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Description
GigaChat 3 Ultra redefines open-source scale by delivering a 702B-parameter frontier model purpose-built for Russian and multilingual understanding. Designed with a modern MoE architecture, it achieves the reasoning strength of giant dense models while using only a fraction of active parameters per generation step. Its massive 14T-token training corpus includes natural human text, curated multilingual sources, extensive STEM materials, and billions of high-quality synthetic examples crafted to boost logic, math, and programming skills. This model is not a derivative or retrained foreign LLM—it is a ground-up build engineered to capture cultural nuance, linguistic accuracy, and reliable long-context performance. GigaChat 3 Ultra integrates seamlessly with open-source tooling like vLLM, sglang, DeepSeek-class architectures, and HuggingFace-based training stacks. It supports advanced capabilities including a code interpreter, improved chat template, memory system, contextual search reformulation, and 128K context windows. Benchmarking shows clear improvements over previous GigaChat generations and competitive results against global leaders in coding, reasoning, and cross-domain tasks. Overall, GigaChat 3 Ultra empowers teams to explore frontier-scale AI without sacrificing transparency, customizability, or ecosystem compatibility.
Description
ZeroGPU serves as a compute efficiency layer tailored for AI inference, enabling AI applications to minimize their inference costs by shifting high-volume tasks to dedicated models within an edge-powered inference network. This solution is founded on the principle that many production-level AI tasks do not necessitate advanced reasoning capabilities; instead, activities like document analysis, content summarization, page classification, signal extraction, PII detection, web content processing, query routing, and message moderation can generally be handled effectively by smaller, task-oriented models rather than costly frontier models. By utilizing ZeroGPU, developers can pinpoint workloads that lack the need for deep reasoning and efficiently direct them to specialized small language models and nano models. This process involves executing these tasks across optimized servers, leveraging approved edge capacity and cloud fallback, while also providing a framework to assess cost savings, improvements in latency, reduction in reliance on frontier-model calls, and overall model performance. In doing so, ZeroGPU not only enhances operational efficiency but also contributes to the broader accessibility of AI technologies.
API Access
Has API
API Access
Has API
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Pricing Details
Free
Free Trial
Free Version
Pricing Details
No price information available.
Free Trial
Free Version
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Deployment
Web-Based
On-Premises
iPhone App
iPad App
Android App
Windows
Mac
Linux
Chromebook
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Customer Support
Business Hours
Live Rep (24/7)
Online Support
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Types of Training
Training Docs
Webinars
Live Training (Online)
In Person
Vendor Details
Company Name
Sberbank
Founded
1841
Country
Russia
Website
giga.chat
Vendor Details
Company Name
ZeroGPU
Founded
2025
Country
United States
Website
zerogpu.ai/