CUDA engineers now supervise the AI that writes their GPU kernels
CUDA engineers, among tech's most sought-after specialists, increasingly supervise AI that writes and tests hundreds of GPU kernels. At base salaries Nvidia advertises as high as $431,250, employers are now paying for judgement about code the engineers did not write.
Reality
- Evidence50
- Adoption30
- Hype gap+35
- Incentives70
- Confidence45
buildOne report1 publisher vLLM measured its portability layer at 3.4 percent below native throughput on an H100
PyTorch says vLLM's frontier models now ship as hardware-specific flat definitions that torch.compile cannot trace, and the new HW agnostic layers are what users on other accelerators get instead. The overhead figure came from an H100.
Reality
- Evidence55
- Adoption45
- Hype gap+10
- Incentives60
- Confidence55
buildConfirmed2 publishers Google and DeepMind's method records a live search, replays thousands of exploration policies against the stored results, and sends only the winner into the next run. No weights are retrained.
Reality
- Evidence55
- Adoption12
- Hype gap+20
- Incentives68
- Confidence58
buildOne report1 publisher Meta's MXFP8 FlashAttention-4 kernel, tackling TMEM scale-factor constraints, hits up to 1.6x over BF16
Meta has open-sourced an MXFP8 forward and backward pass for FlashAttention-4 that it already runs in production ads training. The reported 1.6x forward gain over BF16 sits well under the 2-4x the block-scaled MMA instruction advertises.
Reality
- Evidence58
- Adoption47
- Hype gap+12
- Incentives55
- Confidence60
A DeepSeek engineer says AI will probably match or surpass the kernels he writes by hand
Liu Shengyu, who helped write a component of DeepSeek's latest model, wrote that AI-written operators will probably be as good as his own or better. He expects to keep his job and to lose the hands-on part of it.
Reality
- Evidence48
- Adoption20
- Hype gap+24
- Incentives66
- Confidence57
buildOne report1 publisher NVIDIA's unoptimized DeepSeek-V3 baseline spends 84% of kernel time moving tokens between GPUs
A JAX and Transformer Engine version of the same GB200 run reports 1,068 TFLOPS per GPU, a 10.4x gain. The 84% share bounds what fixing communication alone could have bought, so the expert matmuls got faster too.
Reality
- Evidence45
- Adoption
- Not scored
- Hype gap+35
- Incentives85
- Confidence55
buildOne report1 publisher Base Compute hands kernel tuning to agents; the carryover claim is the unmeasured part
Its August 20 bake-off priced three agents on one Nemotron port. The claim that findings transfer to the next model-chip pair is the one with no number attached.
Reality
- Evidence42
- Adoption15
- Hype gap+28
- Incentives80
- Confidence45
buildConfirmed4 publishers A five-person Nvidia team published the architecture behind its AVO agent alongside a perfect public-set score. The model did not change. The system around it did.
Perspective Coverage
4 publishers
- Builder
- Builder 50%
- Operator
- Operator 29%
- Investor
- Investor 21%
Reality
- Evidence58
- Adoption20
- Hype gap+34
- Incentives82
- Confidence72
buildOne report1 publisher 1,500 submissions in 14 days: what a 12th-place GPU kernel says about agent loops
Sankalp placed 12th of 183 on GPU Mode's B200 QR benchmark using OpenAI Codex. The transferable part is the checker, not the ranking.
Reality
- Evidence45
- Adoption22
- Hype gap+12
- Incentives58
- Confidence52