What GPU do I need to run huihui-ai/DeepSeek-R1-Distill-Qwen-14B-abliterated-v2?
A 14.8B model tuned to reason step by step before answering. 14.8B parameters, published in BF16. View on Hugging Face
DeepSeek-R1-Distill-Qwen-14B-abliterated-v2 is published by huihui-ai on Hugging Face, with 831 downloads and 165 likes to date. It's a Qwen2ForCausalLM model built for text-generation, published natively in BF16.
What DeepSeek-R1-Distill-Qwen-14B-abliterated-v2 is
DeepSeek-R1-Distill-Qwen-14B-abliterated-v2 is a 14.8B-parameter language model published by huihui-ai (community) on Hugging Face. It works through a problem step by step before answering, rather than responding directly. It is released under Not stated.
License note: no license tag published on the model's Hugging Face card; check the repo directly before any commercial use. Facts in this section are sourced from DeepSeek-R1-Distill-Qwen-14B-abliterated-v2's Hugging Face model card, not benchmarked by Aquanode.
What it's used for
- Multi-step reasoning and math
- Code generation
- Agentic tool use
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 | 27.5 GB | 33.0 GB | RTX A6000 | 1 | $0.363/hr |
| cheaper alt. | RTX 5060 Ti | 3 | $0.330/hr | ||
| FP8 (quantized) | 13.8 GB | 16.5 GB | RTX 4000 SFF Ada | 1 | $0.198/hr |
| INT4 (quantized) | 6.9 GB | 8.3 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 DeepSeek-R1-Distill-Qwen-14B-abliterated-v2 at its published (BF16) precision: 1× RTX A6000, at $0.363/hr per GPU ($0.363/hr total). Quantizing to FP8 or INT4 (rows above) can cost less, but requires a compatible quantized checkpoint to exist for this model.
DeepSeek-R1-Distill-Qwen-14B-abliterated-v2: common questions
Can DeepSeek-R1-Distill-Qwen-14B-abliterated-v2 run on a single GPU?
Yes, but not on a desktop card. At BF16 it needs 33.0 GB of VRAM, more than a single 32 GB desktop card holds. The cheapest live single-card fit is a 48.0 GB RTX A6000 at $0.363/hr.
What is the least VRAM DeepSeek-R1-Distill-Qwen-14B-abliterated-v2 can run in?
8.3 GB, at INT4 (quantized), which fits a 12 GB card, against 33.0 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 DeepSeek-R1-Distill-Qwen-14B-abliterated-v2 lower the GPU bill?
Yes. At BF16 the cheapest live fit is one RTX A6000 at $0.363/hr. At INT4 (quantized) it drops to one RTX 5060 Ti at $0.110/hr, provided a quantized checkpoint exists for it.
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 podWeight-to-VRAM math, the fit rules, and how live prices are normalized: full methodology.
More DeepSeek R1 models
- DeepSeek-R1-Distill-Qwen-14B (14.8B, BF16)
- DeepSeek-R1-Distill-Qwen-7B (7.6B, BF16)
- DeepSeek-R1-Distill-Qwen-32B (32.8B, BF16)
- DeepSeek-R1-Distill-Qwen-32B-FP8-dynamic (32.8B, F8_E4M3)
- DeepSeek-R1-Distill-Qwen-1.5B (1.8B, BF16)
Related reading: RTX A6000 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.
