Editorial illustration for vLLM uses custom GPU kernels, TorchInductor and CUTLASS for portable inference
vLLM uses custom GPU kernels, TorchInductor and CUTLASS...
Portable inference across diverse hardware is a brutal optimization problem. vLLM attacks it with a triple threat: custom GPU kernels for raw performance, TorchInductor for graph-level fusion, and battle-tested GEMM libraries like CUTLASS and DeepGEMM. But writing those kernels has meant descending into CUDA’s low-level trenches, until now.
Helion reimagines the kernel authoring experience as a tile-programming DSL that feels like PyTorch with tiles. It gives developers both natural syntax and fine-grained control over memory layout, tiling, and scheduling. And its ahead-of-time autotuning infrastructure automatically navigates a vast configuration space to pick the winning implementation for any workload and target platform.
The result: vLLM’s inference becomes portable without sacrificing the efficiency that made it fast in the first place.
Internally, vLLM relies heavily on custom GPU kernels, TorchInductor fusion, and optimized GEMM backends such as CUTLASS and DeepGEMM to achieve high inference efficiency across different hardware platforms.
The real promise here isn’t just speed, it’s portability without sacrifice. vLLM already proved that custom kernels, TorchInductor fusion, and optimized GEMM backends like CUTLASS can push inference to its limits. Helion takes that momentum and makes it adaptable.
You don’t have to rewrite for every GPU. You don’t have to choose between developer experience and raw throughput. The tile-programming model bridges that gap, giving you PyTorch syntax with CUDA-level control.
And the AOT autotuning? That’s the cheat code. It explores the configuration space you’d never have time to touch, then locks in the optimal kernel for your exact hardware and workload.
The result is inference that stays fast, on today’s accelerators, tomorrow’s, and everything in between. This isn’t just a framework update. It’s a blueprint for how portable, high-performance AI should work.
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Common Questions Answered
What are the three main components vLLM uses to optimize portable inference across different hardware?
vLLM uses custom GPU kernels for raw performance, TorchInductor for graph-level fusion, and optimized GEMM libraries like CUTLASS and DeepGEMM. This triple approach allows vLLM to achieve high performance while maintaining portability across diverse GPU architectures without requiring hardware-specific rewrites.
How does Helion's tile-programming DSL improve the kernel authoring experience compared to traditional CUDA development?
Helion reimagines kernel authoring as a tile-programming DSL that feels like PyTorch, eliminating the need to descend into CUDA's low-level trenches. This approach gives developers natural syntax and intuitive programming patterns while still providing CUDA-level control over GPU operations.
What is the main advantage of vLLM's approach to portable inference without sacrificing performance?
vLLM's approach eliminates the traditional trade-off between developer experience and raw throughput by combining custom kernels, TorchInductor fusion, and optimized GEMM backends. Developers no longer need to rewrite code for every GPU or choose between ease of use and performance, as the tile-programming model bridges this gap.
Why is portable inference across diverse hardware considered a difficult optimization problem?
Portable inference across diverse hardware is challenging because different GPU architectures require specialized optimizations and custom implementations. vLLM addresses this brutal optimization problem by providing a unified framework that works across various hardware platforms without requiring developers to rewrite their code for each specific GPU type.
Further Reading
- Introduction to torch.compile and How It Works with vLLM — vLLM Blog
- Generating State-of-the-Art GEMMs with TorchInductor's CuteDSL Backend — PyTorch Blog
- Run High-Performance LLM Inference Kernels from NVIDIA Using FlashInfer — NVIDIA Developer Blog
- GPU - vLLM Documentation — vLLM Documentation
- Using NVIDIA CUTLASS for High-Performance Inference — NVIDIA Developer
