Qwen2.5-Coder 7B · 11 GPUs measured first-party · llama.cpp Q4_K_M · Updated July 2026

What GPU Do You Need for Qwen2.5-Coder 7B?

Qwen2.5-Coder 7B is the autocomplete tier: small enough to fit an 8GB card (~6GB measured peak), fast enough that completions feel instant: 287 tok/s on the B300, 266 on the H200, and still 38 tok/s on a seven-year-old T4. We measured it on 11 GPUs with llama.cpp at Q4_K_M, logging power and VRAM on every run.

Benchmarked weights: bartowski/Qwen2.5-Coder-7B-Instruct-GGUF

The right tool for inline completion. Code completion has a latency budget of a few hundred milliseconds. The model has to produce a usable suggestion before you type the next character. That's a speed problem, not an intelligence problem, and a 7B coder model is the honest answer to it. My recommended split for a local stack: this model handles fill-in-middle and autocomplete, while a 30B-class model (ideally the Qwen3 Coder MoE) handles the chat-and-refactor side. On a 24GB card you can run both simultaneously.

Numbers worth noticing. The H200's 266 tok/s at 132W (2.02 tok/W) doubles the efficiency of the chart-topping B300, the recurring Hopper-vs-Blackwell pattern in our small-model data. But the more practical row is further down: 6GB-class cards clear the floor, and the $179 Arc A580 fits it. For a completions model that runs all day next to your IDE, low idle-adjacent power draw matters more than peak throughput. This is a workload where a small efficient card is genuinely the better engineering choice.

Our verdict

Qwen2.5-Coder 7B: 287 tok/s peak, ~6GB floor, instant-feel completions on almost any modern card. Use it as the fast half of a local coding stack, autocomplete here, a 30B-class model for the heavy lifting, and an 8GB card is all the hardware this half needs.

FAQ

Can an 8GB card run Qwen2.5-Coder 7B?

Comfortably, ~6GB measured peak at Q4_K_M leaves context headroom on 8GB. The $179 Intel Arc A580 clears it; so does an RTX 3050.

Is a 7B model good enough for coding?

For inline autocomplete and fill-in-middle, yes, that workload rewards speed over depth. For multi-file refactors and architectural questions, pair it with Qwen3 Coder 30B-A3B or another 30B-class model.

How fast is it in practice?

287 tok/s on the B300, 266 on the H200, but even the old T4's 38 tok/s outruns human reading speed. On any modern consumer card, completions are effectively instantaneous.

Qwen2.5-Coder 7B or the newer Qwen3 8B?

For code, the coder tune wins, fill-in-middle training matters for completions. Qwen3 8B is the better general assistant. Same VRAM class (~6GB), so your card runs either.

What does a full local coding stack look like?

Our suggested pairing: this model for completions plus Qwen3 Coder 30B-A3B for chat/agent work, together they fit in 24GB. On an 8GB card, run the 7B alone and let a cloud model handle the big tasks.