Debugging NCCL Ring Failures
When standard tools report a healthy cluster, but your training is stalled, the culprit is often a broken ring topology. We decode specific NCCL algorithms and debugging flags.
From the Front Lines of Tech
Sharing strategic insights and lessons from over 20 years of building scalable systems, leading high-performing teams, and navigating complex technology shifts.
MCP: The End of the API Wrapper
We analyze the JSON-RPC internals of the Model Context Protocol (MCP) and why the 'Context Exchange' architecture renders traditional integration code obsolete.
Network Jitter: The Silent Killer of Training
In distributed training, the slowest packet determines the speed of the cluster. We benchmark GCP's 'Circuit Switched' Jupiter fabric against AWS's 'Multipath' SRD protocol.
Blackwell's Sparse Attention Engines: The Reality of FP4
FP4 isn't just 'lower precision' - it requires a fundamental rethink of activation outliers. We dive into the bit-level implementation of NVFP4, Micro-Tensor Scaling, and the new Tensor Memory hierarchy.
· StrategyThe Reliability Tax: Why Cheap GPUs Cost More
In the Llama 3 training run, Meta experienced 419 failures in 54 days. This post breaks down the unit economics of 'Badput' - the compute time lost to crashes - and why reliability is the only deflationary force in AI.
The Efficiency Moat - Navigating the New Economics of AI Inference
As the AI industry moves from model training to large-scale deployment, the strategic bottleneck has shifted from parameter count to inference orchestration. This post explores how advanced techniques like RadixAttention, Chunked Prefills, and Deep Expert Parallelism are redefining the ROI of GPU clusters and creating a new standard for high-performance AI infrastructure.