Understanding vLLM and SGLang from First Principles: Deconstructing the Core Technologies of Inference Engines

In the process of deploying LLM applications, the choice of inference engine often determines service throughput, latency, and cost. vLLM and SGLang, currently the two most closely watched open-source inference frameworks, are frequently compared, but few developers truly understand "why they're fast" and "where they differ." A community video discussion on "explaining vLLM and SGLang from first principles" hits exactly this pain point—it doesn't start from API usage, but returns to underlying mechanisms like memory management, scheduling, and execution models.

Where Does the Performance Bottleneck of Inference Engines Actually Lie

To understand these two frameworks, you first need to understand the problem they both solve. Autoregressive generation means that for every token generated, all historical tokens must be re-fed into the model for attention computation. If the KV cache is reallocated each time, GPU memory quickly becomes fragmented—especially in long-context scenarios.

The traditional approach pre-allocates contiguous memory blocks of maximum length, resulting in extremely low utilization. From a first-principles perspective, the real problem isn't "the cache isn't big enough" but "the cache is being wasted." Many of the designs in vLLM and SGLang are essentially about combating this waste.

KV Cache (Key-Value Cache) is the core mechanism in autoregressive generation that avoids redundant computation. When generating each token, the Transformer's attention layer needs the Key and Value vectors of all previous tokens. Without caching, the K/V projections for all historical tokens would need to be recomputed at every step, with computation growing quadratically with sequence length. The KV cache stores these intermediate results, so each step only needs to compute the K/V of the new token and concatenate it with the cache for attention. The cost is that memory usage grows linearly with batch size and sequence length—for a 70B model with long context and large batches, the KV cache can reach tens of GB, often exceeding the model weights themselves in memory footprint. This is the fundamental reason inference engines must focus on memory management: cache hit rate and fragmentation rate directly determine how many requests can be served simultaneously.

vLLM: Rewriting the KV Cache with Paging Concepts

vLLM's most core contribution is PagedAttention. It borrows the idea of virtual memory paging from operating systems, dividing the KV cache into fixed-size blocks that are allocated on demand and stored non-contiguously, with a block table completing the mapping from logical to physical addresses. The direct benefit is a dramatic reduction in memory fragmentation, allowing batch sizes to increase significantly.

Another often-overlooked point is continuous batching. Requests no longer need to wait for an entire batch to finish before releasing resources; instead, scheduling happens token by token, and short requests immediately yield their slots upon completion. PagedAttention solves the "space" problem, continuous batching solves the "time" problem, and only their combination constitutes vLLM's throughput advantage. It's worth noting that paging introduces extra block table lookup overhead, so the advantage isn't obvious in extremely short sequence scenarios.

Continuous Batching, also known as iteration-level scheduling, differs from traditional static batching in its scheduling granularity. Static batching requires all requests in a batch to finish generating before releasing resources, forcing short requests to wait for long ones and leaving the GPU idle. Continuous batching re-evaluates after each decoding step: completed requests immediately exit and release their KV blocks, and new requests fill the vacated slots. This keeps the GPU under high load at all times, potentially improving throughput several-fold. It's complementary to PagedAttention—paging allows KV blocks to be allocated and reclaimed non-contiguously, which is what enables continuous batching to flexibly add or remove requests at any moment without triggering memory defragmentation.

SGLang: Abstracting Scheduling into Programmable Execution Plans

SGLang takes a different path. Its starting point isn't just "managing the cache better" but "enabling complex generation workflows to be expressed and executed efficiently." Its core is RadixAttention, which uses a radix tree to manage prefixes shared across multiple requests, achieving finer-grained prefix reuse. In scenarios like multi-turn conversations, few-shot prompting, and tree-structured reasoning, shared prefix hit rates are higher.

More critically, SGLang's frontend DSL design. Developers can describe multi-step generation, branching, parallelism, and constrained decoding in a programming-like manner, and the compiler then translates this logic into efficient execution plans. This "scheduling as program" approach gives SGLang an edge in structured output and agent-type workloads. However, it also means a steeper learning curve than vLLM, and simple scenarios may not need this abstraction.

Radix Tree is a compressed prefix tree that merges and stores paths with identical prefixes. RadixAttention uses it to manage the KV cache: when multiple requests share the same token prefix (e.g., the same system prompt), their KV blocks are stored only once in the tree, and subsequent requests attach to existing nodes for reuse without recomputation. Compared to allocating cache independently per request, this structure naturally supports prefix insertion, lookup, and eviction, and can precisely identify the longest common prefix. In workloads with highly repetitive prefixes—multi-turn conversations, few-shot examples, tree-of-thought—cache hit rates improve significantly, translating directly into lower prefill overhead and latency.

How to Choose Between the Two

Overall, vLLM leans toward general-purpose high-throughput serving, with a mature ecosystem and broad integration, making it suitable as a standard inference backend; SGLang is more flexible in scenarios requiring complex control flow, strong prefix reuse, and structured generation. The two are not substitutes, and many teams select them separately based on business workloads.

Understanding these mechanisms from first principles is more valuable than memorizing benchmark numbers—because workloads change, and only by understanding scheduling and memory models can you judge whether a framework suits your scenario.

Constrained Decoding refers to techniques that force output to satisfy specific syntax or formats (e.g., JSON Schema, regular expressions) during generation. A common approach is to apply a mask to the vocabulary at each decoding step, zeroing out the probabilities of tokens that violate constraints and allowing only legal tokens to be sampled. This is crucial for structured output, function calling, and similar scenarios, but the mask computation itself has overhead, and complex grammars slow down decoding. SGLang's DSL can compile such constraints into execution plans, optimizing them in coordination with scheduling and prefix reuse, giving it an advantage in highly structured workloads like agents and tool calling.

One-line Summary

Comparing the architectural differences and selection logic of vLLM and SGLang from first principles of memory management and scheduling.

Paragraph Summary

The article analyzes the underlying mechanisms of the two major inference engines, vLLM and SGLang, from first principles. It first points out that the real bottleneck in autoregressive generation is wasted KV cache, not insufficient capacity. vLLM uses PagedAttention, borrowing from OS paging, to divide the KV cache into blocks allocated on demand, combined with continuous batching for dual spatial-temporal optimization, targeting general high-throughput serving. SGLang uses RadixAttention with a radix tree for fine-grained prefix reuse, and compiles complex generation workflows into execution plans via a frontend DSL, offering greater flexibility in structured output and agent workloads. The two are not substitutes; selection should be based on workload requirements for control flow and prefix reuse, and understanding underlying mechanisms has more long-term value than memorizing benchmarks.