GitHub – Deepseek-ai/DeepSeek-V3

We present DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total criteria with 37B activated for each token. To accomplish efficient reasoning and affordable training, DeepSeek-V3 embraces Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly confirmed in DeepSeek-V2. Furthermore, DeepSeek-V3 leaders an auxiliary-loss-free technique for load balancing and sets a multi-token forecast training objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion varied and premium tokens, followed by Supervised Fine-Tuning and Reinforcement Learning phases to totally harness its capabilities. Comprehensive evaluations expose that DeepSeek-V3 exceeds other open-source models and attains efficiency comparable to leading closed-source designs. Despite its exceptional efficiency, DeepSeek-V3 needs just 2.788 M H800 GPU hours for its full training. In addition, its training process is extremely stable. Throughout the entire 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 effective architecture of DeepSeek-V2, we leader an auxiliary-loss-free technique for load balancing, which lessens the performance degradation that emerges from encouraging load balancing.
– We investigate a Multi-Token Prediction (MTP) objective and show it helpful to model performance. It can likewise be utilized for speculative decoding for inference velocity.

Pre-Training: Towards Ultimate Training Efficiency

– We develop an FP8 blended precision training framework and, for the very first time, verify the feasibility and effectiveness of FP8 training on a very massive design.
– Through co-design of algorithms, structures, and hardware, we get rid of the communication bottleneck in cross-node MoE training, nearly accomplishing complete computation-communication overlap.
This substantially improves our training performance and decreases the training costs, enabling us to even more scale up the model size without extra overhead.
– At a cost-effective 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 strongest open-source base model. The subsequent training stages after pre-training require only 0.1 M GPU hours.

Post-Training: Knowledge Distillation from DeepSeek-R1

– We introduce an ingenious method to distill reasoning capabilities from the long-Chain-of-Thought (CoT) design, particularly from among the DeepSeek R1 series designs, into basic LLMs, especially DeepSeek-V3. Our pipeline elegantly integrates the confirmation and reflection patterns of R1 into DeepSeek-V3 and notably enhances its reasoning performance. Meanwhile, we likewise keep a control over the output style and length of DeepSeek-V3.

3. Model Downloads

The total 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 optimum performance and flexibility, we have partnered with open-source communities and hardware vendors to provide multiple methods to run the design in your area. For detailed assistance, take a look at Section 6: How_to Run_Locally.

For designers aiming to dive deeper, we advise checking out README_WEIGHTS. md for details 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 welcome your contributions and feedback.

4. Evaluation Results

Base Model

Standard Benchmarks

Best results are revealed in vibrant. Scores with a gap not going beyond 0.3 are thought about to be at the exact same level. DeepSeek-V3 achieves the finest performance on most criteria, especially on mathematics and code jobs. For more evaluation details, please inspect our paper.

Context Window

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

Chat Model

Standard Benchmarks (Models bigger than 67B)

All designs are assessed in a configuration that limits the output length to 8K. Benchmarks containing less than 1000 samples are evaluated multiple times using differing temperature level settings to derive robust results. DeepSeek-V3 stands as the best-performing open-source model, and also displays competitive efficiency against frontier closed-source designs.

Open Ended Generation Evaluation

English open-ended discussion assessments. For AlpacaEval 2.0, we utilize the length-controlled win rate as the metric.

5. Chat Website & API Platform

You can talk with DeepSeek-V3 on DeepSeek’s main 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 deployed in your area using the following hardware and open-source neighborhood software:

DeepSeek-Infer Demo: We provide a simple and light-weight demo for FP8 and BF16 inference.
SGLang: Fully support the DeepSeek-V3 design in both BF16 and FP8 inference modes, with Multi-Token Prediction coming quickly.
LMDeploy: Enables effective FP8 and BF16 inference for local and cloud release.
TensorRT-LLM: Currently supports BF16 reasoning 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 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 embraced in our framework, we just offer FP8 weights. If you require BF16 weights for experimentation, you can use the offered conversion script to carry out the improvement.

Here is an example of transforming FP8 weights to BF16:

Hugging Face’s Transformers has actually not been directly 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 inference folder and install dependences noted in requirements.txt. Easiest method is to use a bundle supervisor like conda or uv to develop a brand-new virtual environment and set up the dependences.

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 given file:

6.2 Inference with SGLang (recommended)

SGLang presently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, providing advanced latency and throughput performance amongst open-source frameworks.

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

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

Multi-Token Prediction (MTP) remains in development, and progress can be tracked in the .

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 (recommended)

LMDeploy, a versatile and high-performance reasoning and serving framework customized for big language designs, now supports DeepSeek-V3. It uses both offline pipeline processing and online deployment capabilities, seamlessly integrating with PyTorch-based workflows.

For comprehensive detailed directions on running DeepSeek-V3 with LMDeploy, please describe here: InternLM/lmdeploy # 2960

6.4 Inference with TRT-LLM (recommended)

TensorRT-LLM now supports the DeepSeek-V3 design, offering accuracy options such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in development and will be launched quickly. You can access the customized branch of TRTLLM particularly 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 (advised)

vLLM v0.6.6 supports DeepSeek-V3 inference for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from standard methods, vLLM offers pipeline parallelism enabling you to run this model on numerous makers connected by networks. For in-depth guidance, please describe the vLLM instructions. Please feel totally free to follow the improvement plan too.

6.6 Recommended Inference Functionality with AMD GPUs

In partnership with the AMD team, we have actually attained 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 community has actually effectively adapted the BF16 version of DeepSeek-V3. For detailed assistance on Ascend NPUs, please follow the directions here.

7. License

This code repository is licensed under the MIT License. Using DeepSeek-V3 Base/Chat models goes through the Model License. DeepSeek-V3 series (including Base and Chat) supports commercial use.