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.
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?
Varies. Check the license terms for your use case before deployment.
Is there an alternative to buying GPUs for 32B class?
Yes: managed inference. Allocate serves 32B class token-metered inside your own boundary, and if you fine-tune it on your data, the weights belong to you. Idle time costs nothing.
More free tools
Allocate serves 32B class token-metered inside your own boundary. No hardware to buy, and if you fine-tune it on your data, the weights are yours.