AllocateCogito v2.1 671B GPU requirements
What it takes to run Cogito v2.1 671B locally: memory by quantization, the smallest GPU that fits, and the managed alternative.
Cogito v2.1 671B serves up to 163,840 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 Cogito v2.1 671B on Allocate, token-metered.
How it works
Common questions
How much VRAM does Cogito v2.1 671B need?
At 8K context: roughly 1481 GB at FP16, 740 GB at 8-bit, and 407 GB at 4-bit, including KV cache and runtime overhead. Longer context adds memory linearly.
Can a single GPU run Cogito v2.1 671B?
At 4-bit, yes: a Mac M3 Ultra (512 GB unified) or larger handles it at 8K context. At FP16 you need multiple devices.
Does quantization hurt Cogito v2.1 671B'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 Cogito v2.1 671B 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 Cogito v2.1 671B?
Yes: managed inference. Allocate serves Cogito v2.1 671B token-metered from $1.25 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 Cogito v2.1 671B token-metered inside your own boundary. No hardware to buy, and if you fine-tune on your data, the weights are yours.