Average Ratings 0 Ratings
Average Ratings 0 Ratings
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.
Description
Tensormesh serves as an innovative caching layer designed for inference tasks involving large language models, allowing organizations to capitalize on intermediate computations, significantly minimize GPU consumption, and enhance both time-to-first-token and overall latency. By capturing and repurposing essential key-value cache states that would typically be discarded after each inference, it eliminates unnecessary computational efforts and achieves “up to 10x faster inference,” all while substantially reducing the strain on GPUs. The platform is versatile, accommodating both public cloud and on-premises deployments, and offers comprehensive observability, enterprise-level control, as well as SDKs/APIs and dashboards for seamless integration into existing inference frameworks, boasting compatibility with inference engines like vLLM right out of the box. Tensormesh prioritizes high performance at scale, enabling sub-millisecond repeated queries, and fine-tunes every aspect of inference from caching to computation, ensuring that organizations can maximize efficiency and responsiveness in their applications. In an increasingly competitive landscape, such enhancements provide a critical edge for companies aiming to leverage advanced language models effectively.
API Access
Has API
Yes
API Access
Has API
Yes
Integrations
SiliconFlow
No
Pricing Details
Free
Open source
Free Trial
No
Free Version
Yes
Pricing Details
No price information available.
Free Trial
No
Free Version
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
Deployment
Web-Based
Yes
On-Premises
Yes
iPhone App
No
iPad App
No
Android App
No
Windows
No
Mac
No
Linux
No
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
Yes
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)
Yes
In Person
No
Vendor Details
Company Name
DeepSeek
Founded
2023
Country
China
Website
deepseek.com
Vendor Details
Company Name
Tensormesh
Founded
2025
Country
United States
Website
www.tensormesh.ai/