What GPU do I need to run RedHatAI/Meta-Llama-3.1-70B-Instruct-FP8?
70.6B parameters, published in F8_E4M3. View on Hugging Face
Meta-Llama-3.1-70B-Instruct-FP8 is published by RedHatAI on Hugging Face, with 145,317 downloads and 52 likes to date. It's a LlamaForCausalLM model built for text-generation, published natively in F8_E4M3.
VRAM required & cheapest live GPU fit
Required VRAM = weight size at each precision, plus a fixed overhead for KV-cache, activations, and fragmentation. Full formula and assumptions: methodology.
A GPU is only matched to a row if its hardware supports that precision, and the primary recommendation is always a single-GPU fit when one exists.
INT4 caveat: requires a quantized checkpoint actually published for this model — check its Hugging Face page before relying on this row.
Cheapest way to run Meta-Llama-3.1-70B-Instruct-FP8 at its published (F8_E4M3) precision: 1× RTX PRO 6000 WS on vastai, at $1.16/hr per GPU ($1.16/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More RedHatAI models
- gemma-4-31B-it-FP8-block (31.3B, F8_E4M3)
- Llama-3.2-1B-Instruct-FP8-dynamic (1.5B, F8_E4M3)
- Qwen3-Coder-Next-FP8-dynamic (79.8B, F8_E4M3)
- Llama-3.2-1B-Instruct-FP8 (1.5B, F8_E4M3)
- Meta-Llama-3.1-8B-Instruct-FP8 (8.0B, F8_E4M3)
- DeepSeek-Coder-V2-Lite-Instruct-FP8 (15.7B, F8_E4M3)
