- Developers & Engineers: Build open-source multimodal assistants, code tools, and long-context pipelines.
- Researchers & Academics: Investigate novel MoE architectures, MLA, MTP, and reinforcement learning fine-tuning.
- Enterprises: Deploy via Azure Foundry, Hugging Face, or DeepSeek API with token-based pricing.
- Content & Data Teams: Create code, summaries, mathematical reasoning, and long-form insights at scale.
- Open-Source Community: Access freely under MIT license; weights run locally or — via FP8 — on consumer hardware including Mac Studio equipped with M3 Ultra.
How to Use DeepSeek V3?
- Get the Model: Available on Hugging Face or through Azure AI Foundry catalog.
- Incorporate Into Projects: Use with OpenAI-compatible API, local frameworks like SGLang or LMDeploy; deploy with FP8 or BF16 precision.
- Send Prompts: Submit up to 128K tokens; includes base and chat-tuned variants.
- Fine-Tune & Use: Support via Supervised Fine-Tuning and Reinforcement Learning distillation from R1 improves reasoning clarity.
- Monitor Metrics: Use benchmark insights and use token-based pricing ($0.0011–$0.0056 input; $0.0046–$0.011 output depending on cache and region).
- Massive MoE Architecture: Runs with 37B activated params per token—high efficiency.
- Innovative Training: Uses Multi-Head Latent Attention and Multi-Token Prediction to speed up inference; trained on 14.8T tokens in FP8 over 2.78M GPU hours.
- Benchmark Leadership: Scores
87–89% on MMLU,90% on Math-500,65% HumanEval,75% MBPP; rivals GPT-4o and Claude 3.5. - Cost-Efficient Training & Inference: Developed for
$5.6M and offers competitive edge pricing; inference at60 TPS, runs even on Mac hardware. - Fully Open-Source: MIT license, open weights, API, and technical details available—including local deployment options.
- Frontier-level performance rivaling closed models
- Efficient inference and cost-effective training
- 128K-token context supports long-document tasks
- Open-source and MIT-licensed for flexibility
- Consumer hardware support expands accessibility
- Large model size—needs hardware sophistication or FP8 tooling
- Cache-hit pricing variations globally may complicate planning
- Early iterations may need calibration for production consistency
API
$0.14/$0.28 per 1M tokens
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Editorial Note
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