Writing Pallas Kernels for JAX: Stepping Outside the XLA Safety Net
When XLA's heuristics fail for custom attention mechanisms, you can't just hope for a compiler update. Here is how you write Triton-like kernels directly in Python using JAX Pallas.
Tag: JAX
Speculative Decoding: Cheating Physics for Latency
Using a 'Draft' model costs 10% more VRAM but saves 50% Latency. Here is the mechanics of the gamble.
MoE Routing Collapse: When Your Specialists Stop Specializing
A model is only as smart as its router. We explore the physics of expert zones, the tax of token dropping, and how to keep your load balancer honest.
JAX XLA: Why Your GPU is Idle 40% of the Time
Recompilation is the silent killer of training throughput. If you see 'Jit' in your profiler, you are losing money. We dive into XLA internals.
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.
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.