Stuart (Stu)

Researcher, Stanford & Cursor · 1 appearance on the record.

computed by AI from the episodes · how this works → · full disclaimer →

8statements → 7claims → 7claims resolved → 100%fully supported → 4/5average certainty → 1.75/5average debate potential →

7 supported 0 partly supported 0 contradicted how the 7 claims stand · each chip opens the sources

1 prediction · 6 assertions · 1 insight · every statement was checked. The prediction and assertions are the 7 claims: statements the public record can support or contradict. 7 are resolved. Everything else (opinions, insights, what ifs, disclosures) can never be settled by the record, so it carries no assessment.

The record, in short

What the tape says about how Stuart argues and how the claims held up. Everything they said, and everything said about them, is in the tabs below.

Their most notable supported claim

Assertion Supported
Stuart: Multi-GPU compilers often produce kernels slower than baseline
“Two is to use compiler-based approaches, but we find these compilers, compilers to be quite suboptimal. They produce kernels that are sometimes slower than non-overlapped baselines”
Stuart (Stu) Jul 29, 2026 ▶ 10:43 Multi-GPU Kernels, Intelligence per Watt, Heterogeneous Inference, and More | YC Paper Club · Y Combinator

Everything Stuart (Stu) said on the Y Combinator Startup Podcast that made the record, most notable first. Filter by type, assessment or year in the ledger →

Assertion Supported
Stuart: Multi-GPU compilers often produce kernels slower than baseline
“Two is to use compiler-based approaches, but we find these compilers, compilers to be quite suboptimal. They produce kernels that are sometimes slower than non-overlapped baselines”
Stuart (Stu) Jul 29, 2026 ▶ 10:43 Multi-GPU Kernels, Intelligence per Watt, Heterogeneous Inference, and More | YC Paper Club · Y Combinator
Assertion Supported
Stuart: Bypassing NCCL intermediate buffers speeds up all-reduce by 80%
“For example, Nickel's default mode forces intermediate buffers which adds extra data movement between the sender and the receiver. And for fine-grained communication, this overhead really accumulates, and by stripping it out, you can speed up an operation as s…”
Stuart (Stu) Jul 29, 2026 ▶ 18:36 Multi-GPU Kernels, Intelligence per Watt, Heterogeneous Inference, and More | YC Paper Club · Y Combinator
Assertion Supported
Stuart: ParallelKittens matches hand-optimized kernels in 50 to 100 lines
“And what we find is that with roughly 50 to 100 lines of device code, PK is able to surpass or match hand-optimized kernels that are often hundreds to thousands lines of code.”
Stuart (Stu) Jul 29, 2026 ▶ 20:38 Multi-GPU Kernels, Intelligence per Watt, Heterogeneous Inference, and More | YC Paper Club · Y Combinator
Assertion Supported
Stuart: GPU networking can consume up to 50% of LLaMA pre-fill runtime
“For example, networking can still consume up to 50% of total runtime for workloads like Lama's MDB pre-fill.”
Stuart (Stu) Jul 29, 2026 ▶ 8:30 Multi-GPU Kernels, Intelligence per Watt, Heterogeneous Inference, and More | YC Paper Club · Y Combinator
Prediction Held up
Stuart: Single NVLink domains will soon scale to hundreds of GPUs
“And you also have a scale of architectures like NBL-L-Seventy-Two, which packs 72 GPUs inside a single NVLink domain, and soon this is going to extend to hundreds of GPUs.”
Stuart (Stu) Jul 29, 2026 ▶ 9:43 Multi-GPU Kernels, Intelligence per Watt, Heterogeneous Inference, and More | YC Paper Club · Y Combinator
Assertion Supported
Stuart: Cursor uses ParallelKittens on tens of thousands of Blackwell GPUs
“For example, Cursor is using it to train Composer on tens of thousands of Blackwell GPUs.”
Stuart (Stu) Jul 29, 2026 ▶ 20:57 Multi-GPU Kernels, Intelligence per Watt, Heterogeneous Inference, and More | YC Paper Club · Y Combinator
Insight
Stuart: Multi-GPU optimization requires overlapping compute with remote memory prefetching
“For multi-GPU kernels a simple, a similar idea applies, except that you're overlapping computation with communication with other GPUs, such that when the current computation is done, the data for next computation is ready and fetched from remote GPU HPMs.”
Stuart (Stu) Jul 29, 2026 ▶ 14:02 Multi-GPU Kernels, Intelligence per Watt, Heterogeneous Inference, and More | YC Paper Club · Y Combinator
Assertion Supported
Stuart: TMA saturates NVLink on Blackwell using roughly 15 SMs
“So on Blackwell, we find that with roughly 15 SMs out of one 48 TMA is able to saturate the NVLink.”
Stuart (Stu) Jul 29, 2026 ▶ 15:27 Multi-GPU Kernels, Intelligence per Watt, Heterogeneous Inference, and More | YC Paper Club · Y Combinator

Appearances (1)

EpisodeDateSpeaking time
Multi-GPU Kernels, Intelligence per Watt, Heterogeneous Inference, and More | YC Paper Clu Jul 29, 2026 13m
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