Company Overview
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Categories Support
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Founded 1953
Company Description
GitHub – Deepseek-ai/DeepSeek-V3
We provide DeepSeek-V3, a strong Mixture-of-Experts (MoE) language design with 671B total parameters with 37B activated for each token. To achieve efficient reasoning and economical training, DeepSeek-V3 embraces Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were completely in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free method for load balancing and sets a multi-token prediction training objective for more powerful efficiency. We pre-train DeepSeek-V3 on 14.8 trillion varied and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to totally harness its abilities. Comprehensive evaluations reveal that DeepSeek-V3 outshines other open-source models and achieves performance equivalent to leading closed-source models. Despite its outstanding performance, DeepSeek-V3 needs only 2.788 M H800 GPU hours for its complete training. In addition, its training process is extremely stable. Throughout the whole training process, we did not experience any irrecoverable loss spikes or perform any rollbacks.
2. Model Summary
Architecture: Innovative Load Balancing Strategy and Training Objective
– On top of the efficient architecture of DeepSeek-V2, we leader an auxiliary-loss-free strategy for load balancing, which minimizes the performance destruction that occurs from motivating load balancing.
– We investigate a Multi-Token Prediction (MTP) objective and prove it helpful to model efficiency. It can likewise be used for speculative decoding for reasoning velocity.
Pre-Training: Towards Ultimate Training Efficiency
– We create an FP8 mixed accuracy training structure and, for the first time, confirm the feasibility and efficiency of FP8 training on an extremely large-scale design.
– Through co-design of algorithms, frameworks, and hardware, we conquer the interaction traffic jam in cross-node MoE training, nearly achieving full computation-communication overlap.
This significantly enhances our training effectiveness and minimizes the training expenses, allowing us to further scale up the design size without additional overhead.
– At an economical cost of just 2.664 M H800 GPU hours, we finish the pre-training of DeepSeek-V3 on 14.8 T tokens, producing the presently greatest open-source base design. The subsequent training stages after pre-training need only 0.1 M GPU hours.
Post-Training: Knowledge Distillation from DeepSeek-R1
– We introduce an innovative approach to distill reasoning abilities from the long-Chain-of-Thought (CoT) model, particularly from one of the DeepSeek R1 series designs, into standard LLMs, particularly DeepSeek-V3. Our pipeline elegantly incorporates the verification and reflection patterns of R1 into DeepSeek-V3 and especially improves its thinking efficiency. Meanwhile, we also maintain a control over the output design and length of DeepSeek-V3.
3. Model Downloads
The overall size of DeepSeek-V3 models on Hugging Face is 685B, that includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights. **
To ensure optimal performance and versatility, we have partnered with open-source communities and hardware suppliers to offer multiple ways to run the design locally. For step-by-step assistance, take a look at Section 6: How_to Run_Locally.
For developers looking to dive much deeper, we advise checking out README_WEIGHTS. md for information on the Main Model weights and the Multi-Token Prediction (MTP) Modules. Please note that MTP assistance is presently under active development within the community, and we welcome your contributions and feedback.
4. Evaluation Results
Base Model
Standard Benchmarks
Best results are revealed in bold. Scores with a space not going beyond 0.3 are thought about to be at the very same level. DeepSeek-V3 accomplishes the finest efficiency on many standards, specifically on math and code jobs. For more examination information, please check our paper.
Context Window
Evaluation results on the Needle In A Haystack (NIAH) tests. DeepSeek-V3 carries out well throughout all context window lengths up to 128K.
Chat Model
Standard Benchmarks (Models larger than 67B)
All designs are assessed in a setup that restricts the output length to 8K. Benchmarks consisting of fewer than 1000 samples are evaluated several times utilizing varying temperature level settings to derive robust last results. DeepSeek-V3 stands as the best-performing open-source model, and likewise shows competitive performance versus frontier closed-source designs.
Open Ended Generation Evaluation
English open-ended conversation assessments. For AlpacaEval 2.0, we utilize the length-controlled win rate as the metric.
5. Chat Website & API Platform
You can chat with DeepSeek-V3 on DeepSeek’s official website: chat.deepseek.com
We likewise offer OpenAI-Compatible API at DeepSeek Platform: platform.deepseek.com
6. How to Run Locally
DeepSeek-V3 can be released in your area using the following hardware and open-source neighborhood software application:
DeepSeek-Infer Demo: We offer an easy and lightweight demonstration for FP8 and BF16 inference.
