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Description

DeepInfer serves as an open-source framework designed for executing DeepSeek language models on Linux platforms that utilize compatible NVIDIA or Huawei Ascend accelerators. It encompasses a variety of features aimed at facilitating offline inference and efficient model execution, including optimized kernels, distributed parallelism, asynchronous batch scheduling, speculative decoding, and the generation of structured outputs in formats like JSON Schema, regular expressions, and EBNF. The project documentation covers support for DeepSeek-V4.1-Flash along with comprehensive guidance on building, configuring, and utilizing examples and evaluation tools. Aimed at developers and infrastructure teams, it assists in constructing or assessing self-managed inference deployments. Additionally, the repository highlights that for production services, there may be a need for supplementary components such as KV-cache, prefill/decode disaggregation, and load-balancing mechanisms that are not part of this particular release, ensuring users are aware of potential additional requirements as they implement the framework. Overall, DeepInfer is designed to optimize the deployment of language models in various contexts, addressing both fundamental and advanced operational needs.

Description

DeepSeek-V2 is a cutting-edge Mixture-of-Experts (MoE) language model developed by DeepSeek-AI, noted for its cost-effective training and high-efficiency inference features. It boasts an impressive total of 236 billion parameters, with only 21 billion active for each token, and is capable of handling a context length of up to 128K tokens. The model utilizes advanced architectures such as Multi-head Latent Attention (MLA) to optimize inference by minimizing the Key-Value (KV) cache and DeepSeekMoE to enable economical training through sparse computations. Compared to its predecessor, DeepSeek 67B, this model shows remarkable improvements, achieving a 42.5% reduction in training expenses, a 93.3% decrease in KV cache size, and a 5.76-fold increase in generation throughput. Trained on an extensive corpus of 8.1 trillion tokens, DeepSeek-V2 demonstrates exceptional capabilities in language comprehension, programming, and reasoning tasks, positioning it as one of the leading open-source models available today. Its innovative approach not only elevates its performance but also sets new benchmarks within the field of artificial intelligence.

API Access

Has API Yes 

API Access

Has API Yes 

Screenshots View All

Screenshots View All

Integrations

SiliconFlow No 

Integrations

SiliconFlow Yes 

Pricing Details

No price information available.
Free Trial No 
Free Version Yes 

Pricing Details

Free
Open source
Free Trial No 
Free Version Yes 

Deployment

Web-Based No 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows No 
Mac No 
Linux Yes 
Chromebook No 

Deployment

Web-Based No 
On-Premises Yes 
iPhone App No 
iPad App No 
Android App No 
Windows Yes 
Mac Yes 
Linux Yes 
Chromebook No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support No 

Customer Support

Business Hours No 
Live Rep (24/7) No 
Online Support No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Types of Training

Training Docs Yes 
Webinars No 
Live Training (Online) No 
In Person No 

Vendor Details

Company Name

DeepSeek

Founded

2023

Country

China

Website

github.com/deepseek-ai/DeepInfer

Vendor Details

Company Name

DeepSeek

Founded

2023

Country

China

Website

deepseek.com

Product Features

Alternatives

Alternatives

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