Overview

  • Founded Date September 2, 1954
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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 reasoning and economical training, DeepSeek-V3 embraces Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were thoroughly verified in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers an auxiliary-loss-free technique for load balancing and sets a multi-token forecast training objective for more powerful efficiency. We pre-train DeepSeek-V3 on 14.8 trillion diverse and top quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to totally harness its capabilities. Comprehensive assessments reveal that DeepSeek-V3 surpasses other open-source designs and attains performance similar to leading closed-source designs. Despite its exceptional efficiency, DeepSeek-V3 needs only 2.788 M H800 GPU hours for its complete training. In addition, its training process is remarkably steady. 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 efficient architecture of DeepSeek-V2, we pioneer an auxiliary-loss-free technique for load balancing, which minimizes the performance degradation that occurs from motivating load balancing.
– We examine a Multi-Token Prediction (MTP) objective and prove it helpful to model performance. It can likewise be utilized for speculative decoding for inference acceleration.

Pre-Training: Towards Ultimate Training Efficiency

– We design an FP8 combined precision training framework and, for the very first time, validate the expediency and effectiveness of FP8 training on a very large-scale model.
– Through co-design of algorithms, frameworks, and hardware, we conquer the interaction bottleneck in cross-node MoE training, nearly accomplishing complete computation-communication overlap.
This considerably improves our training effectiveness and minimizes the training costs, enabling us to further scale up the design size without additional overhead.
– At an economical expense of only 2.664 M H800 GPU hours, we complete 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 only 0.1 M GPU hours.

Post-Training: Knowledge Distillation from DeepSeek-R1

– We present an innovative approach to distill thinking 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 incorporates the confirmation and reflection patterns of R1 into DeepSeek-V3 and especially enhances its reasoning efficiency. Meanwhile, we also maintain a control over the output design and length of DeepSeek-V3.

3. Model Downloads

The total 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 actually partnered with open-source communities and hardware vendors to provide numerous methods to run the model locally. For detailed guidance, have a look at Section 6: How_to Run_Locally.

For designers seeking 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 currently under active development within the neighborhood, and we invite your contributions and feedback.

4. Evaluation Results

Base Model

Standard Benchmarks

Best outcomes are revealed in vibrant. Scores with a gap not going beyond 0.3 are considered to be at the very same level. DeepSeek-V3 accomplishes the very best efficiency on a lot of benchmarks, especially on mathematics and code tasks. For more evaluation details, please our paper.

Context Window

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

Chat Model

Standard Benchmarks (Models bigger than 67B)

All models are assessed in a setup that limits the output length to 8K. Benchmarks including fewer than 1000 samples are tested several times utilizing differing temperature settings to derive robust results. DeepSeek-V3 stands as the best-performing open-source design, and also displays competitive efficiency against frontier closed-source models.

Open Ended Generation Evaluation

English open-ended conversation evaluations. 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 official 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 released locally 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 inference.
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 regional and cloud release.
TensorRT-LLM: Currently supports BF16 inference and INT4/8 quantization, with FP8 assistance 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 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 only offer FP8 weights. If you need BF16 weights for experimentation, you can use the offered conversion script to carry out the change.

Here is an example of converting FP8 weights to BF16:

Hugging Face’s Transformers has not been straight supported yet. **

6.1 Inference with DeepSeek-Infer Demo (example only)

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 reliances listed in requirements.txt. Easiest method is to use a package manager like conda or uv to produce a brand-new virtual environment and install 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 specific format:

Run

Then you can chat with DeepSeek-V3:

Or batch inference on a given file:

6.2 Inference with SGLang (advised)

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

Notably, SGLang v0.4.1 totally supports running DeepSeek-V3 on both NVIDIA and AMD GPUs, making it an extremely flexible and robust option.

SGLang likewise supports multi-node tensor parallelism, allowing you to run this design on multiple network-connected devices.

Multi-Token Prediction (MTP) is in development, 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 (advised)

LMDeploy, a versatile and high-performance inference and serving framework tailored 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 comprehensive 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, offering accuracy options such as BF16 and INT4/INT8 weight-only. Support for FP8 is currently in progress and will be launched quickly. You can access the custom-made branch of TRTLLM specifically for DeepSeek-V3 assistance 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 reasoning for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from basic methods, vLLM uses pipeline parallelism permitting you to run this design on numerous devices connected by networks. For detailed assistance, please refer to the vLLM instructions. Please feel totally free 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 utilizing SGLang, with full compatibility for both FP8 and BF16 accuracy. For detailed assistance, please refer to the SGLang instructions.

6.7 Recommended Inference Functionality with Huawei Ascend NPUs

The MindIE structure from the Huawei Ascend community has effectively adjusted the BF16 version of DeepSeek-V3. For detailed assistance on Ascend NPUs, please follow the instructions here.

7. License

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