PagedAttention vs Continuous Batching vs vLLM vs SGLang — A Practical Breakdown

Or: How I Learned to Stop Worrying and Love Efficient LLM Serving

So you’ve got a fancy language model. Maybe it’s Llama, maybe it’s Mistral, maybe you convinced your boss to spring for something even bigger. Great! Now comes the fun part: actually serving it to real users without watching your GPU memory explode like a confetti cannon at a tech conference.

Welcome to the wild world of LLM serving optimization, where the difference between “this is fine” and “oh god everything’s on fire” often comes down to how you manage memory and batching. Let’s break down the key players in this space, and I promise to keep the jargon to a minimum (well, mostly).

The Problem: Why Serving LLMs is Expensive as Hell

Before we dive into solutions, let’s talk about why this is even a problem. When you’re running inference on large language models, you’re dealing with two brutal realities:

Reality #1: The KV Cache is a Memory Hog

Every time your model generates a token, it needs to remember all the previous tokens in the sequence. This “memory” is stored in something called the KV (key-value) cache. For a single request, this might not seem like much. But multiply it across dozens or hundreds of concurrent users, and suddenly you’re burning through VRAM faster than a crypto miner during a bull run.

Reality #2: Generation is Sequential (and Slow)

Unlike training where you can parallelize everything beautifully, generation happens one token at a time. You can’t generate token 50 until you’ve generated tokens 1 through 49. This means your expensive GPU is sitting there, occasionally doing work, but often just… waiting.

The good news? Smart people have been working on these problems. Let’s see what they came up with.

Continuous Batching: The First Big Win

Traditional serving was embarrassingly naive. Systems would wait until ALL requests in a batch finished before starting new ones. Got a batch with one 2000-token response and nine 50-token responses? Well, those nine requests are going to sit around twiddling their thumbs while the long one finishes.

Continuous batching (also called iteration-level batching) said “wait, that’s stupid” and changed the game. Here’s the insight: once a request finishes, you can immediately swap in a new request to take its place in the batch. You don’t have to wait for the entire batch to complete.

Think of it like a restaurant with a fixed number of tables. Traditional batching is seating people, waiting for EVERYONE to finish their meal, then seating the next group. Continuous batching is seating new diners as soon as a table opens up. Which one sounds more efficient?

The impact is massive. Continuous batching can improve throughput by 2–10x compared to static batching, depending on your workload. It’s now considered table stakes for any serious LLM serving system.

PagedAttention: The Memory Management Revolution

Now we get to PagedAttention, which is probably the most important innovation in LLM serving in recent years. It’s the brainchild of researchers who looked at how operating systems manage memory and thought “hey, we could do that for KV caches too.”

Here’s the problem PagedAttention solves: in traditional serving, when you allocate memory for a KV cache, you have to guess the maximum length and allocate that much memory upfront. Most requests don’t use all of it, but you’ve reserved it anyway. It’s like booking a 10-person table at a restaurant when you know only 4 people are coming, “just in case.”

PagedAttention treats the KV cache like virtual memory in an operating system. Instead of one giant contiguous block per request, it breaks the cache into small, fixed-size “pages.” As the sequence grows, you allocate new pages as needed. When a request finishes, those pages can be reused immediately.

The benefits are wild:

• Near-zero waste from over-allocation
• Memory sharing between requests (useful for things like parallel sampling or multi-turn conversations)
• Much higher effective batch sizes since you’re not wasting memory on empty space

In practice, PagedAttention can improve memory utilization by 2–4x, which means you can serve 2–4x more requests with the same hardware. That’s a game-changer when you’re paying cloud GPU prices.

vLLM: The Implementation That Made It Real

vLLM is where PagedAttention went from “cool paper” to “thing people actually use.” It’s an open-source serving framework that implements PagedAttention along with continuous batching and a bunch of other optimizations.

What makes vLLM special isn’t just PagedAttention (though that’s the headline feature). It’s that the team built a complete, production-ready system that:

• Integrates with popular models out of the box
• Provides an OpenAI-compatible API (so you can swap it in easily)
• Handles all the messy details of GPU memory management
• Includes optimizations like tensor parallelism for multi-GPU serving

The result? vLLM typically delivers 2–4x higher throughput than naive implementations, sometimes more. It’s become the de facto standard for open-source LLM serving, and for good reason.

But here’s the thing: vLLM is optimized for a specific use case — relatively simple request/response patterns. What if you need something more complex?

SGLang: When You Need to Get Fancy

SGLang (Structured Generation Language) approaches the problem from a different angle. Instead of just focusing on efficient serving, it asks: “what if your LLM workload is more complex than simple text generation?”

Modern LLM applications often involve:

• Multi-step reasoning chains
• Parallel generation with constraints
• Tool use and function calling
• Complex prompting with dynamic control flow

SGLang is designed for these workloads. It provides a programming model that lets you express complex LLM programs while the runtime optimizes execution behind the scenes. Think of it as the difference between writing assembly code and using a high-level language with an optimizing compiler.

Under the hood, SGLang uses RadixAttention, an extension of PagedAttention that adds prefix caching. If multiple requests share common prefixes (like a system prompt), SGLang can cache and reuse the KV cache for that prefix. For workloads with shared contexts, this can be a massive win.

The tradeoff? SGLang has more moving parts and a steeper learning curve than vLLM. But if you’re building complex agentic applications or need fine-grained control over generation, it might be exactly what you need.

So… Which One Should You Use?

Here’s my completely opinionated breakdown:

Use vLLM if:

• You need simple, efficient serving of open-source models
• Your workload is mostly straightforward text generation
• You want something that “just works” with minimal configuration
• You’re replacing OpenAI’s API with a self-hosted solution

Use SGLang if:

• You’re building complex multi-step LLM workflows
• You need advanced features like constrained generation or parallel sampling
• Your workload has significant prompt sharing across requests
• You’re willing to invest time learning a new programming model

Use continuous batching (without PagedAttention) if:

• You’re stuck with a proprietary serving system that doesn’t support PagedAttention
• Even basic continuous batching is a huge win over what you have now
• You’re building your own serving infrastructure and want to start simple

The Future: What’s Next?

The field of LLM serving is moving incredibly fast. Some trends to watch:

Speculative decoding is gaining traction — using a small model to guess future tokens and having the large model verify them in parallel. When it works, you get 2–3x speedups.

Disaggregated serving is emerging for really large models, where you split prefill (processing the prompt) and decode (generating tokens) across different machines.

Custom hardware designed specifically for LLM inference is starting to appear, with architectural changes that make these optimizations even more effective.

The Bottom Line

If you’re serious about serving LLMs at scale, you need to understand these techniques. The difference between naive serving and optimized serving isn’t 10% or 20% — it’s often 5–10x in throughput and cost efficiency.

PagedAttention and continuous batching are the foundational innovations that make modern LLM serving feasible. vLLM and SGLang are production-ready implementations that bring these ideas to life, each with different strengths.

The best part? Most of this stuff is open source. You can literally clone vLLM or SGLang right now, spin it up on your GPU, and see these optimizations in action.

Just remember: with great serving efficiency comes great responsibility. Now that you can serve 5x more requests on the same hardware, you’re just going to get 5x more traffic. But hey, at least your GPU bills will be lower!

Have experiences with these frameworks? War stories about LLM serving? Drop them in the comments. And if you found this useful, give it a clap or twelve — it helps other people discover content like this.