tilert 0.1.4

TileRT: Tile-Based Runtime for Ultra-Low-Latency LLM Inference.

TileRT: Tile-Based Runtime for
Ultra-Low-Latency LLM Inference

TileRT serves large language models (LLMs) in ultra-low-latency scenarios — pushing the latency limits of hundred-billion-parameter models to millisecond-level time per output token (TPOT) without compromising model size or quality. Its tile-level runtime engine decomposes LLM operators into fine-grained tile tasks and dynamically overlaps computation, I/O, and communication across multiple GPUs.

The current preview supports DeepSeek-V3.2 and GLM-5 on 8× NVIDIA B200. For full usage, examples, and news, see the GitHub repository.

_{GLM-5.1-FP8 token generation speed with TileRT v0.1.4. Output length 1K, input length 1K–192K. Bars compare TileRT without MTP, with MTP at average acceptance length 3.2, and the peak under best-case MTP acceptance.}

Installation

The official tilert==0.1.4 wheel on PyPI was compiled against the following stack. Treat these as hard requirements, not lower bounds.

Component	Pinned version
NVIDIA driver	Supports CUDA 13.2 runtime
Operating System	Linux x86_64, glibc ≥ 2.28 (manylinux_2_28)
Python	3.12
PyTorch	torch==2.11.0+cu130
transformers	4.46.3
tokenizers	0.20.3

Recommended: pre-built Docker image

The pinned environment is preinstalled in our official image — the recommended way to run TileRT, avoiding version drift on the host. The image is mirrored to two registries; pull from whichever is reachable:

docker pull ghcr.io/tile-ai/tilert:cu132-latest   # GitHub Container Registry
docker pull tileai/tilert:cu132-latest            # Docker Hub

Launch a container with all 8 GPUs attached, then install the wheel inside:

docker run --rm -it --gpus all --ipc=host \
    -v "$PWD":/workspace -w /workspace \
    ghcr.io/tile-ai/tilert:cu132-latest

# Install from PyPI:
pip install tilert==0.1.4

# Or pin the exact wheel from the GitHub Release page (same artifact,
# useful when PyPI is unreachable):
pip install https://github.com/tile-ai/TileRT/releases/download/v0.1.4/tilert-0.1.4-cp312-cp312-manylinux_2_28_x86_64.whl

Verify the install:

python -c "import tilert, torch; print('tilert', tilert.__version__, '/ torch', torch.__version__, '/ cuda', torch.version.cuda)"
# Expected: tilert 0.1.4 / torch 2.11.0+cu130 / cuda 13.0

Documentation

For weight conversion, the generation CLI, the programmatic API, Multi-Token Prediction (MTP), and the latest benchmarks, see the TileRT GitHub repository.