Can I run Qwen 2.5 Coder 7B?

Qwen 2.5 Coder 7B is the model to reach for when you want a local coding assistant rather than a general chatbot. At 7.6B parameters it is built specifically for writing, completing, and explaining code, and that focus is what sets it apart from the all-rounder 7-8B models people usually start with. A 4-bit quant lands around 4.6 GB, so it fits on a 12 GB card like an RTX 3060 with room to spare, runs comfortably in unified memory on an Apple Silicon Mac, and stays within the 8 GB minimum RAM the model needs. If you are squeezed for memory, a 2-bit build drops to about 3.2 GB, though you trade away some accuracy.

Day to day it feels quick enough to use as an inline completion engine. On an RTX 3060 12GB you can expect around 66 tokens per second at 4-bit, and an Apple M Max sits near 76, both fast enough that suggestions appear before you finish reading them; an RTX 4090 pushes that to roughly 186. The 128K context is genuinely useful for feeding it whole files or a small repo, but treat it as a ceiling, not a default. Filling all 128K pushes total memory to about 21 GB, which puts it well past a single 12 GB card, so keep working context to what the task actually needs.

Against the other models on this page, DeepSeek R1 7B is the better pick when you want step-by-step reasoning or chain-of-thought problem solving, since that is what it is tuned for; Qwen 2.5 Coder 7B generally holds the edge on raw code generation and fixing existing functions. Its standout trait is simply being a sharp, dedicated coder at a size that runs on modest hardware. It ships under Apache 2.0, which means you can use it freely, including in commercial and production work, with no provider-specific strings attached, and it pulls cleanly from Ollama as qwen2.5-coder.