GitHub – Deepseek-ai/DeepSeek-V3

We provide DeepSeek-V3, a strong Mixture-of-Experts (MoE) language design with 671B overall parameters with 37B triggered for each token. To accomplish effective reasoning and cost-effective training, DeepSeek-V3 embraces Multi-head Latent Attention (MLA) and DeepSeekMoE architectures, which were completely confirmed in DeepSeek-V2. Furthermore, DeepSeek-V3 leaders an auxiliary-loss-free method for load balancing and sets a multi-token prediction training goal for more powerful performance. We pre-train DeepSeek-V3 on 14.8 trillion diverse and premium tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to completely harness its capabilities. expose that DeepSeek-V3 outperforms other open-source designs and accomplishes efficiency equivalent to leading closed-source models. Despite its excellent performance, DeepSeek-V3 needs only 2.788 M H800 GPU hours for its complete training. In addition, its training process is extremely 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 effective architecture of DeepSeek-V2, we pioneer an auxiliary-loss-free strategy for load balancing, which decreases the efficiency destruction that develops from encouraging load balancing.
– We investigate a Multi-Token Prediction (MTP) objective and show it useful to model performance. It can also be used for speculative decoding for inference velocity.

Pre-Training: Towards Ultimate Training Efficiency

– We create an FP8 combined precision training structure and, for the very first time, confirm the expediency and efficiency of FP8 training on a very large-scale model.
– Through co-design of algorithms, frameworks, and hardware, we get rid of the interaction bottleneck in cross-node MoE training, nearly accomplishing full computation-communication overlap.
This substantially boosts our training effectiveness and minimizes the training expenses, enabling us to even more scale up the design size without extra overhead.
– At a cost-effective cost of only 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 design. The subsequent training phases after pre-training require only 0.1 M GPU hours.

Post-Training: Knowledge Distillation from DeepSeek-R1

– We introduce an ingenious methodology to distill reasoning capabilities from the long-Chain-of-Thought (CoT) model, particularly from one of the DeepSeek R1 series models, into basic LLMs, particularly DeepSeek-V3. Our pipeline elegantly integrates the verification and reflection patterns of R1 into DeepSeek-V3 and significantly improves its reasoning performance. Meanwhile, we likewise maintain a control over the output design and length of DeepSeek-V3.

3. Model Downloads

The overall size of DeepSeek-V3 designs 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 ideal performance and flexibility, we have partnered with open-source neighborhoods and hardware vendors to supply several methods to run the model in your area. For detailed guidance, examine out Section 6: How_to Run_Locally.

For developers wanting to dive much deeper, we advise 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 shown in vibrant. Scores with a space not surpassing 0.3 are considered to be at the exact same level. DeepSeek-V3 achieves the best performance on many criteria, especially on math and code jobs. For more examination details, please inspect 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 larger than 67B)

All models are examined in a setup that restricts the output length to 8K. Benchmarks consisting of less 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 likewise shows competitive efficiency against frontier closed-source designs.

Open Ended Generation Evaluation

English open-ended discussion 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 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 in your area utilizing the following hardware and open-source neighborhood software application:

DeepSeek-Infer Demo: We supply a basic and lightweight 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 reasoning for local and cloud release.
TensorRT-LLM: Currently supports BF16 reasoning and INT4/8 quantization, with FP8 support 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 design on AMD GPUs by means of 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 offer FP8 weights. If you require BF16 weights for experimentation, you can use the supplied conversion script to carry out the transformation.

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 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 dependencies noted in requirements.txt. Easiest way is to use a bundle manager like conda or uv to produce 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 talk with DeepSeek-V3:

Or batch reasoning on a provided file:

6.2 Inference with SGLang (suggested)

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

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

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

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

Here are the launch guidelines 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 structure customized for large language models, now supports DeepSeek-V3. It offers both offline pipeline processing and online release abilities, seamlessly incorporating with PyTorch-based workflows.

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

6.4 Inference with TRT-LLM (advised)

TensorRT-LLM now supports the DeepSeek-V3 design, offering precision alternatives 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 specifically 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 (recommended)

vLLM v0.6.6 supports DeepSeek-V3 reasoning for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from standard strategies, vLLM uses pipeline parallelism allowing you to run this design on several machines linked by networks. For in-depth guidance, please refer to the vLLM instructions. Please feel totally free to follow the improvement plan too.

6.6 Recommended Inference Functionality with AMD GPUs

In cooperation with the AMD group, we have actually accomplished Day-One assistance for AMD GPUs using SGLang, with full compatibility for both FP8 and BF16 precision. For detailed guidance, please describe the SGLang guidelines.

6.7 Recommended Inference Functionality with Huawei Ascend NPUs

The MindIE structure from the Huawei Ascend neighborhood has successfully adjusted the BF16 variation of DeepSeek-V3. For step-by-step guidance on Ascend NPUs, please follow the guidelines here.

7. License

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