Compiling TensorRT Engines: The Calibration Trap
When aggressive INT8 quantization goes horribly rogue because of unrepresentative calibration data, and precisely how the blind pursuit of hyper efficiency can utterly destroy the end user experience.
Tag: gpu
The Storage Wall: Why Your GPUs are Waiting on GCS
Buying expensive GPUs to wait on cheap storage is an operational failure. We break down the math of 'Badput' and why high-performance I/O is actually a discount.
· StrategySpot Market Arbitrage for AI: The Economics of Fault Tolerance
If your training loop isn't fault-tolerant, you're paying a 40% 'insurance tax' to your cloud provider. We look at the architectural cost of 30-second preemption notices.
Visualizing All-Reduce Bandwidth: The Physics of Distributed Training
When your model doesn't fit on one GPU, you're no longer just learning coding-you're learning physics. We dive deep into the primitives of NCCL, distributed collectives, and why the interconnect is the computer.
The Compute-to-Cashflow Gap
The AI industry is shifting from celebrating large compute budgets to hunting for efficiency. Your competitive advantage is no longer your GPU count, but your cost-per-inference.
NCCL Debugging & Tuning: NCCL_ALGO (Ring, Tree, CollNet)
NCCL debugging is critical for distributed training bottlenecks. Learn to set NCCL_DEBUG, tune the NCCL_ALGO environment variable for Ring, Tree, or CollNet, and troubleshoot GPU network failures.