A Comprehensive Comparison of SGLang, vLLM, TileLang, Triton, and FlashInfer

As large language models move from research demos into real production systems, the surrounding inference stack becomes just as important as the model weights themselves. Hardware changes, longer context windows, and more complex prompts have pushed teams to rethink how language models are served, optimized, and maintained.

The current conversation around large model inference often centers on a handful of influential projects: SGLang, vLLM, TileLang, Triton, and FlashInfer. Understanding these five tools does not mean ranking them as if they belong to the same category. Instead, it means recognizing how they occupy different layers of the same technical problem.

Two Layers of the Stack

A useful way to frame the landscape is to separate inference into two broad layers.

• Serving and orchestration layer: This includes frameworks that manage request scheduling, memory use, batching, and generation flow. vLLM and SGLang are often discussed here.
• Kernel and programming layer: This includes tools for writing and compiling low-level GPU operations. FlashInfer, Triton, and TileLang usually fall into this group.

The distinction matters because a serving framework can be excellent at routing requests while still relying on lower-level kernels for speed. Likewise, a low-level programming tool can produce powerful kernels without itself handling production traffic, caching policies, or API routing.

Individual Building Blocks

vLLM is widely known as a high-throughput serving engine. Its breakthrough came through PagedAttention, a memory management strategy that reduces the overhead of key-value caching. The result is more efficient use of GPU memory and stronger support for high concurrency.

SGLang focuses on complex language model interaction, including multi-turn dialogue and agent-style workflows. Its RadixAttention mechanism improves prefix reuse, while structured generation helps control output more precisely. For tasks involving long prompts, repeated context, or orchestration-heavy agents, SGLang can offer a meaningful advantage.

FlashInfer is more narrowly focused on accelerating attention operations. It provides optimized CUDA kernels for different attention patterns, such as prefill, decoding, and sparse attention. Because attention is central to transformer inference, FlashInfer is often used as a building block inside larger serving stacks.

Triton began as a GPU programming language and compiler from OpenAI. It lets developers express custom operations in Python-like syntax rather than writing raw CUDA directly. That lowers the barrier for custom kernel development while preserving performance potential.

TileLang is a domain-specific language aimed at tensor core optimization. It emphasizes loop transformations and hierarchical memory management, making it possible to target advanced hardware features more directly. It is especially relevant for teams pushing performance on newer GPU architectures.

Side-by-Side View

From this perspective, vLLM and SGLang are often compared as high-level serving frameworks. FlashInfer, Triton, and TileLang are more commonly evaluated as lower-level acceleration or programming layers.

How the Layers Interact

In real deployments, the five tools rarely exist in isolation. They may be combined in different ways.

• A serving engine such as vLLM might integrate FlashInfer kernels to improve attention performance.
• A complex agent system might choose SGLang for prefix caching and structured generation.
• A team building a novel architecture might use Triton or TileLang to create custom operations for unusual attention or normalization layers.

This creates a layered workflow. The top layer handles traffic, memory, and orchestration. The bottom layer handles raw computation. The best results often come when both layers are designed with awareness of each other.

Selection Dimensions

When teams evaluate these options, they usually weigh several factors.

• Performance: Which layer delivers the largest latency or throughput gain?
• Engineering cost: How much custom work is required?
• Hardware compatibility: Does the solution support specific GPU architectures?
• Ecosystem maturity: How easy is integration with existing models and tools?
• Flexibility: Can the team adapt quickly to new model designs?

For standard text generation, an established serving engine may be enough. For long-context retrieval or agentic workflows, caching and scheduling behavior become more critical.

Final Perspective

The five technologies form a broader inference ecosystem rather than a simple competitive lineup. vLLM and SGLang address service-level challenges, while FlashInfer, Triton, and TileLang address kernel-level challenges. Recognizing both perspectives helps teams avoid narrow comparisons and build more resilient inference infrastructure.

The right stack depends on the bottleneck. If the issue is memory and batching, serving frameworks lead. If the issue is attention compute, kernel libraries matter. If the issue is custom model behavior, programming languages become essential. Together, these tools shape the way large models are optimized, deployed, and served.