Reasoning
How to deploy Dolphin3.0-R1-Mistral-24B on a GPU cloud
A 23.6B model tuned to reason step by step before answering. Full specs, license and use cases.
Dolphin3.0-R1-Mistral-24B size and hardware requirements
23.6B
Total parameters
Dense (no MoE)
Architecture
BF16
Published precision
52.7 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 43.9 GB | 52.7 GB | A100 | 1 | $0.851/hr |
| FP8 (quantized) | 22.0 GB | 26.3 GB | RTX 4080 Super | 1 | $0.383/hr |
| INT4 (quantized) | 11.0 GB | 13.2 GB | RTX A4000 | 1 | $0.113/hr |
How to run Dolphin3.0-R1-Mistral-24B
Run Dolphin3.0-R1-Mistral-24B with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve dphn/Dolphin3.0-R1-Mistral-24B --tensor-parallel-size 1Run Dolphin3.0-R1-Mistral-24B with GGUF quantizations
Prebuilt GGUF weights published at mradermacher/Dolphin3.0-R1-Mistral-24B-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf mradermacher/Dolphin3.0-R1-Mistral-24B-GGUFSource: https://huggingface.co/mradermacher/Dolphin3.0-R1-Mistral-24B-GGUF
Deploy Dolphin3.0-R1-Mistral-24B on Aquanode
Aquanode has no one-click deploy template for Dolphin3.0-R1-Mistral-24B; 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× A100 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