GitHub – Deepseek-ai/DeepSeek-V3

We provide DeepSeek-V3, a strong Mixture-of-Experts (MoE) language design with 671B overall specifications with 37B activated for each token. To achieve efficient inference and cost-efficient training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly confirmed in DeepSeek-V2. Furthermore, DeepSeek-V3 leaders an auxiliary-loss-free method for load balancing and sets a multi-token prediction training objective for more powerful performance. We pre-train DeepSeek-V3 on 14.8 trillion varied and premium tokens, followed by Supervised Fine-Tuning and Reinforcement Learning phases to completely harness its capabilities. Comprehensive evaluations reveal that DeepSeek-V3 exceeds other open-source models and achieves efficiency equivalent to leading closed-source models. Despite its exceptional performance, DeepSeek-V3 requires only 2.788 M H800 GPU hours for its complete training. In addition, its training procedure is incredibly stable. Throughout the whole training process, 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 efficient architecture of DeepSeek-V2, we leader an auxiliary-loss-free technique for load balancing, which reduces the efficiency degradation that emerges from motivating load balancing.
– We examine a Multi-Token Prediction (MTP) goal and prove it helpful to model efficiency. It can also be utilized for speculative decoding for reasoning velocity.

Pre-Training: Towards Ultimate Training Efficiency

– We design an FP8 mixed accuracy training structure and, for the very first time, verify the expediency and effectiveness of FP8 training on an extremely massive model.
– Through co-design of algorithms, frameworks, and hardware, we get rid of the interaction traffic jam in cross-node MoE training, nearly accomplishing complete computation-communication overlap.
This significantly improves our training performance and lowers the training costs, enabling us to further scale up the design size without additional overhead.
– At an affordable expense of just 2.664 M H800 GPU hours, we complete the pre-training of DeepSeek-V3 on 14.8 T tokens, producing the currently greatest open-source base design. 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 boil down thinking capabilities from the long-Chain-of-Thought (CoT) model, specifically 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 notably enhances its reasoning performance. Meanwhile, we also preserve a control over the output style 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 neighborhoods and hardware vendors to offer multiple methods to run the design in your area. For detailed guidance, inspect out Section 6: How_to Run_Locally.

For designers looking to dive much deeper, we suggest checking out README_WEIGHTS. md for details on the Main Model weights and the Multi-Token Prediction (MTP) Modules. Please note that MTP assistance is presently under active advancement 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 gap not exceeding 0.3 are thought about to be at the same level. DeepSeek-V3 attains the finest performance on many standards, specifically on mathematics and code jobs. For more assessment details, please check our paper.

Context Window

Evaluation results on the Needle In A Haystack (NIAH) tests. DeepSeek-V3 performs well across all context window lengths approximately 128K.

Chat Model

Standard Benchmarks (Models bigger than 67B)

All models are evaluated in a configuration that limits the output length to 8K. Benchmarks containing fewer than 1000 samples are evaluated multiple times utilizing differing temperature level settings to derive robust last outcomes. DeepSeek-V3 stands as the best-performing open-source design, and likewise displays competitive performance versus frontier closed-source designs.

Open Ended Generation Evaluation

English open-ended conversation examinations. For AlpacaEval 2.0, we use the length-controlled win rate as the metric.

5. Chat Website & API Platform

You can chat with DeepSeek-V3 on DeepSeek’s main site: chat.deepseek.com

We also offer OpenAI-Compatible API at DeepSeek Platform: platform.deepseek.com

6. How to Run Locally

DeepSeek-V3 can be deployed in your area utilizing the following hardware and open-source neighborhood software application:

DeepSeek-Infer Demo: We offer an easy and lightweight demo for FP8 and BF16 reasoning.
SGLang: Fully support the DeepSeek-V3 model in both BF16 and FP8 reasoning modes, with Multi-Token Prediction coming soon.
LMDeploy: Enables efficient FP8 and BF16 inference for regional and cloud deployment.
TensorRT-LLM: Currently supports BF16 inference and INT4/8 quantization, with FP8 assistance coming quickly.
vLLM: Support DeepSeek-V3 design with FP8 and BF16 modes for tensor parallelism and pipeline parallelism.
AMD GPU: Enables running the DeepSeek-V3 design 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 embraced in our structure, we only offer FP8 weights. If you require BF16 weights for experimentation, you can use the provided conversion script to perform the change.

Here is an example of transforming FP8 weights to BF16:

Hugging Face’s Transformers has 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 set up dependences listed in requirements.txt. Easiest method is to utilize a bundle manager like conda or uv to create a brand-new virtual environment and set up 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 chat with DeepSeek-V3:

Or batch inference on an file:

6.2 Inference with SGLang (recommended)

SGLang currently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, delivering cutting edge latency and throughput efficiency among open-source structures.

Notably, SGLang v0.4.1 fully supports running DeepSeek-V3 on both NVIDIA and AMD GPUs, making it a highly flexible and robust solution.

SGLang also supports multi-node tensor parallelism, enabling you to run this design on numerous network-connected devices.

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 versatile and high-performance inference and serving framework customized for big language models, now supports DeepSeek-V3. It provides both offline pipeline processing and online deployment capabilities, flawlessly incorporating with PyTorch-based workflows.

For thorough detailed directions on running DeepSeek-V3 with LMDeploy, please refer to here: InternLM/lmdeploy # 2960

6.4 Inference with TRT-LLM (suggested)

TensorRT-LLM now supports the DeepSeek-V3 design, using accuracy options such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in progress 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 new features 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 uses pipeline parallelism enabling you to run this model on numerous devices connected by networks. For detailed assistance, please refer to 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 group, we have achieved Day-One assistance for AMD GPUs using SGLang, with full compatibility for both FP8 and BF16 accuracy. For comprehensive assistance, please describe the SGLang directions.

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 step-by-step guidance on Ascend NPUs, please follow the guidelines here.

7. License

This code repository is licensed under the MIT License. Making use of DeepSeek-V3 Base/Chat designs goes through the Model License. DeepSeek-V3 series (including Base and Chat) supports business usage.