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Overview

  • Founded Date August 12, 1937
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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 overall parameters with 37B triggered for each token. To attain efficient reasoning and economical 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 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 diverse and high-quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning phases to totally harness its abilities. Comprehensive evaluations reveal that DeepSeek-V3 exceeds other open-source designs and accomplishes performance similar 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 procedure is remarkably stable. Throughout the whole training procedure, 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 lessens the efficiency deterioration that develops from encouraging load balancing.
– We investigate a Multi-Token Prediction (MTP) objective and prove it useful to design performance. It can likewise be used for speculative decoding for reasoning acceleration.

Pre-Training: Towards Ultimate Training Efficiency

– We design an FP8 blended accuracy training framework and, for the very first time, validate the feasibility and efficiency of FP8 training on an extremely large-scale model.
– Through co-design of algorithms, frameworks, and hardware, we conquer the interaction bottleneck in cross-node MoE training, nearly achieving full computation-communication overlap.
This significantly improves our training performance and minimizes the training expenses, enabling us to even more scale up the design size without extra 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 currently strongest open-source base model. The subsequent training phases after pre-training require only 0.1 M GPU hours.

Post-Training: Knowledge Distillation from DeepSeek-R1

– We present an innovative method to boil down thinking capabilities from the long-Chain-of-Thought (CoT) model, specifically from one of the DeepSeek R1 series models, into standard LLMs, particularly DeepSeek-V3. Our pipeline elegantly includes the confirmation and reflection patterns of R1 into DeepSeek-V3 and significantly enhances its reasoning performance. Meanwhile, we also preserve 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 flexibility, we have partnered with open-source neighborhoods and hardware suppliers to provide several methods to run the design locally. For step-by-step guidance, examine out Section 6: How_to Run_Locally.

For designers seeking to dive deeper, we suggest checking out 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 development within the community, and we invite your contributions and feedback.

4. Evaluation Results

Base Model

Standard Benchmarks

Best results are shown in strong. Scores with a space not exceeding 0.3 are considered to be at the same level. DeepSeek-V3 attains the finest performance on many standards, especially on math and code jobs. For more assessment information, please examine our paper.

Context Window

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

Chat Model

Standard Benchmarks (Models bigger than 67B)

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

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 chat with DeepSeek-V3 on DeepSeek’s official 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 locally using the following hardware and open-source community software:

DeepSeek-Infer Demo: We supply a simple and light-weight 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 effective FP8 and BF16 inference for regional and cloud implementation.
TensorRT-LLM: Currently supports BF16 inference and INT4/8 quantization, with FP8 assistance coming quickly.
vLLM: DeepSeek-V3 model 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 devices.
Since FP8 training is natively adopted in our structure, we only provide FP8 weights. If you require BF16 weights for experimentation, you can use the offered conversion script to carry out the transformation.

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

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 reasoning on a given file:

6.2 Inference with SGLang (suggested)

SGLang currently supports MLA optimizations, DP Attention, FP8 (W8A8), FP8 KV Cache, and Torch Compile, providing state-of-the-art 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 an extremely versatile and robust option.

SGLang likewise supports multi-node tensor parallelism, enabling you to run this model on several network-connected makers.

Multi-Token Prediction (MTP) remains in development, and development 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 large language models, now supports DeepSeek-V3. It uses both offline pipeline processing and online implementation capabilities, effortlessly incorporating 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 progress and will be released soon. You can access the customized branch of TRTLLM particularly for DeepSeek-V3 assistance 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 (advised)

vLLM v0.6.6 supports DeepSeek-V3 reasoning for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from basic strategies, vLLM provides pipeline parallelism permitting you to run this model on numerous makers connected by networks. For comprehensive guidance, please refer to the vLLM guidelines. Please do not hesitate to follow the improvement plan too.

6.6 Recommended Inference Functionality with AMD GPUs

In partnership with the AMD team, we have accomplished Day-One assistance for AMD GPUs using SGLang, with full compatibility for both FP8 and BF16 accuracy. For in-depth guidance, please refer to the SGLang directions.

6.7 Recommended Inference Functionality with Huawei Ascend NPUs

The MindIE structure from the Huawei Ascend community has effectively adapted the BF16 variation of DeepSeek-V3. For step-by-step assistance on Ascend NPUs, please follow the instructions here.

7. License

This code repository is accredited under the MIT License. Using DeepSeek-V3 Base/Chat designs goes through the Model License. DeepSeek-V3 series (consisting of Base and Chat) supports commercial use.

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