AllocateMeta Llama 3.1 8B Instruct Turbo GPU requirements
What it takes to run Meta Llama 3.1 8B Instruct Turbo locally: memory by quantization, the smallest GPU that fits, and the managed alternative.
Meta Llama 3.1 8B 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: RTX 4090 (24 GB)
How it works
Common questions
How much VRAM does Meta Llama 3.1 8B Instruct Turbo need?
At 8K context: roughly 19 GB at FP16, 9 GB at 8-bit, and 5 GB at 4-bit, including KV cache and runtime overhead. Longer context adds memory linearly.
Can a single GPU run Meta Llama 3.1 8B Instruct Turbo?
At 4-bit, yes: a RTX 4090 (24 GB) or larger handles it at 8K context. At FP16 you need a RTX 4090 (24 GB) or larger.
Does quantization hurt Meta Llama 3.1 8B 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 3.1 8B 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 3.1 8B Instruct Turbo?
Yes: managed inference. Allocate serves Meta Llama 3.1 8B Instruct Turbo token-metered from $0.18 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 8B Instruct Turbo token-metered inside your own boundary. No hardware to buy, and if you fine-tune on your data, the weights are yours.