Reasoning
How to deploy DeepSeek-R1-Distill-Qwen-14B-abliterated-v2 on a GPU cloud
A 14.8B model tuned to reason step by step before answering. Full specs, license and use cases.
DeepSeek-R1-Distill-Qwen-14B-abliterated-v2 size and hardware requirements
14.8B
Total parameters
Dense (no MoE)
Architecture
BF16
Published precision
33.0 GB
Min VRAM (native)
| Precision | Weight size on disk | Required VRAM | Cheapest live fit | GPUs needed | Est. $/hr (full fit) |
|---|---|---|---|---|---|
| BF16 | 27.5 GB | 33.0 GB | RTX A6000 | 1 | $0.330/hr |
| FP8 (quantized) | 13.8 GB | 16.5 GB | RTX 4000 SFF Ada | 1 | $0.180/hr |
| INT4 (quantized) | 6.9 GB | 8.3 GB | RTX 4070 Super | 1 | $0.110/hr |
How to run DeepSeek-R1-Distill-Qwen-14B-abliterated-v2
Run DeepSeek-R1-Distill-Qwen-14B-abliterated-v2 with vLLM
Generic example, not from the model's own docs: adjust flags (quantization, context length, parallelism) for your setup.
vllm serve huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2 --tensor-parallel-size 1Run DeepSeek-R1-Distill-Qwen-14B-abliterated-v2 with GGUF quantizations
Prebuilt GGUF weights published at mradermacher/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2-GGUF. Run with llama.cpp's llama-server or load the repo directly in LM Studio.
llama-server -hf mradermacher/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2-GGUFSource: https://huggingface.co/mradermacher/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2-GGUF
Deploy DeepSeek-R1-Distill-Qwen-14B-abliterated-v2 on Aquanode
Aquanode has no one-click deploy template for DeepSeek-R1-Distill-Qwen-14B-abliterated-v2; 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 A6000 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