Smaller, faster, safer: running Kimi and GLM at scale
This article discusses technical strategies for scaling AI models like Kimi and GLM, focusing on inference and quantization. It provides a broad overview of infrastructure considerations for developers working with large language models.
Why it matters
Efficient inference is critical for the economic and technical viability of deploying large-scale AI applications in production environments.
Blog Agents Week AI Developers +3 Show 3 more tags 6 Tags Show 6 tags Selected Tags Agents Week AI Developers Inference Quantization Workers AI All tags Matching tags No tags found 1.1.1.1 2FA Abuse Access Access Control Lists (ACLs) Accessibility Account Takeover Acquisitions Addressing Advanced Certificate Manager Advanced DDoS Advertising Aegis Africa Afroflare Agent Readiness Agents Agents Week Agents Week 2026 AI AI Bots AI Gateway AI Search AI-SPM AI WAF AI Week Alertmanager Always Online AMD AMP Analytics Anonymous Anti Malware Anycast API API Gateway API Security API Shield APJC Apple Application Security Application Services Area 1 Security Argo Smart Routing ASCII Asia Athenian Project Atlassian Attacks Audit Logs Austin Australia Authentication Authy Automatic HTTPS Automatic Platform Optimization Automation AutoMinify Auto Rag Awards AWS Baidu Bandwidth Alliance Bandwidth Costs Best Practices Beta Better Internet BGP Billing Birthday Week Blackbird Black Friday Bot Fight Mode Bot Management Botnet Bots…
Get smarter about the news
Sign up free for a feed built around what you actually care about, Dive Deeper research on any story, and the full text of every article.
Create free accountAlready have an account? Sign in