LLM
How to deploy Nous-Hermes-2-Mistral-7B-DPO on a GPU cloud
A 7.2B language model for chat and instruction-following. Full specs, license and use cases.
Nous-Hermes-2-Mistral-7B-DPO size and hardware requirements
7.2B
Total parameters
Dense (no MoE)
Architecture
BF16
Published precision
16.2 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 13.5 GB | 16.2 GB | RTX 3090 | 1 | $0.147/hr |
| FP8 (quantized) | 6.7 GB | 8.1 GB | RTX 4070 Super | 1 | $0.110/hr |
| INT4 (quantized) | 3.4 GB | 4.0 GB | RTX 4070 Super | 1 | $0.110/hr |
How to run Nous-Hermes-2-Mistral-7B-DPO
Run Nous-Hermes-2-Mistral-7B-DPO with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve NousResearch/Nous-Hermes-2-Mistral-7B-DPO --tensor-parallel-size 1Run Nous-Hermes-2-Mistral-7B-DPO with GGUF quantizations
Prebuilt GGUF weights published at mradermacher/Nous-Hermes-2-Mistral-7B-DPO-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf mradermacher/Nous-Hermes-2-Mistral-7B-DPO-GGUFSource: https://huggingface.co/mradermacher/Nous-Hermes-2-Mistral-7B-DPO-GGUF
Deploy Nous-Hermes-2-Mistral-7B-DPO on Aquanode
Aquanode has no one-click deploy template for Nous-Hermes-2-Mistral-7B-DPO; 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 3090 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 pod