#GPU throughput
427 related articles
Industry Insights·3 min
China's Internet Giants Collectively Expand AI Capital Expenditure: Six Key Beneficiary Sectors in the Computing Infrastructure Supply Chain
China's internet giants collectively increase AI CapEx as computing infrastructure shifts from expectations to delivery. Analysis of six key beneficiary sectors including AI data centers, chips, and storage.
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Product Reviews·3 min
Moore Threads AI Coding Plan: A Fully Domestic AI Programming Service with 30-Day Free Trial
Moore Threads launches AI Coding Plan powered by its MTT S5000 GPU and GLM-4 code model, achieving full-stack domestic AI coding. Compatible with VS Code and Cursor, with a 30-day free trial.
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Industry Insights·3 min
Alibaba's $52B AI Investment: A Full-Stack Cloud Infrastructure Upgrade for the Agent Era
Alibaba invests $52B in AI cloud infrastructure over 3 years. Bailian Platform hits 8B yuan ARR with 11 quarters of triple-digit AI revenue growth. Deep analysis of Alibaba Cloud's full-stack Agent upgrade strategy.
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Industry Insights·3 min
Risks of AI Account Rotation Tools Exposed: Security Threats Behind the Gray Market
Deep dive into how AI quota-cracking tools work, exposing the legal, compliance, and data security risks behind account rotation gray markets, with legitimate alternatives like API pay-per-use and subscription upgrades.
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Research·2 min
Agent Loops in Practice: Transforming Token Output into Productivity from CUDA Kernels to Automated Research
Deep dive into how the Humanize framework transforms LLM tokens into engineering productivity via Agent Loops. Covers KDA winning CUDA kernel contests, virtual hardware optimization, and 50% research cost reduction.
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Tutorials·2 min
Tutorial: Deploying a PD-Disaggregated SGLang Multi-Node Inference Cluster on AMD GPUs
Learn how to deploy a PD-disaggregated SGLang inference cluster on AMD GPUs using a single config file, boosting LLM throughput and latency performance.
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Tech Frontiers·2 min
SGLang v0.5.12.post1 Released: DeepSeek V4 Stability Fixes and Blackwell Adaptation
SGLang v0.5.12.post1 stability patch details: 12 critical fixes covering DeepSeek V4 garbled text and crashes, NIXL PD disaggregated inference logic, Blackwell B300 adaptation, and cold start optimization.
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Tech Frontiers·2 min
LFM2.5-8B-A1B: A MoE Model with 1.5B Active Parameters Delivering 4x Its Weight Class Performance
Liquid AI releases LFM2.5-8B-A1B, a MoE model with 8B total params but only 1.5B active, matching 6B-class models in tool calling. Supports 128K context, local deployment, multilingual, with SGLang Day-0 support.
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Industry Insights·2 min
SGLang Enters Finance: How AI Inference Infrastructure Is Reshaping Wall Street
SGLang co-hosts a finance AI inference event with Crusoe AI and Cloudflare, exploring LLM inference deployment in trading, risk management, and compliance — signaling Wall Street's shift to production-grade AI infrastructure.
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Industry Insights·2 min
AMD MI355X Beats B200: Full-Stack Optimization Breakdown for 5% Lower TCO on DeepSeek-R1 Inference
AMD Instinct MI355X achieves 5% lower TCO than NVIDIA B200 on DeepSeek-R1 disaggregated inference via SGLang+MoRI full-stack optimization with 1.25x per-GPU throughput.
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Tech Frontiers·1 min
Cloudflare Contributes Critical KV Cache and Mooncake Fixes to SGLang
Cloudflare contributes decode KV cache offload and Mooncake recovery fixes to SGLang, resolving garbled output under high concurrency for Kimi K2.6 and enabling automatic fault recovery in distributed inference.
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Tech Frontiers·1 min
SGLang Hosts Agent Loops Office Hour, Focusing on Agentic Loop Architecture Optimization
SGLang team hosts an Agent Loops Office Hour exploring inference optimization for agentic loops, covering KV Cache reuse, low-latency multi-turn dialogue, and tool calling techniques.
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Industry Insights·3 min
AI Fully Automated Orchestration in Practice: How Software Production Costs Are Being Completely Disrupted
Deep analysis of AI fully automated software orchestration: from Claude Code workflows to parallel orchestration strategies, exploring how models like MiniMax M1 drive software production costs toward zero.
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Industry Insights·3 min
Deep Dive into Three Major LLM Career Paths: Requirements, Tech Stacks, and Career Prospects
Deep analysis of three core LLM roles—Application Engineer, Development Engineer, and Algorithm Engineer—covering technical requirements, salary thresholds, and career prospects including RAG, fine-tuning, and inference deployment.
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Industry Insights·2 min
Meta Partners with AWS: Bringing in Tens of Millions of Graviton Cores to Expand AI Infrastructure
Meta partners with AWS to add tens of millions of Graviton cores for AI inference, diversifying its infrastructure to support Meta AI and Agentic experiences for billions of users.
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Tutorials·2 min
Claude Code Sub-Agent: A Practical Guide to Multi-Agent Collaboration for Enhanced Development Efficiency
Deep dive into Claude Code Sub-Agent mechanism with a practical blog writing + Git commit case study, showing how multi-agent collaboration solves instruction loss and context bloat issues.
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Product Reviews·2 min
Kimi K2.6 Open-Source Hands-On: How Strong Is Its Orchestration of 300 Concurrent Agents?
Deep analysis of Moonshot AI's open-source Kimi K2.6 Agent orchestration: 300 sub-Agents executing 4000-step tasks, outperforming GPT-5.4 in coding benchmarks, LoRA fine-tuning on 2x RTX 4090s.
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Product Reviews·3 min
Local Deployment of Qwen 3.6 27B on 4×3080Ti: Real-World Coding Test with OpenCode
Real-world test of Qwen 3.6 27B FP8 deployed on 4×3080Ti 16GB modded GPUs with OpenCode for system tool development. Covers hardware setup, inference speed, context management, and productivity gains.
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Tutorials·3 min
Decoding LLM Naming Conventions: Parameter Counts, Quantization Formats & VRAM Requirements Quick Reference
Decode LLM naming conventions, understand 32B parameters & AWQ/GGUF quantization formats, with 4-bit VRAM estimation formulas, MOE model pitfalls, and model selection by GPU tier.
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Product Reviews·2 min
AI Coding Appliance vs Cloud LLMs: Can ¥480K in Annual Fees Buy 4 Local Deployment Solutions?
A deep cost comparison between AI coding appliances and cloud LLM APIs. A 20-person team spending ¥480K/year on tokens can deploy 4 local OnePanel units at ¥99K each, breaking even in 2.5 months.
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