DeepSeek Open-Source & Self-Hosting: Open Weights Guide
Master DeepSeek V4 self-hosting with open weights from HuggingFace. When to self-host vs use the API, vLLM deployment, quantization strategies, and fine-tuning workflows.
DeepSeek V4 Pro and Flash are fully open-weight — available on HuggingFace under a permissive license. This means you can run the same model locally that powers the API, with full control over inference parameters, quantization, and fine-tuning. For organizations with data sovereignty requirements, predictable workloads, or custom fine-tuning needs, self-hosting can be the right call.
But self-hosting DeepSeek V4 Pro isn't trivial — 1.6T total parameters (49B active via MoE) requires significant GPU resources. The page in this section covers the decision framework for self-hosting vs API and the practical deployment patterns.
Note:
API vs self-host break-even: At $0.435/M input tokens, self-hosting V4 Pro makes sense around 50M+ tokens/month on dedicated GPUs. Below that, the API is cheaper than electricity. Flash at $0.14/M is almost never cheaper to self-host.
What You'll Find Here
Open Weights Workflow
When to self-host vs use the API. vLLM deployment patterns for V4 Pro and Flash. Quantization strategies (GGUF, AWQ, GPTQ). Fine-tuning workflows with LoRA/QLoRA. Prompting considerations for locally-deployed models vs the API.
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