AllocateMeta Llama 3 70B Instruct Turbo GPU requirements
What it takes to run Meta Llama 3 70B Instruct Turbo locally: memory by quantization, the smallest GPU that fits, and the managed alternative.
Meta Llama 3 70B Instruct Turbo serves up to 8,192 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: L40S (48 GB)
How it works
Common questions
How much VRAM does Meta Llama 3 70B Instruct Turbo need?
At 8K context: roughly 157 GB at FP16, 78 GB at 8-bit, and 43 GB at 4-bit, including KV cache and runtime overhead. Longer context adds memory linearly.
Can a single GPU run Meta Llama 3 70B Instruct Turbo?
At 4-bit, yes: a L40S (48 GB) or larger handles it at 8K context. At FP16 you need a B200 (192 GB) or larger.
Does quantization hurt Meta Llama 3 70B Instruct Turbo'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 70B Instruct Turbo under?
Llama community. Check the license terms for your use case before deployment.
Is there an alternative to buying GPUs for Meta Llama 3 70B Instruct Turbo?
Yes: managed inference. Allocate serves Meta Llama 3 70B Instruct Turbo token-metered from $0.88 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 70B Instruct Turbo token-metered inside your own boundary. No hardware to buy, and if you fine-tune on your data, the weights are yours.