Continuous Batching Explained: The Scheduling Trick Behind Fast LLM Responses

The most visible part of an LLM product is the chat UI. The most expensive part is the invisible loop behind it: turning long prompts into the first token, then producing thousands of tokens per second across many concurrent users. If your service feels “snappy,” chances are you are winning a scheduling game, not a modeling game.

Continuous batching is one of the core tricks that makes high-load LLM serving viable. It is not magic. It is a set of practical ideas about attention, caching, and how to keep a GPU busy even when user requests arrive at awkward times and have wildly different lengths.

Tags: LLM inference, continuous batching, KV cache, throughput, latency, GPU scheduling, serving infrastructure

The Core Insight

Continuous batching is a throughput optimization that comes from treating LLM inference as a queueing and packing problem under a strict memory budget.

At a high level, every request goes through two phases:

• Prefill: the model processes the entire prompt to produce the first new token and populate internal state.
• Decode: the model generates new tokens one by one, reusing cached state.

If you serve only one request at a time, prefill dominates time-to-first-token (TTFT), and decode dominates total cost for long outputs. If you serve many users, the key question becomes: how do you combine different phases across requests without wasting compute?

The underlying mechanics are worth stating plainly.

1) Attention is expensive, and naive generation repeats work.
In causal self-attention, each token attends to previous tokens. During prefill, attention is quadratic in sequence length because you compute interactions across the prompt.

2) KV caching turns repeated work into memory.
Once you have computed key and value projections for earlier tokens, you can store them in a KV cache and avoid recomputing them on every decode step. That changes the incremental cost of generating the next token from “touch everything again” to “append one token and attend over cached keys.” Compute drops substantially, but memory usage grows with the number of cached tokens.

3) Chunked prefill turns long prompts into an incremental process.
Real prompts can be huge (repositories, long conversations, retrieval context). Sometimes you cannot prefill the entire prompt in one go due to GPU memory constraints. Chunked prefill processes the prompt in segments and extends the KV cache as it goes. The important point is that you can choose chunk sizes to fit your memory and latency goals.

4) Ragged batching removes padding as the primary source of waste.
Classic batching assumes rectangular tensors: all sequences in a batch have the same length, so you pad shorter prompts. This works for homogeneous workloads, but it becomes wasteful when lengths vary or when you mix prefill (many tokens) with decode (one token).

Ragged batching takes a different approach: instead of adding a batch axis and padding, you concatenate token sequences from multiple requests into one long sequence and use an attention mask to ensure tokens from different requests do not attend to each other. You keep the GPU busy with “real tokens,” not padding tokens.

5) Dynamic scheduling keeps the batch full as requests finish.
When a request completes (hits end-of-sequence or output limit), you remove it and immediately replace it with new work from the queue. Continuous batching is the combination of ragged batching plus dynamic scheduling, with chunked prefill providing the flexibility to pack work into a fixed memory budget.

A good mental model is: continuous batching is a packing algorithm that tries to maximize tokens/sec by keeping the device at (or near) its memory limit, while mixing:

• decode tokens (cheap compute, steady cadence)
• prefill chunks (expensive compute, bursty and variable)

Why This Matters

If you build or operate an LLM-backed product, continuous batching changes what “performance” means.

1) It converts variability into utilization.
User prompts are not uniform. Some are short and finish quickly; others are long, or request long outputs. Without continuous batching, this variability turns into idle bubbles on the GPU (waiting for the longest request, over-padding, or under-filled batches). With continuous batching, you can trade scheduling complexity for higher utilization.

2) It lets you scale concurrency without scaling waste.
Naive dynamic batching often collapses under padding overhead when a long prefill request joins a batch of decoding requests. Under load, you can end up paying for hundreds of padding tokens per forward pass. Ragged batching attacks this directly by making “batching” mean “concatenate and mask,” not “pad to a rectangle.”

3) It creates a new set of product-level tradeoffs.
Once you can keep throughput high, user experience depends on how you prioritize and shape work:

• Do you optimize TTFT for interactive chat, or tokens/sec for bulk generation?
• Do you prioritize fairness (no request starves) or raw throughput (always fill the GPU with the most efficient work)?
• How do you handle long prompts that can monopolize memory?

The non-obvious consequence is that inference serving is no longer “just deploy a model.” It is systems engineering.

Key Takeaways

• Continuous batching is built on three building blocks: KV caching, chunked prefill, and ragged batching with dynamic scheduling.
• Prefill is expensive but parallelizable; decode is cheaper per step but dominates time and cost over long outputs. Efficient serving mixes both.
• Padding is not a minor tax. In heterogeneous workloads it can become the primary cost driver, especially when you try to keep tensor shapes static for compilation and graph capture.
• Ragged batching replaces “pad to match shapes” with “concatenate and mask,” allowing you to keep the GPU doing useful work.

Quotes worth remembering (from the source):

• “Continuous batching combines three key techniques to maximize throughput in LLM serving.”
• “By removing the batch dimension and using attention masks to control token interactions, continuous batching allows mixing prefill and decode phases in the same batch.”

Practical advice you can apply:

• Start with instrumentation, not ideology. Track TTFT, tokens/sec, GPU memory headroom, and batch composition (prefill tokens vs decode tokens). You cannot tune what you do not measure.
• Set explicit budgets. Decide on a memory budget (tokens or bytes) per device and enforce it in your scheduler. Your throughput will be capped by how predictably you stay within that budget.
• Protect interactive latency. If your product is chat-like, consider a scheduling policy that reserves a slice of capacity for TTFT-sensitive requests, instead of letting long prefills dominate.
• Plan for complexity and failure modes. Continuous batching can increase operational complexity: more moving parts, more corner cases, and more subtle regressions when model shapes, context lengths, or precision settings change.

A realistic counterpoint: continuous batching is not a free lunch.

• It can introduce fairness issues (short requests may leapfrog long ones, or vice versa, depending on policy).
• It can increase tail latency if the scheduler over-optimizes utilization and delays TTFT.
• It complicates debugging because execution is no longer “one request equals one batch.”

If you are serving at low concurrency, you may not need any of this. But once you hit sustained load, the scheduler becomes your performance multiplier.

Looking Ahead

Continuous batching is the start of the serving story, not the end.

The next layer of hard problems is KV cache management: paging, fragmentation, eviction policies, and how you represent cache blocks to make memory use predictable under concurrency. Techniques like paged attention exist because the cache becomes the dominant resource at scale.

If you are building an LLM service, the strategic move is to treat inference as a pipeline with explicit resource accounting:

• compute (kernels per token)
• memory (KV cache growth and layout)
• scheduling (who gets to run next, and at what granularity)

Models will continue to improve, but the biggest product-level wins often come from serving architecture that squeezes more useful tokens out of the same hardware.

Sources

• Continuous batching from first principles (Hugging Face Blog)
  https://huggingface.co/blog/continuous_batching

Based on analysis of Continuous batching from first principles (Hugging Face Blog) https://huggingface.co/blog/continuous_batching