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DeepSeek V3.2 is an open-weights large language model released by deepseek-ai and hosted on Hugging Face. It belongs to the DeepSeek V3 family, which uses a 671B-parameter Mixture-of-Experts architecture with ~37B active parameters per token and a 128K-token context window. It is designed for text generation, reasoning, coding, and instruction-following tasks. Users should check the Hugging Face model card for the definitive V3.2-specific changelog and benchmarks.
The model weights are freely downloadable from Hugging Face under the license published on the model card. There are no per-token fees when you self-host, but you are responsible for compute costs — typically $16–$24/hr for an 8×H100 cloud cluster, or roughly $0.10–$0.30 per million tokens at moderate throughput. Third-party API providers hosting DeepSeek checkpoints generally charge $0.27–$1.10 per million tokens.
You can load it using the Hugging Face Transformers library or serve it through high-throughput engines such as vLLM, SGLang, or TGI. For lower-resource environments, the community typically publishes quantized variants (GGUF, AWQ, GPTQ) that can run with llama.cpp or similar runtimes on consumer GPUs with 24–48 GB VRAM.
Running the full 671B-parameter model at BF16 precision requires approximately 8× H100 80 GB GPUs (roughly 1.2–1.4 TB of aggregate GPU memory to hold the full MoE weights). Quantized community builds (4-bit GPTQ/AWQ) can reduce the requirement to 2–4 high-VRAM GPUs, and GGUF quantizations can run on high-end consumer setups with 48+ GB system RAM, though with reduced throughput.
The DeepSeek V3 family scores in the 87–88% range on MMLU, mid-60s on HumanEval, and ~60% on MATH, placing it in the same tier as GPT-4-class systems on key reasoning and coding benchmarks. Closed models from OpenAI, Anthropic, and Google still tend to lead on agentic, multimodal, and safety-tuned tasks, but DeepSeek offers transparency, self-hosting, and a roughly 10–50× cost advantage per token when self-hosted at scale.
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Last verified March 2026