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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 achieve efficient reasoning and economical training, DeepSeek-V3 adopts Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly validated in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free technique for load balancing and sets a multi-token prediction training objective for stronger performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to totally harness its capabilities. Comprehensive assessments expose that DeepSeek-V3 outshines other open-source models and attains efficiency comparable to leading closed-source models. Despite its exceptional efficiency, DeepSeek-V3 requires only 2.788 M H800 GPU hours for its complete training. In addition, its training process is incredibly steady. Throughout the entire 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 efficient architecture of DeepSeek-V2, we pioneer an auxiliary-loss-free method 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 design performance. It can likewise be used for speculative decoding for inference velocity.

Pre-Training: Towards Ultimate Training Efficiency

– We create an FP8 mixed precision training structure and, for the very first time, validate the feasibility and effectiveness of FP8 training on an extremely large-scale model.
– Through co-design of algorithms, structures, and hardware, we get rid of the communication traffic jam in cross-node MoE training, almost achieving complete computation-communication overlap.
This considerably enhances our training performance and lowers the training expenses, allowing us to even more scale up the design size without additional overhead.
– At an affordable expense of only 2.664 M H800 GPU hours, we finish the pre-training of DeepSeek-V3 on 14.8 T tokens, producing the currently greatest open-source base model. The subsequent training phases after pre-training need just 0.1 M GPU hours.

Post-Training: Knowledge Distillation from DeepSeek-R1

– We introduce an ingenious methodology to boil down reasoning capabilities from the long-Chain-of-Thought (CoT) model, particularly from one of the DeepSeek R1 series designs, into standard LLMs, especially DeepSeek-V3. Our pipeline elegantly integrates the verification and reflection patterns of R1 into DeepSeek-V3 and notably enhances its thinking efficiency. Meanwhile, we also preserve 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, which consists of 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights. **

To ensure optimal efficiency and flexibility, we have partnered with open-source neighborhoods and hardware vendors to offer numerous methods to run the model in your area. For step-by-step assistance, have a look at Section 6: How_to Run_Locally.

For developers seeking to dive deeper, we recommend exploring README_WEIGHTS. md for information on the Main Model weights and the Multi-Token Prediction (MTP) Modules. Please note that MTP assistance is currently under active advancement within the neighborhood, and we welcome your contributions and feedback.

4. Evaluation Results

Base Model

Standard Benchmarks

Best outcomes are shown in bold. Scores with a gap not going beyond 0.3 are thought about to be at the very same level. DeepSeek-V3 accomplishes the very best efficiency on many criteria, especially on mathematics and code tasks. For more evaluation details, please inspect our paper.

Context Window

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

Chat Model

Standard Benchmarks (Models larger than 67B)

All models are evaluated in a configuration that restricts the output length to 8K. Benchmarks containing fewer than 1000 samples are evaluated numerous times using differing temperature level settings to derive robust outcomes. DeepSeek-V3 stands as the best-performing open-source model, and likewise displays competitive efficiency versus frontier closed-source models.

Open Ended Generation Evaluation

English open-ended conversation evaluations. 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 site: chat.deepseek.com

We likewise supply 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:

DeepSeek-Infer Demo: We offer a simple and light-weight demo for FP8 and BF16 inference.
SGLang: Fully support the DeepSeek-V3 model in both BF16 and FP8 inference modes, with Multi-Token Prediction coming soon.
LMDeploy: Enables efficient FP8 and BF16 inference for regional and cloud release.
TensorRT-LLM: Currently supports BF16 reasoning and INT4/8 quantization, with FP8 support 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 devices.
Since FP8 training is natively embraced in our framework, we only offer FP8 weights. If you need BF16 weights for experimentation, you can utilize the offered conversion script to carry out the change.

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 listed in requirements.txt. Easiest way is to utilize a package manager like conda or uv to develop a brand-new virtual environment and set up the dependences.

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 particular format:

Run

Then you can chat with DeepSeek-V3:

Or batch inference 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 advanced latency and throughput efficiency among open-source frameworks.

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

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

Multi-Token Prediction (MTP) is in advancement, and development can be tracked in the optimization plan.

Here are the launch directions from the SGLang team: https://github.com/sgl-project/sglang/tree/main/benchmark/deepseek_v3

6.3 Inference with LMDeploy (suggested)

LMDeploy, a versatile and high-performance reasoning and serving framework customized for large language models, now supports DeepSeek-V3. It provides both offline pipeline processing and online implementation capabilities, flawlessly integrating with PyTorch-based workflows.

For detailed step-by-step instructions 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 soon. You can access the customized branch of TRTLLM particularly for DeepSeek-V3 support through the following link to experience the brand-new functions straight: https://github.com/NVIDIA/TensorRT-LLM/tree/deepseek/examples/deepseek_v3.

6.5 Inference with vLLM (recommended)

vLLM v0.6.6 supports DeepSeek-V3 reasoning for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from standard methods, vLLM provides pipeline parallelism enabling you to run this design on several makers linked by networks. For in-depth assistance, please describe the vLLM guidelines. Please feel complimentary to follow the enhancement strategy as well.

6.6 Recommended Inference Functionality with AMD GPUs

In partnership with the AMD team, we have actually achieved Day-One support for AMD GPUs utilizing SGLang, with full compatibility for both FP8 and BF16 precision. For detailed guidance, 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 adjusted the BF16 version of DeepSeek-V3. For detailed on Ascend NPUs, please follow the instructions here.

7. License

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