What GPU do I need to run RedHatAI/Meta-Llama-3.1-8B-Instruct-FP8?
8.0B parameters, published in F8_E4M3. View on Hugging Face
Set up Meta-Llama-…-8B-Instruct-FP8Meta-Llama-3.1-8B-Instruct-FP8 is published by RedHatAI on Hugging Face, with 174,844 downloads and 44 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.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| FP8 (native) | 7.5 GB | 9.0 GB | RTX 5060 Ti | 1 | $0.110/hr |
| INT4 (quantized) | 3.7 GB | 4.5 GB | RTX 5060 Ti | 1 | $0.110/hr |
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 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-3.1-8B-Instruct-FP8 at its published (F8_E4M3) precision: 1× RTX 5060 Ti, at $0.110/hr per GPU ($0.110/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Meta-Llama-3.1-8B-Instruct-FP8: common questions
Does Meta-Llama-3.1-8B-Instruct-FP8 fit on a 12 GB GPU?
Yes. At FP8 (native) it needs 9.0 GB of VRAM, so a 12 GB card holds it with 3.0 GB to spare. An 8 GB card is not enough for it at FP8 (native).
Is Meta-Llama-3.1-8B-Instruct-FP8 already quantized?
Yes. It is published in FP8, one byte per parameter, so the 9.0 GB figure is already a quantized footprint rather than a full-precision one. Only INT4 goes below it, at 4.5 GB. A GPU without FP8 tensor cores cannot run it as published, which is why cards here are matched on precision support and not on VRAM alone.
What is the least VRAM Meta-Llama-3.1-8B-Instruct-FP8 can run in?
4.5 GB, at INT4 (quantized), which fits a 6 GB card, against 9.0 GB at FP8 (native). That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Weight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Llama 3.1 models
- Llama-3.1-8B-Instruct (8.0B, BF16)
- Llama-3.1-8B (8.0B, BF16)
- Meta-Llama-3.1-8B-Instruct (8.0B, BF16)
- Meta-Llama-3.1-8B-Instruct (8.0B, BF16)
- Llama-3.1-Nemotron-Nano-8B-v1 (8.0B, BF16)
Related reading: H100 pricing and specs, The best GPUs for AI, ranked, and Best GPU for LLM inference.
