“Rigorous benchmarks have driven progress in autonomous GPU kernel performance optimization… but no equivalent exists for TPUs.”
It’s a damning admission, really. While the NVIDIA ecosystem has spent years building a playground where developers can iterate on kernels with actual feedback, the TPU world has essentially been “trust us, it’s fast.” For the better part of a decade, optimizing for Google’s hardware has felt less like engineering and more like trying to solve a Rubik’s cube in a dark room. You change a line of code, wait for the compiler to finish its magic, and then pray the throughput actually went up.
The frustration isn’t just about the lack of tools; it’s about the culture of opacity. In the GPU world, if a kernel is slow, you can profile it, find the bottleneck, and fix it. In the TPU world, you’re often just staring at a wall of XLA logs that look like they were written by a confused alien. We’ve been conditioned to accept this because the raw TFLOPs are impressive, but raw power is useless if you can’t actually steer it toward a specific workload without a PhD in Google’s internal compiler architecture.
The problem is that the TPU is a black box wrapped in a riddle, wrapped in a proprietary API. If you’re working in JAX, you’re mostly at the mercy of XLA. But XLA is a general-purpose compiler; it’s not always the most efficient way to implement a specific, weird operation that only exists in your niche research paper. When GPU developers hit a wall, they write a custom CUDA kernel. When TPU developers hit a wall, they usually just buy more TPU pods.
Tuning a TPU kernel without a benchmark is like trying to bake a cake where the oven temperature is a secret and the timer is broken. You just keep trying different settings and hoping the result isn’t a charred mess. (And God knows we’ve all spent too many hours staring at XLA HLO).
Enter JAXBench. The idea here isn’t just to provide a set of tests, but to create a framework for autonomous kernel optimization. We’re talking about AI writing the code to make the AI hardware run faster. Who actually enjoys writing TPU kernels by hand? The process is tedious, error-prone, and frankly, a bit miserable.
The lack of a standardized benchmark for TPU kernels has been a massive drag on the ecosystem. It’s a structural failure. By failing to provide a way to measure incremental gains, Google essentially ensured that only a handful of people—the “high priests” of TPU optimization—actually knew how to squeeze the hardware for every drop of performance.
By providing a “shared target to hillclimb on,” JAXBench finally gives LLMs—the ones tasked with writing the kernels—a way to verify their work. The loop is simple: the AI proposes a kernel, the benchmark tests it on actual TPU hardware, and the result is fed back into the AI to refine the code. This is the same loop that has already made GPU kernel generation significantly more viable.
But let’s be honest about the friction here. TPU access is still a gated community. Unless you’re at a Tier-1 lab or have a massive GCP credit line, you can’t just spin up a cluster to see if your JAXBench results hold. Even then, the latency of the compile-test-repeat cycle is enough to make anyone want to quit. You’re not just fighting the code; you’re fighting the cloud infrastructure.
Still, the shift toward autonomous optimization is the only logical path. The complexity of modern accelerators has officially outpaced the human ability to hand-tune them. We’ve reached a point where the hardware is too intricate for a human to optimize efficiently without spending six months in a rabbit hole of memory alignment and tiling strategies.
The era of the human TPU expert is over.
If we can actually automate the kernel-writing process, the bottleneck shifts from “how do I make this fast” to “what do I actually want to compute.” The real victory here isn’t the benchmark itself, but the admission that we need machines to optimize the machines. It’s a surrender, but it’s a practical one.
Within six months, we will see a public leaderboard for autonomous TPU kernels that forces Google to actually release more detailed hardware specs to keep the competition honest. Until then, we’re just guessing.
