LLM
How to deploy Meta-Llama-3.1-70B-Instruct-FP8 on a GPU cloud
A 70.6B language model for chat and instruction-following. Full specs, license and use cases.
Meta-Llama-3.1-70B-Instruct-FP8 size and hardware requirements
70.6B
Total parameters
Dense (no MoE)
Architecture
F8_E4M3
Published precision
78.8 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP8 (native) | 65.7 GB | 78.8 GB | RTX PRO 6000 | 1 | $1.64/hr |
| INT4 (quantized) | 32.9 GB | 39.4 GB | RTX A6000 | 1 | $0.330/hr |
How to run Meta-Llama-3.1-70B-Instruct-FP8
Run Meta-Llama-3.1-70B-Instruct-FP8 with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve RedHatAI/Meta-Llama-3.1-70B-Instruct-FP8 --tensor-parallel-size 1Deploy Meta-Llama-3.1-70B-Instruct-FP8 on Aquanode
Aquanode has no one-click deploy template for Meta-Llama-3.1-70B-Instruct-FP8; you install the inference engine yourself with the commands below. Aquanode sells GPU pods billed per second, not a hosted inference API.
- Launch a bare GPU pod sized to the requirement above (1× RTX PRO 6000 or larger).
- Open a terminal on the pod, or save one of the commands above as a startup script so it runs automatically the first time the pod boots.
- Run the command and connect to the resulting endpoint.
Launch a GPU pod