What GPU do I need to run NousResearch/Hermes-3-Llama-3.1-70B?
A 70.6B language model for chat and instruction-following. 70.6B parameters, published in BF16. View on Hugging Face
Hermes-3-Llama-3.1-70B is published by NousResearch on Hugging Face, with 3,129 downloads and 138 likes to date. It's a LlamaForCausalLM model built for text-generation, published natively in BF16.
What Hermes-3-Llama-3.1-70B is
Hermes-3-Llama-3.1-70B is a 70.6B-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-3.1-70B'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 times a flat 1.2 overhead for activations and fragmentation. The KV-cache grows with context and is not in that factor; it is listed per model below where the architecture is published. Full formula and assumptions: methodology.
| Precision | Weight size | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 131.4 GB | 157.7 GB | AMD MI300X | 1 | $2.63/hr |
| cheaper alt. | RTX A5000 | 7 | $1.23/hr | ||
| FP8 (quantized) | 65.7 GB | 78.8 GB | RTX PRO 6000 | 1 | $1.32/hr |
| cheaper alt. | RTX 4000 SFF Ada | 4 | $0.792/hr | ||
| INT4 (quantized) | 32.9 GB | 39.4 GB | RTX A6000 | 1 | $0.363/hr |
| cheaper alt. | RTX A5000 | 2 | $0.352/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-3.1-70B at its published (BF16) precision: 1× AMD MI300X, at $2.63/hr per GPU ($2.63/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-3.1-70B: KV cache by context length
The KV cache is the memory the attention layers hold for every token of context, on top of the weights. It is not part of the flat 1.2x overhead in the table above, grows with context length and with every concurrent request, and is why a long-context deployment needs more VRAM than the table shows.
Grouped-query attention: every layer caches keys and values for a few shared KV heads. Cached per token: 327,680 bytes at 16-bit.
| Context | KV cache, one sequence |
|---|---|
| 4K | 1.3 GB |
| 32K | 10.0 GB |
| 128K | 40.0 GB |
Computed from the layer, head and window counts in the model's own configuration (NousResearch/Hermes-3-Llama-3.1-70B), at 16 bits per cached value. Each concurrent sequence needs its own cache, on top of the weights.
Hermes-3-Llama-3.1-70B: common questions
Can Hermes-3-Llama-3.1-70B run on a single GPU?
Yes, but not on a desktop card. At BF16 it needs 157.7 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 192.0 GB AMD MI300X at $2.63/hr.
Does quantizing Hermes-3-Llama-3.1-70B lower the GPU bill?
Yes. At BF16 the cheapest live fit is one AMD MI300X at $2.63/hr. At INT4 (quantized) it drops to one RTX A6000 at $0.363/hr, provided a quantized checkpoint exists for it.
How to run Hermes-3-Llama-3.1-70B
Run Hermes-3-Llama-3.1-70B with vLLM
From NousResearch/Hermes-3-Llama-3.1-70B's own deployment docs.
`vllm serve NousResearch/Hermes-3-Llama-3.1-70B`Source: https://huggingface.co/NousResearch/Hermes-3-Llama-3.1-70B/raw/main/README.md
Run Hermes-3-Llama-3.1-70B with GGUF quantizations
Prebuilt GGUF weights published at bartowski/Hermes-3-Llama-3.1-70B-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf bartowski/Hermes-3-Llama-3.1-70B-GGUFSource: https://huggingface.co/bartowski/Hermes-3-Llama-3.1-70B-GGUF
Deploy Hermes-3-Llama-3.1-70B on Aquanode
Aquanode has no one-click deploy template for Hermes-3-Llama-3.1-70B; 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× AMD MI300X 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-3.1-405B-FP8 (405.9B, F8_E4M3)
- Hermes-3-Llama-3.1-8B (8.0B, BF16)
Alternatives at this size
Other models for text-generation within about a third of Hermes-3-Llama-3.1-70B's 70.6B parameters, from other model lines.
- Qwen-72B (72.3B, BF16)
- Llama-3.3-70B-Instruct (70.6B, BF16)
- Kimi-Linear-48B-A3B-Instruct (49.1B, BF16)
- Hunyuan-A13B-Instruct (80.4B, BF16)
- Apertus-70B-Instruct-2509 (70.6B, BF16)
More on Hermes-3-Llama-3.1-70B
Fits on a 192 GB GPU at BF16: every model that fits in 192 GB.
Fits on an 80 GB GPU at FP8: every model that fits in 80 GB.
Fits on a 48 GB GPU at INT4: every model that fits in 48 GB.
Best chat and assistants models: how Hermes-3-Llama-3.1-70B ranks against the rest.
Related reading: AMD MI300X pricing and specs, How much VRAM you need for LLMs, Serving LLMs with vLLM, vLLM vs TensorRT-LLM vs SGLang, and Best GPU for LLM inference.
