LLM-42 enables deterministic LLM inference via a decode–verify–rollback protocol, without rewriting GPU kernels. Built on SGLang v0.5.3.
Raja Gond‡, Aditya K Kamath†, Ramachandran Ramjee‡, Ashish Panwar‡
‡Microsoft Research †University of Washington
Standard LLM serving is non-deterministic: dynamic batching changes GPU reduction orders, producing different outputs across runs. LLM-42 fixes this with a lightweight verify-rollback loop:
- Decode — generate tokens using fast, unmodified kernels with dynamic batching.
- Verify — replay a window of tokens under a fixed-shape schedule to check consistency.
- Rollback — on mismatch, discard inconsistent tokens and resume from the last verified position.
Only requests marked is_deterministic=True incur verification; the rest run at full speed.
# Create and attach to a GPU-enabled Docker container (uses lmsysorg/sglang:v0.5.4)
./run_container.sh create && ./run_container.sh attach
# Inside the container: workspace is mounted at /workspace
cd /workspace
apt update; apt upgrade -y
git config --global --add safe.directory /workspace
# Build sgl-kernel and install sglang in editable mode
./build_all.sh
# Authenticate with Hugging Face to download gated models (e.g., Llama)
huggingface-cli login --token <HF_TOKEN>
# Terminal 1: Start the LLM-42 server (waits for model to load)
bash llm42_benchmarks/basic/launch_server.sh
# Terminal 2: Once the server is ready, run the determinism-check client
python3 llm42_benchmarks/basic/client.py| Flag | Default | Description |
|---|---|---|
--enable-llm42 |
0 |
Set to 3 to enable LLM-42 DVR |
--llm42-window-size |
64 |
Tokens decoded before verification |
--llm42-verify-batch-size |
8 |
Requests per verification batch (grouped verification) |
Additional flags for benchmarking: --enable-deterministic-inference 2 (global batch-invariant baseline), --llm42-skip-mismatch (mismatch rate control / synthetic mismatch injection).
4× NVIDIA H100 PCIe (80 GB HBM3), 64-core CPU, ~1.65 TB DRAM.
├── python/sglang/
│ ├── srt/
│ │ ├── llm42/ # Core LLM-42 decode–verify–rollback logic
│ │ ├── batch_invariant_ops/ # Batch-invariant kernel wrappers
│ │ ├── layers/ # Model layers (attention, MoE, etc.)
│ │ ├── models/ # Supported model architectures
│ │ ├── managers/ # Request scheduling & memory management
│ │ └── sampling/ # Sampling strategies
│ ├── launch_server.py # Server entry point
│ └── bench_serving.py # Serving benchmark client
├── sgl-kernel/ # Custom CUDA/Triton kernels
│ ├── csrc/ # C++/CUDA sources
│ └── python/ # Python bindings
├── llm42_benchmarks/ # LLM-42 benchmark scripts
├── llm42-plots/ # Plotting scripts for paper figures
├── benchmark/ # Upstream SGLang benchmarks
├── docker/ # Dockerfiles & Kubernetes manifests
├── scripts/ # CI, utility, and helper scripts
├── build_all.sh # Build sgl-kernel + install sglang
└── run_container.sh # Create/attach to dev container
@article{gond2025llm42,
title = {{LLM-42}: Enabling Determinism in {LLM} Inference with Verified Speculation},
author = {Gond, Raja and Kamath, Aditya K and Ramjee, Ramachandran and Panwar, Ashish},
journal = {arXiv preprint arXiv:2601.17768},
year = {2026},
url = {https://arxiv.org/abs/2601.17768}
}This project is licensed under the terms in the LICENSE file. It is built on SGLang, which is licensed under the Apache License 2.0.
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