Allocate32B class GPU requirements
What it takes to run 32B class (Qwen, DeepSeek distills) locally: memory by quantization, the smallest GPU that fits, and the managed alternative.
32B class serves up to 256,000 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 32B class 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 32B class?
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 32B class'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 32B class under?
The catalog does not list a license for this model. Check the lab’s model card for the exact terms before commercial deployment.
Is there an alternative to buying GPUs for 32B class?
Yes: managed inference. Allocate serves comparable models token-metered inside your own boundary, and if you fine-tune an open base on your data, the weights belong to you.
More free tools
Allocate serves open-weight models like 32B class token-metered inside your own boundary. No hardware to buy, and if you fine-tune on your data, the weights are yours.