Local coding LLM on a 24GB GPU: Qwen2.5-Coder 32B vs Qwen3-Coder 30B-A3B (it's all about context)

If you want a local coding model on an RTX 4090 / 3090 / 7900 XTX, the question isn't only "does it load", it's "does it load with enough context to read my files".

Qwen2.5-Coder 32B (dense)

So the dense 32B is great for chat-length prompts. AWQ via vLLM gives you comfortable 8K (Linux / WSL2 only).

Qwen3-Coder 30B-A3B (MoE)

Only ~3B active parameters per token, so its bandwidth ceiling is far higher than a dense 32B.

Note: the site has no measured run of it and runs no coding benchmarks, so this is about fit, not code quality.

Want even longer context?

North Mini Code 1.0 30B-A3B GGUF Q4_K_M: ~20.8 GB at 32K, ~23.5 GB at 128K (tight). Only 13 of its 49 layers keep a growing cache.

Quick start

# dense 32B, default 4K window
ollama pull qwen2.5-coder:32b

# AWQ, 8K comfortable
vllm serve Qwen/Qwen2.5-Coder-32B-Instruct-AWQ --max-model-len 8192 --host 127.0.0.1 --port 8000

TL;DR

• Short prompts, best dense coder: Qwen2.5-Coder 32B AWQ.
• Whole-file / repo context on one card: an A3B MoE coder.

Full guide with GPU checks and troubleshooting: https://quantized.uk/cookbook/qwen-coder-32b-single-4090/

Model page with all quants: https://quantized.uk/quant-hub/qwen2.5-coder-32b/