What GPU do I need to run NousResearch/Hermes-3-Llama-3.1-8B?
A 8B language model for chat and instruction-following. 8.0B parameters, published in BF16. View on Hugging Face
Hermes-3-Llama-3.1-8B is published by NousResearch on Hugging Face, with 408,561 downloads and 492 likes to date. It's a LlamaForCausalLM model built for text-generation, published natively in BF16.
What Hermes-3-Llama-3.1-8B is
Hermes-3-Llama-3.1-8B is a 8B-parameter language model published by Nous Research on Hugging Face. It is released under llama3.
License note: check the model's own license terms on Hugging Face before commercial use. Facts in this section are sourced from Hermes-3-Llama-3.1-8B'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) |
|---|---|---|---|---|---|
| BF16 | 15.0 GB | 17.9 GB | RTX A5000 | 1 | $0.176/hr |
| FP8 (quantized) | 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 Hermes-3-Llama-3.1-8B at its published (BF16) precision: 1× RTX A5000, at $0.176/hr per GPU ($0.176/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
Hermes-3-Llama-3.1-8B: common questions
Does Hermes-3-Llama-3.1-8B fit on a 24 GB GPU?
Yes. At BF16 it needs 17.9 GB of VRAM, so a 24 GB card holds it with 6.1 GB to spare. A 16 GB card is not enough for it at BF16.
What is the least VRAM Hermes-3-Llama-3.1-8B can run in?
4.5 GB, at INT4 (quantized), which fits a 6 GB card, against 17.9 GB at BF16. That row assumes an INT4 checkpoint has actually been published for this model, so check its Hugging Face page before planning around it.
Does quantizing Hermes-3-Llama-3.1-8B lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX A5000 at $0.176/hr. At FP8 (quantized) it drops to one RTX 5060 Ti at $0.110/hr, provided a quantized checkpoint exists for it.
How to run Hermes-3-Llama-3.1-8B
Run Hermes-3-Llama-3.1-8B with vLLM
From NousResearch/Hermes-3-Llama-3.1-8B's own deployment docs.
`vllm serve NousResearch/Hermes-3-Llama-3.1-8B`Source: https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-8B/raw/main/README.md
Run Hermes-3-Llama-3.1-8B with GGUF quantizations
Prebuilt GGUF weights published at bartowski/Hermes-3-Llama-3.1-8B-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf bartowski/Hermes-3-Llama-3.1-8B-GGUFSource: https://huggingface.co/bartowski/Hermes-3-Llama-3.1-8B-GGUF
Deploy Hermes-3-Llama-3.1-8B on Aquanode
Aquanode has no one-click deploy template for Hermes-3-Llama-3.1-8B; 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 A5000 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 Hermes 3 models
- Hermes-3-Llama-3.1-70B (70.6B, BF16)
- Hermes-3-Llama-3.1-405B-FP8 (405.9B, F8_E4M3)
Related reading: RTX A5000 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.
