendable to 128K with YaRN per the card, Apache-2.0, released November 2024. It's a dense model specialised for code. (Our VRAM engine sizes the full model at about 32.8B, the figure the pages below use.)

The long context is the part that makes it genuinely useful for real projects — 32K natively, and up to 128K with YaRN scaling — enough to hold a large file, or several, in view at once. That, plus a fully open licence and the fact that it fits one consumer card, is why it became the default "local Copilot" model and why people still run it.

How much VRAM it needs

Computed by our VRAM engine at 16K context (full table on cost to run Qwen2.5-Coder-32B):

At ~23.3 GB, Q4_K_M is a genuine but tight fit on a 24GB card — it works, with little room to spare. Keep the working context modest (16K is plenty for most coding) to stay inside 24GB; the full 128K (YaRN-extended) context, or Q5+, wants a 32GB card or some offload. This is the definition of a 24GB model.

For coding, the context budget usually matters more than squeezing out the last of the quality, so the practical setup is Q4_K_M with a working context sized to your task: 16K covers most single-file and small-project work inside 24GB, and you reach for the extended 128K context only when feeding it a large codebase, at which point a 32GB card or some offload earns its keep. Q4 also keeps the model responsive enough to feel like a real assistant you type alongside rather than a batch job. And the whole point is privacy — a local coder never sends a line of your code anywhere, which for proprietary or client work is the difference between usable and forbidden.

Which GPU runs it

A 24GB card — an RTX 3090 or 4090 — at Q4_K_M is the private-Copilot setup. For long-context work or higher quality, a 32GB card gives breathing room. See best GPU for Qwen2.5-Coder-32B, and our dedicated best GPU for AI coding locally guide.

Referral link

Rent a 24GB card for your local Copilot

A 24GB RTX 3090 was listing from about $0.139/hr on Vast.ai's spot market (26 Sep 2026) — enough to run Qwen2.5-Coder-32B at Q4. Prices move; check the live figure before you rent.

See live 24GB GPU pricesCompare live prices first →

Referral link — we may earn a commission at no cost to you. Prices on our compare page are the provider's live figures, cheapest first; this never changes the ranking.

How to run it (and wire it into your editor)

ollama run qwen2.5-coder:32b       # name :32b — the bare tag pulls a smaller default

Name the size explicitly: the qwen2.5-coder library has sizes from 0.5B up, so the bare tag resolves to a smaller default, not the 32B. Once it's running under Ollama, point your editor's local-model integration at it — our Qwen3-Coder in VS Code walkthrough covers the same wiring for the newer model, and it applies here too.

Who should skip it (and the newer option)

Skip Qwen2.5-Coder-32B if you want the newest and fastest: Qwen3-Coder is the current generation, and its 30B-A3B mixture-of-experts build activates only ~3B parameters, so it's quicker on the same class of card. Skip it if you only have a 12–16GB GPU, where a smaller coder (the 7B or 14B) is the honest fit. Keep Qwen2.5-Coder-32B on your list if you specifically want a proven, dense 32B coder on a single 24GB card — it remains a strong, private Copilot. For the head-to-head with a cloud assistant, see Qwen3-Coder vs GitHub Copilot.

For the current generation, see is Qwen3-Coder worth it and how to run Qwen3-Coder in VS Code.