Allocate400B MoE class GPU requirements
What it takes to run 400B MoE class (Qwen 3.5, Llama 4) locally: memory by quantization, the smallest GPU that fits, and the managed alternative.
400B MoE class serves up to 262,144 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: Mac M3 Ultra (512 GB unified)
Or skip the hardware: run 400B MoE class on Allocate, token-metered.
How it works
Common questions
How much VRAM does 400B MoE class need?
At 8K context: roughly 884 GB at FP16, 442 GB at 8-bit, and 243 GB at 4-bit, including KV cache and runtime overhead. Longer context adds memory linearly.
Can a single GPU run 400B MoE class?
At 4-bit, yes: a Mac M3 Ultra (512 GB unified) or larger handles it at 8K context. At FP16 you need multiple devices.
Why does 400B MoE class need so much memory as a MoE model?
Only some experts activate per token, which sets speed, but all 400 billion parameters must sit in memory. MoE trades memory for throughput.
What license is 400B MoE 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 400B MoE 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 400B MoE class token-metered inside your own boundary. No hardware to buy, and if you fine-tune on your data, the weights are yours.