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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 criteria with 37B triggered for each token. To accomplish efficient reasoning and affordable training, DeepSeek-V3 embraces Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were completely verified in DeepSeek-V2. Furthermore, DeepSeek-V3 pioneers 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 abilities. Comprehensive examinations reveal that DeepSeek-V3 outperforms other open-source models and accomplishes performance comparable to leading closed-source models. Despite its excellent efficiency, DeepSeek-V3 requires just 2.788 M H800 GPU hours for its full training. In addition, its training process is extremely steady. 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 effective architecture of DeepSeek-V2, we pioneer an auxiliary-loss-free method for load balancing, which reduces the performance destruction that arises from encouraging load balancing.
– We investigate a Multi-Token Prediction (MTP) goal and show it advantageous to model efficiency. It can also be used for speculative decoding for inference acceleration.

Pre-Training: Towards Ultimate Training Efficiency

– We create an FP8 mixed precision training structure and, for the very first time, verify the feasibility and efficiency of FP8 training on an extremely massive model.
– Through co-design of algorithms, structures, and hardware, we overcome the communication traffic jam in cross-node MoE training, almost achieving full computation-communication overlap.
This significantly improves our training efficiency and lowers the training costs, allowing us to even more scale up the model size without additional overhead.
– At an affordable 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 greatest open-source base model. The subsequent training phases 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, specifically from among the DeepSeek R1 series models, into standard LLMs, especially DeepSeek-V3. Our pipeline elegantly integrates the verification and reflection patterns of R1 into DeepSeek-V3 and especially improves its reasoning efficiency. Meanwhile, we likewise keep a control over the output design and length of DeepSeek-V3.

3. Model Downloads

The total size of DeepSeek-V3 models 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 guarantee optimum performance and versatility, we have actually partnered with open-source neighborhoods and hardware suppliers to provide multiple methods to run the design locally. For step-by-step assistance, check out Section 6: How_to Run_Locally.

For designers wanting to dive deeper, we advise exploring 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 community, and we invite your contributions and feedback.

4. Evaluation Results

Base Model

Standard Benchmarks

Best outcomes are displayed in bold. Scores with a space not surpassing 0.3 are considered to be at the very same level. DeepSeek-V3 attains the finest performance on the majority of criteria, especially on math and code jobs. For more examination information, 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 as much as 128K.

Chat Model

Standard Benchmarks (Models larger than 67B)

All designs are examined in a setup that limits the output length to 8K. Benchmarks containing fewer than 1000 samples are tested numerous times utilizing varying temperature level settings to obtain robust outcomes. DeepSeek-V3 stands as the best-performing open-source model, and likewise exhibits competitive performance versus frontier closed-source designs.

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 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 supply a simple and lightweight demonstration for FP8 and BF16 reasoning.
SGLang: Fully support the DeepSeek-V3 design in both BF16 and FP8 inference modes, with Multi-Token Prediction coming quickly.
LMDeploy: Enables efficient FP8 and BF16 inference for regional and cloud deployment.
TensorRT-LLM: Currently supports BF16 inference and INT4/8 quantization, with FP8 support coming soon.
vLLM: Support 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 through 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 supply FP8 weights. If you require BF16 weights for experimentation, you can utilize the provided conversion script to carry out the change.

Here is an example of converting FP8 weights to BF16:

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

6.1 Inference with DeepSeek-Infer Demo (example only)

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 reasoning folder and set up reliances listed in requirements.txt. Easiest way is to use a package manager like conda or uv to produce 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 inference on a provided file:

6.2 Inference with SGLang (suggested)

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

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

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

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

Here are the launch guidelines from the SGLang group: 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 structure customized for large language models, now supports DeepSeek-V3. It offers both offline pipeline processing and online deployment capabilities, perfectly incorporating with PyTorch-based workflows.

For detailed detailed guidelines 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 model, using 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 specifically for DeepSeek-V3 assistance through the following link to experience the new functions straight: 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 strategies, vLLM provides pipeline parallelism enabling you to run this design on several makers connected by networks. For detailed assistance, please refer to the vLLM instructions. Please feel free to follow the enhancement plan too.

6.6 Recommended Inference Functionality with AMD GPUs

In partnership with the AMD group, we have actually 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 instructions.

6.7 Recommended Inference Functionality with Huawei Ascend NPUs

The MindIE framework from the Huawei Ascend community has successfully adjusted the BF16 variation of DeepSeek-V3. For 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 is subject to the Model License. DeepSeek-V3 series (including Base and Chat) supports industrial usage.

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