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Company Description
GitHub – Deepseek-ai/DeepSeek-V3
We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language design with 671B total parameters with 37B activated for each token. To accomplish effective inference and economical training, DeepSeek-V3 embraces Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were completely validated in DeepSeek-V2. Furthermore, DeepSeek-V3 leaders an auxiliary-loss-free method for load balancing and sets a multi-token forecast training objective for more powerful performance. We pre-train DeepSeek-V3 on 14.8 trillion varied and top quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to totally harness its capabilities. Comprehensive examinations expose that DeepSeek-V3 surpasses other open-source designs and attains performance comparable to leading closed-source models. Despite its excellent efficiency, DeepSeek-V3 needs just 2.788 M H800 GPU hours for its complete training. In addition, its training procedure is incredibly stable. Throughout the whole training procedure, we did not experience any irrecoverable loss spikes or carry out any rollbacks.
2. Model Summary
Architecture: Innovative Load Balancing Strategy and Training Objective
– On top of the effective architecture of DeepSeek-V2, we leader an auxiliary-loss-free method for load balancing, which lessens the performance destruction that occurs from motivating load balancing.
– We examine a Multi-Token Prediction (MTP) objective and show it beneficial to design efficiency. It can also be utilized for speculative decoding for reasoning acceleration.
Pre-Training: Towards Ultimate Training Efficiency
– We develop an FP8 mixed accuracy training framework and, for the first time, validate the expediency and efficiency of FP8 training on an extremely massive design.
– Through of algorithms, structures, and hardware, we overcome the communication bottleneck in cross-node MoE training, almost achieving full computation-communication overlap.
This substantially improves our training efficiency and lowers the training expenses, enabling us to even more scale up the model size without additional overhead.
– At an affordable cost of only 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 model. The subsequent training stages after pre-training need just 0.1 M GPU hours.
Post-Training: Knowledge Distillation from DeepSeek-R1
– We introduce an ingenious method to distill thinking abilities from the long-Chain-of-Thought (CoT) model, particularly from among the DeepSeek R1 series models, into standard LLMs, especially DeepSeek-V3. Our pipeline elegantly incorporates the confirmation and reflection patterns of R1 into DeepSeek-V3 and significantly enhances its reasoning efficiency. Meanwhile, we also keep a control over the output style and length of DeepSeek-V3.
3. Model Downloads
The overall size of DeepSeek-V3 designs on Hugging Face is 685B, that includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights. **
To guarantee optimum efficiency and versatility, we have partnered with open-source neighborhoods and hardware vendors to offer several ways to run the design locally. For detailed assistance, take a look at Section 6: How_to Run_Locally.
For designers seeking to dive deeper, we suggest exploring README_WEIGHTS. md for information on the Main Model weights and the Multi-Token Prediction (MTP) Modules. Please note that MTP support is currently under active advancement within the community, and we invite your contributions and feedback.
4. Evaluation Results
Base Model
Standard Benchmarks
Best results are revealed in vibrant. Scores with a gap not exceeding 0.3 are considered to be at the same level. DeepSeek-V3 accomplishes the very best efficiency on many criteria, specifically on mathematics and code jobs. For more evaluation information, please check our paper.
Context Window
Evaluation results on the Needle In A Haystack (NIAH) tests. DeepSeek-V3 performs well throughout all context window lengths as much as 128K.
Chat Model
Standard Benchmarks (Models bigger than 67B)
All designs are evaluated in a configuration that restricts the output length to 8K. Benchmarks containing fewer than 1000 samples are evaluated numerous times utilizing differing temperature settings to derive robust last outcomes. DeepSeek-V3 stands as the best-performing open-source model, and also exhibits competitive performance versus frontier closed-source models.
Open Ended Generation Evaluation
English open-ended conversation examinations. 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 main website: chat.deepseek.com
We likewise provide OpenAI-Compatible API at DeepSeek Platform: platform.deepseek.com
6. How to Run Locally
DeepSeek-V3 can be released in your area utilizing the following hardware and open-source neighborhood software application:
DeepSeek-Infer Demo: We supply a basic and light-weight demo for FP8 and BF16 reasoning.
SGLang: Fully support the DeepSeek-V3 model in both BF16 and FP8 inference modes, with Multi-Token Prediction coming soon.
LMDeploy: Enables effective FP8 and BF16 reasoning for local and cloud deployment.
TensorRT-LLM: Currently supports BF16 inference and INT4/8 quantization, with FP8 support 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 by means of 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 only offer FP8 weights. If you need BF16 weights for experimentation, you can use the provided conversion script to carry out the transformation.
Here is an example of converting FP8 weights to BF16:
Hugging Face’s Transformers has actually not been straight supported yet. **
6.1 Inference with DeepSeek-Infer Demo (example just)
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 reasoning folder and install dependencies listed in requirements.txt. Easiest method is to utilize a plan manager like conda or uv to create a new virtual environment and install the dependencies.
Download the design weights from Hugging Face, and put them into/ path/to/DeepSeek-V 3 folder.
Model Weights Conversion
Convert Hugging Face design weights to a specific format:
Run
Then you can talk with DeepSeek-V3:
Or batch reasoning on a provided file:
6.2 Inference with SGLang (advised)
SGLang currently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, delivering modern latency and throughput efficiency amongst open-source structures.
Notably, SGLang v0.4.1 completely supports running DeepSeek-V3 on both NVIDIA and AMD GPUs, making it a highly versatile and robust solution.
SGLang likewise supports multi-node tensor parallelism, enabling you to run this design on several network-connected machines.
Multi-Token Prediction (MTP) is in advancement, and progress can be tracked in the optimization plan.
Here are the launch instructions from the SGLang team: https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3
6.3 Inference with LMDeploy (recommended)
LMDeploy, a flexible and high-performance reasoning and serving framework tailored for big language models, now supports DeepSeek-V3. It offers both offline pipeline processing and online implementation abilities, flawlessly incorporating with PyTorch-based workflows.
For extensive detailed directions on running DeepSeek-V3 with LMDeploy, please refer to here: InternLM/lmdeploy # 2960
6.4 Inference with TRT-LLM (recommended)
TensorRT-LLM now supports the DeepSeek-V3 model, using accuracy alternatives such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in development and will be launched soon. You can access the customized branch of TRTLLM specifically for DeepSeek-V3 support through the following link to experience the brand-new functions directly: https://github.com/NVIDIA/TensorRT-LLM/tree/deepseek/examples/deepseek_v3.
6.5 Inference with vLLM (suggested)
vLLM v0.6.6 supports DeepSeek-V3 inference for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from basic methods, vLLM offers pipeline parallelism enabling you to run this design on multiple devices connected by networks. For comprehensive guidance, please describe the vLLM instructions. Please do not hesitate to follow the improvement plan also.
6.6 Recommended Inference Functionality with AMD GPUs
In cooperation with the AMD team, we have actually achieved Day-One assistance for AMD GPUs utilizing SGLang, with full compatibility for both FP8 and BF16 accuracy. For in-depth assistance, please refer to the SGLang instructions.
6.7 Recommended Inference Functionality with Huawei Ascend NPUs
The MindIE structure from the Huawei Ascend neighborhood has actually effectively adapted the BF16 variation of DeepSeek-V3. For detailed assistance on Ascend NPUs, please follow the directions here.
7. License
This code repository is accredited under the MIT License. Using DeepSeek-V3 Base/Chat models undergoes the Model License. DeepSeek-V3 series (including Base and Chat) supports industrial use.