Demystifying LLM Serving Infrastructure: How PagedAttention and Continuous Batching Scale Inference

Moving a Large Language Model (LLM) from a local prototype in a Jupyter notebook to a high-throughput, multi-tenant production environment is a brutal awakening. While data scientists spend months optimizing model weights, quantization, and fine-tuning, infrastructure engineers face a completely different set of demons: memory fragmentation, queue latency, and GPU starvation.

In a production serving environment, the bottleneck is rarely just the raw compute power (FLOPs) of the GPU; it is almost always memory bandwidth and capacity.

In this deep dive, we will unpack the two foundational pillars that revolutionized LLM serving engines like vLLM: PagedAttention and Continuous Batching.

1. The Silent Killer: Understanding the KV Cache Memory Explosion During autoregressive generation, an LLM generates tokens one by one. To avoid recalculating the Key and Value matrices for all previous tokens at every step, inference engines cache these tensors in GPU VRAM—collectively known as the KV Cache. The memory footprint of the KV cache scales linearly with sequence length and batch size: For a 70B parameter model with a 4K context window, the KV cache can easily consume tens of gigabytes of VRAM per request. The Flaw of Traditional Static Allocation Historically, serving frameworks allocated a contiguous chunk of VRAM upfront based on the model’s maximum possible sequence length (e.g., 4096 or 8192 tokens) to prevent out-of-memory errors during generation. This creates two massive inefficiencies:

• Internal Fragmentation: If a user only requests a 200-token response, the remaining pre-allocated memory for that slot sits idle and unusable.
• External Fragmentation: Varying request lengths make it nearly impossible to neatly pack memory blocks, leading to massive blocks of unused, stranded VRAM. As a result, traditional systems often waste 60% to 80% of their GPU VRAM, drastically choking concurrency.

2. PagedAttention: Virtual Memory for LLMs To solve memory fragmentation, researchers borrowed a concept that operating systems have used for decades to manage RAM: Virtual Memory and Paging. Introduced by vLLM, PagedAttention partitions the KV cache of each sequence into small, fixed-size blocks (e.g., 16 tokens per block). These blocks can be stored non-contiguously in physical GPU memory.

[ Traditional Contiguous Allocation ]
[ Request A: Used ][ Request A: Unused/Wasted (Max Len) ]

[ PagedAttention Block-Based Allocation ]

Block Table (Logical -> Physical Mapping):

Req 1 -> [Block #12] -> [Block #5] -> [Block #28]

How It Works Under the Hood:

• The Block Table: Each request maintains a logical-to-physical block table managed by the inference engine.
• On-Demand Allocation: Blocks are allocated on the fly as new tokens are generated, rather than reserving a massive block upfront.
• Memory Sharing (Copy-on-Write): PagedAttention enables efficient memory sharing for advanced features like parallel sampling, beam search, and multi-turn chat branching, where multiple sequences share common prompt prefixes. The Impact: Memory waste drops from ~70% to under 4%. This allows the GPU to pack significantly more concurrent requests into VRAM, directly multiplying throughput.
  1. Continuous Batching (Iteration-Level Scheduling) Memory management solves capacity, but what about scheduling efficiency? In traditional deep learning batching (static batching), a batch of requests is formed, sent to the GPU, and the entire batch must wait until every request finishes generating its final token. This leads to severe GPU starvation:
• Short requests finish early, leaving their allocated slots empty while waiting for the longest request in the batch to complete.

Incoming requests must wait in an external queue until the current batch fully clears out. Iteration-Level Scheduling to the Rescue Continuous Batching (or iteration-level scheduling) changes the scheduling granularity from the request level down to the iteration (token) level.

#

Conceptual loop of Continuous Batching

while active_requests_queue or running_batch: #

1. Complete one forward pass (generate 1 token for all active sequences)

logits = model.forward(running_batch) # 2. Check for completed sequences

for req in running_batch:
 if req.is_finished():
 running_batch.remove(req)
 if active_requests_queue:
running_batch.add(active_requests_queue.pop())

By decoupling request lifecycles from batch lifecycles:

• As soon as a request generates an end-of-sequence token, its slot is instantly freed.
• A waiting request from the queue is slotted in on the very next forward iteration.
• GPU compute units remain saturated near 100%, slashing average latency and increasing overall system throughput by up to 23x compared to static batching.
  1. Bringing It All Together in Production When architecting an LLM serving stack today, building from scratch is rarely necessary thanks to production-grade engines built on these exact principles. Whether you deploy using vLLM, TensorRT-LLM, or TGI (Text Generation Inference), understanding these internals is critical for:
• Right-sizing GPU instances: Knowing your KV cache block size helps calculate exact VRAM requirements per concurrent user.
• Tuning max model len: Preventing over-allocation and maximizing concurrency limits.
• Optimizing chunked prefill: Handling large context windows without stalling the generation queue. Key Takeaways for AI Engineers:
• VRAM is your primary constraint: Optimize KV cache management before throwing more hardware at the problem.
• Batching is dynamic: Never rely on static batch sizes for autoregressive text generation.
• Infrastructure is code: Low-level memory scheduling decisions dictate your API's cost-per-token economics. What serving engine are you currently using in your production stack? Let’s discuss in the comments below! Artificial Intelligence, LLM, Machine Learning Infrastructure, و Python.