AllocateQwen 2.5 Coder 32B Instruct GPU requirements
What it takes to run Qwen 2.5 Coder 32B Instruct locally: memory by quantization, the smallest GPU that fits, and the managed alternative.
Qwen 2.5 Coder 32B Instruct serves up to 16,384 tokens of context; the KV cache grows linearly toward that ceiling, so the slider below shows exactly what longer context costs in memory.
Smallest single device that fits: RTX 4090 (24 GB)
How it works
Common questions
How much VRAM does Qwen 2.5 Coder 32B Instruct need?
At 8K context: roughly 73 GB at FP16, 36 GB at 8-bit, and 20 GB at 4-bit, including KV cache and runtime overhead. Longer context adds memory linearly.
Can a single GPU run Qwen 2.5 Coder 32B Instruct?
At 4-bit, yes: a RTX 4090 (24 GB) or larger handles it at 8K context. At FP16 you need a A100 (80 GB) or larger.
Does quantization hurt Qwen 2.5 Coder 32B Instruct's quality?
Modern 4-bit quantization costs a small amount of quality for a 4x memory saving; 8-bit is near-lossless. Validate on your own tasks before production.
What license is Qwen 2.5 Coder 32B Instruct under?
Qwen license. Check the license terms for your use case before deployment.
Is there an alternative to buying GPUs for Qwen 2.5 Coder 32B Instruct?
Yes: managed inference. Allocate serves Qwen 2.5 Coder 32B Instruct token-metered from $0.8 per million input tokens at provider list price, plus the 7% transaction fee. Idle time costs nothing.
More free tools
Allocate serves open-weight models like Qwen 2.5 Coder 32B Instruct token-metered inside your own boundary. No hardware to buy, and if you fine-tune on your data, the weights are yours.