Alibaba Cloud
32.5 Billion Parameters (Dense GQA)
Alibaba Qwen 2.5 Coder 32B Instruct VRAM Requirements Guide
World #1 open-weights coding model. Rivals Claude 3.5 Sonnet on coding benchmarks while fitting on a single 24GB consumer GPU.
4-Bit (Q4_K_M) - Recommended
21.64 GB
>98% perplexity retention. Standard Ollama/llama.cpp quantization.
8-Bit (Q8_0) - Near Lossless
37.62 GB
Virtually identical to FP16 (~99.9% fidelity). Demands high-capacity VRAM.
16-Bit (FP16 / BF16) - Full
69.80 GB
Uncompressed weights. Requires multi-GPU clusters or datacenter cards.
Recommended Hardware Sweet Spot
NVIDIA RTX 4090 (24GB) or RTX 3090 (24GB)
Provides sufficient headroom for weights, 8k+ context KV-cache, and CUDA runtime buffers without Out-Of-Memory (OOM) crashes.
Run Alibaba Qwen 2.5 Coder 32B Instruct Locally
Ollama:
ollama run qwen2.5-coder:32b-instruct-q4_K_MvLLM:
vllm serve Qwen/Qwen2.5-Coder-32B-Instruct --max-model-len 8192 --gpu-memory-utilization 0.95Test Custom Hardware & Context Windows
Simulate your exact GPU, calculate token throughput speeds, or compare cloud rental break-even costs.
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