AllocateMeta Llama 3.3 70B Instruct Turbo GPU requirements
What it takes to run Meta Llama 3.3 70B Instruct Turbo locally: memory by quantization, the smallest GPU that fits, and the managed alternative.
Meta Llama 3.3 70B Instruct Turbo serves up to 131,072 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 3.3 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 3.3 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 3.3 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 3.3 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 3.3 70B Instruct Turbo?
Yes: managed inference. Allocate serves Meta Llama 3.3 70B Instruct Turbo token-metered from $1.04 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 3.3 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.