Observed Signal · May 26, 2026 · Technical Tutorial · Source: DEV Community · Impact: 2/5 · Sentiment: Positive
TileLang for High-Performance GPU Kernels
This technical tutorial introduces TileLang, a Python-first DSL for writing high-performance GPU kernels that balances the high-level convenience of Triton with the low-level control of CUTLASS/CuTe. TileLang exposes tiles as first-class objects, requires explicit buffer placement (shared memory, registers), and relies on a layout-inference pass to derive thread mappings and memory layouts. The article walks through a GEMM example, an MLA (Multi‑Head Latent Attention) decode kernel (DeepSeek) where TileLang's layout inference enables a compact ~80-line implementation matching FlashMLA H100 fp16 performance, and a production RMSNorm+SiLU drop-in used at AtlasCloud that expands supported channel widths and improved latency. The post details primitives (T.alloc_shared, T.gemm, T.Pipelined, etc.), backend targets, and one-line optimization knobs like swizzling and warp policies.
A technical guide demonstrating TileLang's layout-inference and kernel-building features with production examples and measured performance gains; relevant to AI/ML infrastructure and developers optimizing GPU inference workloads but not industry-shifting.
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Key Takeaways & Evidence Grounding
- TileLang is a Python DSL that gives explicit control over tiles, buffer placement, pipelining, and warp partitioning while performing layout inference to fill in low-level details.
- The author provides a worked GEMM example (C = ReLU(A @ B)) demonstrating explicit shared-memory and register allocation, pipelining, and tile-level T.gemm usage.
- TileLang's layout inference and warp policies enabled an ~80-line MLA decode kernel (DeepSeek) that the author benchmarks near FlashMLA H100 fp16 performance (batch 64/128), ahead of Triton and FlashInfer in those tests.
- Backends listed include NVIDIA, AMD, CPU, WebGPU, CuTeDSL and community vendor forks (Ascend & MUSA); the repo can be installed via pip or built from source using LLVM/CUDA toolchains.
- AtlasCloud used TileLang to implement a drop-in RMSNorm+SiLU kernel supporting previously unsupported channel widths, reducing an attention-block RMSNorm latency from 42 μs to ~20 μs and delivering ~1.79× end-to-end VAE encode/decode speedups on production resolutions.
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TileRT Enables Ultra-High Interactivity on NVIDIA GPUs
SemiAnalysis reports on TileRT, a persistent-engine approach that compiles an entire decode graph into a single resident kernel on NVIDIA GPUs to reduce per-token latency. In InferenceX benchmarks TileRT reached up to 500 tokens/s/user on a single B200 decode server (GLM5 FP8 744B) and delivered large gains versus traditional GPU inference engines (e.g., ~3× vs GB300 NVL72 in some tests). TileRT is designed to handle latency-sensitive decode while remaining interoperable with throughput-optimized prefill engines such as vLLM. TileRT is already deployed in production at Xiaomi and Z.ai, but currently supports a small model catalog (GLM-5/5.1, DeepSeek-V3.2) and primarily serves batch size 1 decode workloads, reflecting trade-offs between per-user interactivity and aggregate throughput.
cuTile Rust: Safe Rust GPU Kernels at Near-cuBLAS Speed
cuTile Rust, a tile-based DSL and crate introduced by NVIDIA researchers in the paper “Fearless Concurrency on the GPU” (arXiv:2606.15991), applies Rust’s ownership and borrow-checker model across the host-to-GPU launch boundary by partitioning mutable outputs into provably disjoint tiles and passing exclusive &mut views to tile kernels. The approach compiles to CUDA Tile IR and then into GPU cubins, requiring sm_80+ GPUs, CUDA 13.3, Rust 1.89+, and Linux. Authors report throughput reaching about 96% of cuBLAS on GEMM (on a B200) and end-to-end Grout inference results (171 tok/s for Qwen3-4B on an RTX 5090), though independent reproduction varies by hardware and workload. The crate and toolchain are early-stage, CUDA/Linux-only, and API/macros may change between releases.
Advanced GPU Optimization: Training LLMs with CUDA & ROCm
This technical tutorial (Part 2) shows how to implement the backward pass and full training loop for large language models using HIP (CUDA/ROCm) in C++. It covers writing gradient kernels for linear layers, softmax, and LayerNorm; implementing a fused AdamW optimizer as a single GPU kernel; mixed-precision training using FP16/BF16 with FP32 master weights and loss scaling; activation (gradient) checkpointing to trade compute for memory; and a complete HIP/C++ training iteration with profiling guidance. The article also outlines performance considerations (occupancy, memory bandwidth, kernel launch overhead) and previews a future Part 3 on multi-GPU distributed training (All-Reduce, Ring-AllReduce, ZeRO-style sharding).
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