Prefill-decode disaggregation separates the two phases of LLM inference — prompt processing and token generation — onto dedicated GPU pools, eliminating the head-of-line blocking that causes latency spikes under concurrent load. Production deployments report 1.5x to 2.5x throughput gains, with cache-aware variants like Together AI’s CPD pushing improvements to 40%. …
Editorial team 
Terraform by AI: 5% Today, 90% by 2029, No Guardrails
Gartner published its first-ever Market Guide for AI Assistants for Infrastructure as Code in March 2026, projecting that 90% of I&O organizations will integrate context-aware AI assistants into their IaC workflows — generating Terraform, remediating drift, and provisioning environments — by 2029, up from just 5% today (Firefly). A second …
vLLM vs SGLang: Which Engine Actually Wins in 2026?
On H100 SXM5 80GB running Llama 3.3 70B Instruct at FP8, SGLang serves 1,920 tokens per second at 50-way concurrency — just 3.8% faster than vLLM’s 1,850. But swap to Llama 3.1 8B, and that gap explodes to 29%: SGLang hits 16,200 tok/s versus vLLM’s 12,500. The inference engine you …
73% of RAG Failures Start Before the LLM Sees Your Query
The Retrieval Wall Nobody Monitors Industry analysis in 2026 consistently shows that when RAG fails, the failure point is retrieval 73% of the time, not generation (Lushbinary). Your LLM is fine. Your chunking strategy, your retrieval count, and your embedding freshness are not. Every team that ships a RAG system …
AI Agent Testing Misses 4 of 7 Failure Modes Before Prod
$47K Fraudulent Refund Exposed Testing Gaps In January 2026, a prompt injection in a customer support agent processed a $47,000 fraudulent refund. The agent had passed every demo test. It handled happy-path conversations flawlessly. Then someone fed it external content with embedded instructions, and the system complied without hesitation. According …
GPU Schedulers Waste 38% Time on Agent Cache Regeneration
Agent Cache Rebuilds Waste 38% GPU When researchers at the University of Hong Kong instrumented a 32-GPU A100 cluster running SWE-bench coding agents on vLLM v0.6.0, they found a number that should bother every platform engineer: 38% of total execution time was spent regenerating KV cache that had been discarded …
Serverless GPU Cold Starts Take 40s – Here’s How to Fix
The 1000x Latency Gap A cold-start instance on a serverless GPU platform produces its first token after more than 40 seconds. A warm instance generates subsequent tokens in roughly 30 milliseconds. That is a latency ratio of over 1,300:1 between the cold and warm states, and it is the single …
Anthropic Launches Fable 5: Public Mythos-Class Model
Anthropic launched Claude Fable 5 and Claude Mythos 5 today — a Mythos-class model that tops nearly every benchmark. Fable 5 is available to the public via API and Amazon Bedrock at $10/M input and $50/M output tokens, less than half the price of Mythos Preview. Mythos 5, the unrestricted …
Cloud Egress Fees Now Surpass GPU Compute Costs for AI
Egress Fees Outpace GPU Cost AWS charges $0.09 per GB for data transfer out to the internet. A single RAG pipeline processing 10,000 queries daily with 50 KB embedding payloads per request generates roughly 15 TB of egress per month — that is $1,350 before you factor in vector DB …
Google I/O 2026: How AI Agents Replaced the Search Box
Google replaced its 25-year-old search box with an AI-powered interface at I/O 2026. The new “intelligent search box” accepts text, images, files, video, and Chrome tabs, powered by Gemini 3.5 Flash. Instead of blue links, users get interactive AI-generated experiences, custom visualizations, and “information agents” that monitor the web around …
LLM Gateways Cut 72% of Wasted API Spend in Production
Wasted LLM Spend: The Gateway Fix Enterprise LLM API spend crossed $8.4 billion in 2025, and the majority of teams hardcode a single frontier model for every request — including the 80% that could run on a model costing one-tenth the price. LLM gateways fix this systematically. A workload of …
Function Calling Accuracy Plummets in Production Workflows
Benchmarks Claim 95%. Production Disagrees. The Berkeley Function Calling Leaderboard (BFCL V4) reports that GPT-4o achieves over 90% accuracy on single-function tool calls. Add a second tool to the context, and accuracy drops by double digits. Add five, and you’re in a different regime entirely. The gap between benchmark function …