Can LLMs Write Fast Multi-GPU Kernels? — Simran Arora, Together AI

Between NVIDIA's A100 in 2020 and the B200 in 2024, BF16 tensor core throughput improved 7.2x.

Intra node communication improved 3x, and inter node communication only 2x. That widening gap has pushed the bottleneck in large AI workloads off the individual GPU and onto the links between them, far enough that a standard PyTorch and NCCL baseline lands below 50% of its communication aware roofline on most problems. Simran Arora leads the frontier performance research team at Together AI, and her group's answer is ParallelKittens, a small set of primitives that adds roughly a dozen lines to a single GPU kernel and now runs in production at Together AI and Cursor. The harder question was whether models can apply the same principles. ParallelKernelBench hands a model an unoptimized PyTorch reference and a topology spec across 87 problems drawn from real repositories, then asks for a CUDA kernel that moves data directly over NVLink. The best frontier model solved 28 of them zero shot, with 22 beating the baseline. Drawing more samples lifts correctness to 36, but the share that is both correct and faster stalls near 31%.

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