AllocateMeta Llama 3.1 405B Instruct GPU requirements
What it takes to run Meta Llama 3.1 405B Instruct locally: memory by quantization, the smallest GPU that fits, and the managed alternative.
Meta Llama 3.1 405B Instruct serves up to 4,096 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 Meta Llama 3.1 405B Instruct on Allocate, token-metered.
How it works
Common questions
How much VRAM does Meta Llama 3.1 405B Instruct need?
At 8K context: roughly 895 GB at FP16, 447 GB at 8-bit, and 246 GB at 4-bit, including KV cache and runtime overhead. Longer context adds memory linearly.
Can a single GPU run Meta Llama 3.1 405B Instruct?
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 Meta Llama 3.1 405B 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 Meta Llama 3.1 405B Instruct under?
Llama community. Check the license terms for your use case before deployment.
Is there an alternative to buying GPUs for Meta Llama 3.1 405B Instruct?
Yes: managed inference. Allocate serves Meta Llama 3.1 405B Instruct token-metered from $3.5 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 Meta Llama 3.1 405B Instruct token-metered inside your own boundary. No hardware to buy, and if you fine-tune on your data, the weights are yours.