SGLang: Fully support the DeepSeek-V3 model in both BF16 and FP8 reasoning modes, with Multi-Token Prediction coming quickly.
LMDeploy: Enables effective FP8 and BF16 inference for local and cloud implementation.
TensorRT-LLM: Currently supports BF16 inference and INT4/8 quantization, with FP8 assistance coming soon.
vLLM: Support DeepSeek-V3 design with FP8 and BF16 modes for tensor parallelism and pipeline parallelism.
AMD GPU: Enables running the DeepSeek-V3 model on AMD GPUs via SGLang in both BF16 and FP8 modes.
Huawei Ascend NPU: Supports running DeepSeek-V3 on Huawei Ascend gadgets.
Since FP8 training is natively adopted in our structure, we just offer FP8 weights. If you need BF16 weights for experimentation, you can utilize the offered conversion script to perform the change.
Here is an example of converting FP8 weights to BF16:
Hugging Face’s Transformers has not been directly supported yet. **
6.1 Inference with DeepSeek-Infer Demo (example only)
System Requirements
Note
Linux with Python 3.10 only. Mac and Windows are not supported.
Dependencies:
Model Weights & Demo Code Preparation
First, clone our DeepSeek-V3 GitHub repository:
Navigate to the inference folder and install dependences listed in requirements.txt. Easiest method is to use a package manager like conda or uv to develop a new virtual environment and install the dependencies.
Download the model weights from Hugging Face, and put them into/ path/to/DeepSeek-V 3 folder.
Model Weights Conversion
Convert Hugging Face model weights to a particular format:
Run
Then you can chat with DeepSeek-V3:
Or batch reasoning on a provided file:
6.2 Inference with SGLang (suggested)
SGLang presently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, providing modern latency and throughput efficiency amongst open-source structures.
Notably, SGLang v0.4.1 totally supports running DeepSeek-V3 on both NVIDIA and AMD GPUs, making it an extremely versatile and robust option.
SGLang likewise supports multi-node tensor parallelism, allowing you to run this model on several network-connected devices.
Multi-Token Prediction (MTP) is in advancement, and progress can be tracked in the optimization strategy.
Here are the launch instructions from the SGLang group: https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3
6.3 Inference with LMDeploy (suggested)
LMDeploy, a flexible and high-performance inference and serving structure customized for big language designs, now supports DeepSeek-V3. It provides both offline pipeline processing and online implementation abilities, effortlessly integrating with PyTorch-based workflows.
For extensive detailed guidelines on running DeepSeek-V3 with LMDeploy, please describe here: InternLM/lmdeploy # 2960
6.4 Inference with TRT-LLM (advised)
TensorRT-LLM now supports the DeepSeek-V3 model, providing accuracy options such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in development and will be released quickly. You can access the custom-made branch of TRTLLM particularly for DeepSeek-V3 assistance through the following link to experience the new functions straight: https://github.com/NVIDIA/TensorRT-LLM/tree/deepseek/examples/deepseek_v3.
6.5 Inference with vLLM (advised)
vLLM v0.6.6 supports DeepSeek-V3 reasoning for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from standard methods, vLLM uses pipeline parallelism allowing you to run this design on multiple devices linked by networks. For comprehensive assistance, please refer to the vLLM directions. Please feel complimentary to follow the enhancement strategy also.
6.6 Recommended Inference Functionality with AMD GPUs
In cooperation with the AMD group, we have achieved Day-One support for AMD GPUs utilizing SGLang, with complete compatibility for both FP8 and BF16 precision. For in-depth guidance, please describe the SGLang instructions.
6.7 Recommended Inference Functionality with Huawei Ascend NPUs
The MindIE structure from the Huawei Ascend neighborhood has successfully adjusted the BF16 variation of DeepSeek-V3. For detailed assistance on Ascend NPUs, please follow the guidelines here.
7. License
This code repository is licensed under the MIT License. Using DeepSeek-V3 Base/Chat models undergoes the Model License. DeepSeek-V3 series (including Base and Chat) supports business use.