TileLang explained: write AI kernels at tile granularity
Deploying TileLang kernels: pin the toolchain and target
Move a verified operator into an inference or training service without surprising rebuilds
What you will learn
- Freeze the full environment
- Separate build from warm service
- Prepare rollback and diagnosis
Before you start
- One target operator and device
- A correct framework reference
- A compatible compiler and runtime
Move from a passing README example to evidence for one real model bottleneck
Key takeaways
- An image build is not a device execution test.
- JIT compile time and request time differ.
- A model release should identify its kernel revision.
Freeze the full environment
Record TileLang, Python and PyTorch versions, CUDA or ROCm toolchain, driver and GPU architecture. A source build also involves the repository’s customized TVM submodule unless you deliberately use an existing TVM path.
The installation guide offers CUDA-versioned Dockerfiles and separate ROCm guidance. Building an image without a GPU is possible in the documented CUDA flow, but an image build is not an execution check on the deployment GPU.
Separate build from warm service
A JIT kernel may compile a new specialization for a new shape or compile-time argument. Decide which shapes the service will accept, warm common specializations deliberately, and capture compilation failures separately from request latency.
Keep a tested PyTorch or other reference fallback for unsupported shapes and devices. Do not let a benchmark-only kernel silently become the sole production path before it has correctness and failure tests.
Prepare rollback and diagnosis
Pin the image digest and exact kernel code with the model release. Record generated artifacts, logs and device details when a kernel fails; a general server error is not enough to debug compiler lowering.
No container or model service was deployed for this article. The steps are a release checklist derived from the pinned installation and JIT paths.
Decision guide
| Criterion | Option A | Option B |
|---|---|---|
| Best when | You need predictable behavior and easy auditing | You need adaptive optimization and have reliable telemetry |
| Main risk | May leave performance on the table | Can become difficult to explain or debug |
Implementation steps
- 1
Pin framework, compiler, driver and device versions.
- 2
Warm known specializations and retain a reference fallback.
- 3
Test rollback on the actual deployment GPU.
Copy-ready example
kernel_revision: d82101f
framework: pinned-pytorch
backend: cuda-or-rocm
device_arch: measured-target
shapes: explicitly-supported
fallback: tested-referenceFrequently asked questions
Can I compile on a CPU host and assume the GPU path works?
No. The guide allows some image builds without a GPU; runtime correctness still needs the target hardware.
What should a deployment manifest record?
Exact package and source versions, framework, toolkit, driver, device architecture and accepted shapes.
Sources
- TileLang / docs/get_started/Installation.mdSource checked 2026-10-04
- TileLang / README.mdSource checked 2026-10-04
- TileLang / pyproject.tomlSource checked 2026-10-04
- TileLang / tilelang/jit/__init__.pySource checked 2026-10-04
- TileLang / docs/tutorials/debug_tools_for_tilelang.mdSource checked 2026-10-04