What GPU do I need to run RedHatAI/Meta-Llama-3.1-70B-Instruct-FP8?
A 70.6B language model for chat and instruction-following. 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 143,028 downloads and 52 likes to date. It's a LlamaForCausalLM model built for text-generation, published natively in F8_E4M3.
What Meta-Llama-3.1-70B-Instruct-FP8 is
Meta-Llama-3.1-70B-Instruct-FP8 is a 70.6B-parameter language model published by Red Hat AI on Hugging Face. It is released under Llama 3.1 Community License Agreement.
License note: Meta's own commercial license, not OSI-approved open source; the repo is gated on Hugging Face, so you accept its terms there before downloading weights. Facts in this section are sourced from Meta-Llama-3.1-70B-Instruct-FP8's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Chat assistants
- Instruction following
- Synthetic data generation
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) | 65.7 GB | 78.8 GB | RTX PRO 6000 | 1 | $1.38/hr |
| cheaper alt. | RTX 5060 Ti | 5 | $0.550/hr | ||
| INT4 (quantized) | 32.9 GB | 39.4 GB | RTX A6000 | 1 | $0.363/hr |
| cheaper alt. | RTX 5060 Ti | 3 | $0.330/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-3.1-70B-Instruct-FP8 at its published (F8_E4M3) precision: 1× RTX PRO 6000, at $1.38/hr per GPU ($1.38/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-3.1-70B-Instruct-FP8: common questions
Can Meta-Llama-3.1-70B-Instruct-FP8 run on a single GPU?
Yes, but not on a desktop card. At FP8 (native) it needs 78.8 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 95.0 GB RTX PRO 6000 at $1.38/hr.
Is Meta-Llama-3.1-70B-Instruct-FP8 already quantized?
Yes. It is published in FP8, one byte per parameter, so the 78.8 GB figure is already a quantized footprint rather than a full-precision one. Only INT4 goes below it, at 39.4 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.
Does quantizing Meta-Llama-3.1-70B-Instruct-FP8 lower the GPU bill?
Yes. At FP8 (native) the cheapest live fit is one RTX PRO 6000 at $1.38/hr. At INT4 (quantized) it drops to one RTX A6000 at $0.363/hr, provided a quantized checkpoint exists for it.
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 podWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More Llama 3.1 models
- Llama-3.1-70B-Instruct (70.6B, BF16)
- Llama-3.1-70B-Instruct-FP8 (70.6B, F8_E4M3)
- Llama-3.1-70B-LatamGPT-SFT-1.0 (70.6B, BF16)
- Llama-3.1-70B (70.6B, BF16)
- Meta-Llama-3.1-70B (70.6B, BF16)
Related reading: RTX PRO 6000 pricing and specs, The NVIDIA Inception program, explained, Google Colab alternatives for dedicated GPU access, Free GPU credits for students and researchers, and RunPod volume disk vs. network volume, compared.
