- Developers & Engineers: Build efficient chatbots, reasoning pipelines, and coding assistants with a high context window.
- Researchers & AI Practitioners: Run advanced benchmarks across reasoning, math, code, and multilingual tasks.
- Enterprises & API Users: Deploy via Hugging Face, DeepSeek API, or Bedrock Chat for production-grade integration.
- Data & Analytics Teams: Utilize long-context summarization, Q&A, retrieval, and knowledge extraction.
- Open-Source Community: Self-host or fine-tune via MIT-licensed weights; use dedicated vLLM support for local deployment.
How to Use DeepSeek V2?
- Get the Model: Download base/chat versions (128K context) from Hugging Face or use hosted API endpoints.
- Install Runtime: Use Optimized vLLM inference setup or Transformers integration for efficient use.
- Send Prompts: Provide up to 128K-token inputs in text, code, or reasoning prompts.
- Use Chat Variant: For conversational outputs; includes RL fine-tuning for improved alignment.
- Monitor & Deploy: Tune temperature, use function calling, and scale with enterprise reliability.
- Very Large Context: Supports 128K-token windows via YaRN architecture.
- Efficient MoE Design: 236B total with only 21B active per token—42% lower training cost and 5.76× faster throughput.
- Benchmark Powerhouse: Delivers competitive scores:
78 MMLU,84 Chinese MMLU,79 Math,49 HumanEval on code. - Open-Source with RL & SFT: Offers SFT and RL-tuned chat models with strong alignment and rationale.
- High Throughput Inference: MLA halves KV cache, enabling high-speed, low-memory use in long contexts.
- Massive 128 K context window for long-document handling
- Efficient compute: powerful MoE with smaller active footprint
- Strong benchmark results across language, math, code, and Chinese
- Open-weight model with MIT license and inference optimizations
- Chat/ RL versions for conversational use, hosted and local options
- Requires high VRAM and optimized runtimes for efficiency
- Chat variant slightly trails closed-source best (e.g., GPT‑4)
- Open-source inference performance may lag proprietary systems without tuning
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Editorial Note
This page was researched and written by the ATB Editorial Team. Our team researches each AI tool by reviewing its official website, testing features, exploring real use cases, and considering user feedback. Every page is fact-checked and regularly updated to ensure the information stays accurate, neutral, and useful for our readers.
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