Testing Qwen Coder Local Deployment on 24GB GPUs

On November 12, 2026 at 10:54, a developer published a hands-on comparison on DEV Community's AI section titled "Qwen Coder Local Deployment Compared: Context Trade-offs on a 24GB Card." The piece evaluates memory usage and context-length trade-offs when running coding models locally on a single 24GB card, specifically an RTX 4090, RTX 3090, or 7900 XTX.

Two models are put side by side: the dense Qwen2.5-Coder 32B and the MoE-based Qwen3-Coder 30B-A3B. The author's framing is that the real question is not whether a model fits in VRAM, but how much context remains available for reading code files after it is loaded. For the dense Qwen2.5-Coder 32B, at a 4K context the GGUF Q4_K_M quantization needs 21.7 GB, roughly 90% of VRAM, leaving little headroom; the AWQ INT4 variant needs 18.1 GB, or about 75%, and supports an 8K context; the EXL2 3.5bpw quantization sits at 16.0 GB. Deployed through vLLM, the AWQ option gives a comfortable 8K context, though only on Linux or WSL2. The dense 32B model, the author concludes, suits chat-length prompts well.

For the MoE model, Qwen3-Coder 30B-A3B, the published excerpt cuts off right after the heading for the GGUF Q4_K_M row, so its VRAM footprint and achievable context lengths at various quantiles cannot be verified from the available material. The MoE model's numbers are therefore missing.

The event currently rests at this single test. Whether other developers have followed up with additional benchmarks, and what a complete comparison of the two models would conclude, are not covered by the available reports.