When it comes to scalable inference for large language models (LLMs), SGLang and vLLM
are two prominent engines that offer robust features for multi-GPU setups. Both are actively
evolving, providing options suited to different use cases and workflows. Here’s a
comprehensive comparison based on their capabilities, features, stability, and usability.

Feature Highlights and Capabilities

SGLang features innovative capabilities such as Data Parallelism (DP)—which maintains
two copies of the model in memory for faster inference—and LLM routing, enabling
dynamic workload distribution. It also supports tensor parallelism, allowing multiple GPUs
to act as a single, more powerful unit, making it well-suited for handling larger models
efficiently.
VLLM, primarily supporting pipeline parallelism, divides models into stages processed
sequentially across GPU clusters. While this approach is effective, it can sometimes lag
behind data parallelism in speed for certain workloads. Nonetheless, vLLM also offers
tensor parallelism, making it comparable in terms of parallel execution capabilities.

Performance and Optimization

Initially, SGLang showcased significant speed advantages over vLLM, especially with multi-
GPU inference. Both engines are continuously integrating new features like DeepGEMM
and MLA to improve speed and efficiency. Although specific performance benchmarks are
not discussed here, SGLang is generally regarded as a fast inference engine, particularly
when utilizing its recently fixed quantized cache implementation.

On the other hand, vLLM is transitioning to its new V1 engine, aiming to improve speed and
stability. Despite ongoing development, some users report that vLLM sometimes
underperforms or encounters stability issues, particularly under high concurrency scenarios.

Stability and Compatibility

Both SGLang and vLLM are still maturing and can experience crashes when pushed to high
concurrency levels. System environment plays a critical role; for instance, on platforms like
p5d SageMaker nodes with specific Linux kernels and GPU driver versions, vLLM has been
observed to be less stable than SGLang, which highlights the importance of environment-
specific testing and validation.

It's recommended to maintain both stable and nightly versions of each engine in your
environment. This approach offers flexibility and helps mitigate compatibility issues caused
by varying system configurations or driver versions.

Advanced Features and Multinode Deployment

vLLM offers more advanced speculative decoding, which can enhance throughput
significantly, especially for large models. Meanwhile, SGLang has recently launched
EAGLE2/EAGLE3 structured decoding (sd), capable of achieving very high TPS for large
models like Llama 3.1 8B on a single H100 GPU. Deploying these features typically
requires some additional training and tuning.

For multinode setups, SGLang's setup complexity is generally lower than that of vLLM,
especially compared to setting up Ray with vLLM via Slurm. This simplicity can translate
into reduced setup time and fewer headaches during deployment.

Tuning and Deployment Considerations

Tuning configurations—such as adjusting quantization formats (FP16, FP8, W8A8)—can
significantly impact inference performance and efficiency. The environment's specific CUDA
and driver versions also affect stability and performance. Since both engines heavily rely on
GPU kernels and Python glue code, differences in system setup can influence results.
Thorough testing in your environment is always recommended.

Conclusion: The It Depends Verdict

Ultimately, the choice between SGLang and vLLM hinges on your specific use case:

  • For straightforward, fast multi-GPU inference with simpler setup, SGLang is
    currently a compelling choice.
  • For advanced decoding features like speculative decoding and potential long-term
    improvements, vLLM offers promising capabilities.
  • Maintaining both tools in your environment provides flexibility to adapt to evolving
    needs and stability concerns.

Both engines are actively racing to improve, and having multiple robust options is a real
advantage in the complex landscape of large-scale LLM inference.