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Overview

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Company Description

GitHub – Deepseek-ai/DeepSeek-V3

We provide DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B overall specifications with 37B triggered for each token. To achieve effective reasoning and cost-effective training, DeepSeek-V3 embraces Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were completely validated in DeepSeek-V2. Furthermore, DeepSeek-V3 leaders an auxiliary-loss-free method for load balancing and sets a multi-token forecast training goal for more powerful performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and top quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning phases to totally harness its capabilities. Comprehensive evaluations expose that DeepSeek-V3 outshines other open-source models and achieves performance comparable to leading closed-source designs. Despite its outstanding efficiency, DeepSeek-V3 requires only 2.788 M H800 GPU hours for its full training. In addition, its training procedure is remarkably steady. 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 efficient architecture of DeepSeek-V2, we pioneer an auxiliary-loss-free method for load balancing, which decreases the performance deterioration that emerges from encouraging load balancing.
– We investigate a Multi-Token Prediction (MTP) goal and show it beneficial to design efficiency. It can likewise be used for speculative decoding for reasoning acceleration.

Pre-Training: Towards Ultimate Training Efficiency

– We develop an FP8 mixed precision training framework and, for the very first time, verify the feasibility and effectiveness of FP8 training on an exceptionally large-scale design.
– Through co-design of algorithms, structures, and hardware, we get rid of the interaction traffic jam in cross-node MoE training, nearly achieving full computation-communication overlap.
This substantially improves our training effectiveness and lowers the training costs, allowing us to even more scale up the design size without additional overhead.
– At a cost-effective expense of just 2.664 M H800 GPU hours, we finish 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 introduce an ingenious method to boil down reasoning capabilities from the long-Chain-of-Thought (CoT) design, particularly from one of the DeepSeek R1 series designs, into basic LLMs, particularly DeepSeek-V3. Our pipeline elegantly integrates the confirmation and reflection patterns of R1 into DeepSeek-V3 and notably enhances its reasoning efficiency. Meanwhile, we likewise maintain 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 make sure optimum performance and flexibility, we have actually partnered with open-source neighborhoods and hardware vendors to provide numerous methods to run the model in your area. For step-by-step assistance, take a look at Section 6: How_to Run_Locally.

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

4. Evaluation Results

Base Model

Standard Benchmarks

Best results are displayed in strong. Scores with a space not exceeding 0.3 are considered to be at the exact same level. DeepSeek-V3 attains the finest efficiency on most criteria, especially on mathematics and code jobs. For more assessment information, please check our paper.

Context Window

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

Chat Model

Standard Benchmarks (Models bigger than 67B)

All designs are examined in a configuration that limits the output length to 8K. Benchmarks containing fewer than 1000 samples are evaluated multiple times using varying temperature level settings to derive robust outcomes. DeepSeek-V3 stands as the best-performing open-source model, and likewise exhibits competitive performance against frontier closed-source designs.

Open Ended Generation Evaluation

English open-ended discussion examinations. 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 main website: chat.deepseek.com

We likewise supply OpenAI-Compatible API at DeepSeek Platform: platform.deepseek.com

6. How to Run Locally

DeepSeek-V3 can be released locally using the following hardware and open-source neighborhood software:

DeepSeek-Infer Demo: We offer a basic and lightweight demonstration for FP8 and BF16 reasoning.
SGLang: Fully support the DeepSeek-V3 design in both BF16 and FP8 reasoning modes, with Multi-Token Prediction coming quickly.
LMDeploy: Enables efficient FP8 and BF16 inference for regional and cloud implementation.
TensorRT-LLM: Currently supports BF16 inference and INT4/8 quantization, with FP8 assistance 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 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 structure, we just supply FP8 weights. If you require BF16 weights for experimentation, you can utilize 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 straight 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 listed in .txt. Easiest method is to utilize a plan manager like conda or uv to create 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 model weights to a specific format:

Run

Then you can chat with DeepSeek-V3:

Or batch reasoning on an offered file:

6.2 Inference with SGLang (advised)

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

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

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

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

Here are the launch instructions from the SGLang team: 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 tailored for large language designs, now supports DeepSeek-V3. It uses both offline pipeline processing and online release capabilities, seamlessly incorporating with PyTorch-based workflows.

For extensive detailed guidelines on running DeepSeek-V3 with LMDeploy, please refer to here: InternLM/lmdeploy # 2960

6.4 Inference with TRT-LLM (recommended)

TensorRT-LLM now supports the DeepSeek-V3 model, offering accuracy choices 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 support through the following link to experience the 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 methods, vLLM uses pipeline parallelism permitting you to run this model on numerous makers linked by networks. For detailed guidance, please describe the vLLM guidelines. Please do not hesitate to follow the improvement strategy also.

6.6 Recommended Inference Functionality with AMD GPUs

In partnership with the AMD group, we have actually accomplished Day-One support for AMD GPUs utilizing SGLang, with full compatibility for both FP8 and BF16 precision. For comprehensive assistance, please refer to the SGLang instructions.

6.7 Recommended Inference Functionality with Huawei Ascend NPUs

The MindIE framework from the Huawei Ascend community has effectively adapted the BF16 variation of DeepSeek-V3. For detailed assistance on Ascend NPUs, please follow the guidelines here.

7. License

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