We provide DeepSeek-V3, a strong Mixture-of-Experts (MoE) language model with 671B total specifications with 37B activated for each token. To attain efficient inference and cost-efficient 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 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 varied and top quality tokens, followed by Supervised Fine-Tuning and Reinforcement Learning stages to totally harness its abilities. Comprehensive examinations expose that DeepSeek-V3 exceeds other open-source designs and accomplishes performance similar to leading closed-source designs. Despite its excellent performance, DeepSeek-V3 needs only 2.788 M H800 GPU hours for its full training. In addition, its training procedure is extremely steady. 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 minimizes the performance destruction that emerges from motivating load balancing.
- We examine a Multi-Token Prediction (MTP) goal and prove it beneficial to design efficiency. It can likewise be utilized for speculative decoding for inference velocity.

Pre-Training: Towards Ultimate Training Efficiency

- We develop an FP8 mixed accuracy 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 conquer the interaction traffic jam in cross-node MoE training, nearly accomplishing complete computation-communication overlap.
This significantly improves our training performance and decreases the training costs, enabling us to even more scale up the model 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 currently greatest open-source base model. The subsequent training phases after pre-training require just 0.1 M GPU hours.

Post-Training: Knowledge Distillation from DeepSeek-R1

- We introduce an innovative methodology to distill thinking abilities 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 incorporates the confirmation and reflection patterns of R1 into DeepSeek-V3 and especially improves its thinking efficiency. Meanwhile, we also keep a control over the output style and length of DeepSeek-V3.

3. Model Downloads

The overall size of DeepSeek-V3 designs on Hugging Face is 685B, which includes 671B of the Main Model weights and 14B of the Multi-Token Prediction (MTP) Module weights. **

To ensure optimal performance and versatility, we have actually partnered with open-source communities and hardware suppliers to offer numerous methods to run the model 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 information on the Main Model weights and the Multi-Token Prediction (MTP) Modules. Please note that MTP assistance is currently under active advancement within the neighborhood, and we invite your contributions and feedback.

4. Evaluation Results

Base Model

Standard Benchmarks

Best results are displayed in vibrant. Scores with a gap not going beyond 0.3 are thought about to be at the same level. DeepSeek-V3 accomplishes the very best performance on the majority of benchmarks, specifically on mathematics and code jobs. For more evaluation 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 up to 128K.

Chat Model

Standard Benchmarks (Models larger than 67B)

All designs are assessed in a setup that restricts the output length to 8K. Benchmarks consisting of fewer than 1000 samples are checked numerous times using differing temperature level settings to obtain robust last outcomes. DeepSeek-V3 stands as the best-performing open-source design, and also exhibits competitive performance versus frontier closed-source models.

Open Ended Generation Evaluation

English open-ended conversation examinations. 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 provide 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 application:

DeepSeek-Infer Demo: We provide an easy and lightweight demo for FP8 and BF16 reasoning.
SGLang: Fully support the DeepSeek-V3 model in both BF16 and FP8 inference modes, with Multi-Token Prediction coming quickly.
LMDeploy: Enables effective FP8 and BF16 reasoning for local and cloud release.
TensorRT-LLM: Currently supports BF16 inference and INT4/8 quantization, with FP8 support coming quickly.
vLLM: Support DeepSeek-V3 model with FP8 and BF16 modes for tensor parallelism and pipeline parallelism.
AMD GPU: Enables running the DeepSeek-V3 design on AMD GPUs through SGLang in both BF16 and FP8 modes.
Huawei Ascend NPU: Supports running DeepSeek-V3 on Huawei Ascend gadgets.
Since FP8 training is natively adopted in our structure, we just offer FP8 weights. If you need BF16 weights for experimentation, you can utilize the supplied conversion script to carry out the change.

Here is an example of transforming FP8 weights to BF16:

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

6.1 Inference with DeepSeek-Infer Demo (example just)

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 method is to utilize a bundle supervisor 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 model weights to a specific format:

Run

Then you can talk with DeepSeek-V3:

Or batch reasoning on an offered file:

6.2 Inference with SGLang (recommended)

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

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

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

Here are the launch directions from the SGLang group: https://github.com/sgl-project/sglang/t … eepseek_v3

6.3 Inference with LMDeploy (advised)

LMDeploy, a versatile and high-performance reasoning and serving framework customized for big language models, now supports DeepSeek-V3. It provides both offline pipeline processing and online implementation abilities, perfectly integrating with PyTorch-based workflows.

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

6.4 Inference with TRT-LLM (recommended)

TensorRT-LLM now supports the DeepSeek-V3 design, providing accuracy choices such as BF16 and INT4/INT8 weight-only. Support for FP8 is presently in progress and will be released quickly. You can access the custom-made branch of TRTLLM particularly for DeepSeek-V3 assistance through the following link to experience the brand-new features directly: https://github.com/NVIDIA/TensorRT-LLM/ … epseek_v3.

6.5 Inference with vLLM (recommended)

vLLM v0.6.6 supports DeepSeek-V3 inference for FP8 and BF16 modes on both NVIDIA and AMD GPUs. Aside from standard strategies, vLLM uses pipeline parallelism allowing you to run this model on numerous machines linked by networks. For in-depth assistance, please refer to the vLLM directions. Please feel complimentary to follow the improvement strategy also.

6.6 Recommended Inference Functionality with AMD GPUs

In partnership with the AMD team, we have accomplished Day-One support for AMD GPUs utilizing SGLang, with complete compatibility for both FP8 and BF16 precision. For in-depth guidance, please describe the SGLang directions.

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 step-by-step assistance on Ascend NPUs, please follow the directions here.

7. License

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