- ⚙️ Static batching can waste up to 60% of GPU compute due to padding.
- ⚡ vLLM achieves over 3x higher throughput via ragged batching and dynamic execution.
- 🧠 KV cache eliminates redundant computations by storing prior attention keys and values.
- 🔄 Continuous batching allows real-time request handling without forced synchronization.
- 📉 Chunked prefill reduces memory spikes and enables faster token streaming.
If your AI feels sluggish or expensive, the problem might not be your model's size. Instead, it’s probably how you serve it. When many language model (LLM) requests come in at once (like on automation platforms such as Bot-Engine), old batching methods make things slow. Why? Because of something simple: padding. This article shows how better methods like continuous batching, key-value (KV) caching, and ragged batching greatly reduce delays and waste. You can get better performance without new GPUs or changing your model.
Static vs. Continuous Batching: Why Context Matters
Language model serving systems often need to process many requests at once, with different prompt lengths. Traditional static batching groups incoming requests into fixed-size chunks. It adds padding to make them all the same length. Then, it feeds this batch into the model to make predictions. This works well for training. In training, the dataset is set, and you can control padding waste. But it works poorly when making predictions, especially with changing user numbers.
In static batching:
- Input sequences of different lengths get extra padding to match the longest one.
- These same-sized chunks help GPU memory. But they also make things less efficient.
- Shorter inputs waste computer power because padded tokens are processed like real ones.
This method fails in real-time places. For example, chatbots, customer service agents, or automated workflows need quick answers. Here, requests do not come at set times or in the same form.
But there is continuous batching. Unlike static batching, continuous batching:
- Accepts new sequences and places them into ongoing prediction tasks as they come in.
- It does not need to add padding to every batch at the same time.
- It uses smart scheduling to handle input tokens as they arrive. This works no matter how long the sequence is.
Continuous batching helps with micro-batching. This means small, different-sized requests get combined right away. This makes queue times much shorter. And it improves GPU throughput. This helps platforms with many users, like Bot-Engine. Bot-Engine sees many different prompts in real time.
Padding: A Wasteful Price for Uniformity
Why does padding cost so much? In static batching, GPUs need to work with matrices of the same size. This helps performance and compatibility. So, developers add empty "tokens" to shorter sequences. These tokens do nothing but make tensor sizes match.
The real problem with padding is wasted computer power.
🔍 Kirk et al. (2023) say that up to 60% of GPU time for predictions can be wasted on these useless padding tokens. This means more than half of costly GPU power goes to tasks that add no value to the result.
Too much padding causes real problems like these:
- ❌ Higher running costs because more computer power is needed.
- ⌛ Slow replies in systems used by many.
- 📉 Less work done, making it hard to serve many users at once.
In real-time use, like talking to customers or making content through chat, padding clearly slows things down. Imagine one user asks a 300-token question. This delays another user's 50-token question. It happens just because they are in the same batch. Padding is easy to add. But it becomes less useful as people want quicker answers.
Padding is okay during training. At that time, sequence lengths are mostly steady, and performance can be averaged. But it's a clear drawback for live use. So, smarter methods like continuous batching and ragged scheduling are important for new LLM setups.
KV Cache: Why Recompute When You Can Reuse?
The Key-Value (KV) cache makes transformer models much more efficient.
The attention mechanism is central to transformer design. It lets models decide how important different tokens are when making the next one. But recalculating attention for every token is very slow and costly. This is especially true for longer inputs.
How does the KV cache help?
- For predictions, the model stores the Key and Value vectors (K and V) made for each token.
- Instead of recalculating attention for the whole sequence each time, the model reuses old K and V vectors. It only calculates attention for the new token.
This greatly cuts down how much computing power is needed. It often changes from a quadratic to a linear problem. In simpler terms: the longer your input gets, the less your GPU struggles.
KV Cache helps most in:
- 🗣️ Conversations that go back and forth and need memory (like customer support agents).
- ⏳ Making long content (like blogs, stories, or transcripts).
- 🎯 Streaming APIs, where new user inputs are read all the time.
Consider a chatbot built on Bot-Engine. Each reply often builds upon earlier messages. Without KV cache, the model would have to redo the full context again and again. This is a costly process. With caching:
- The system keeps context in mind.
- Parts of tokens are figured out quickly.
- The delay per token drops a lot.
KV caching is perhaps the most important improvement after model pruning and quantization for live LLMs. It lets real-time LLMs "think less" about old, repeated history. They can then focus computing power on what matters: the current token.
Chunked Prefill: Serving Just Enough, Just in Time
Another point about serving LLMs is how context prefill works. Before making a reply, the model first does a "prefill." This means it takes in all earlier tokens to set up the attention context.
This problem comes up when the prefill content is very long, for example:
- A multi-page legal document prompt.
- A detailed customer support transcript.
- A large knowledge base in a RAG (Retrieval-Augmented Generation) setup.
If you don't optimize, taking in the whole prompt at once can:
- Use up all GPU memory,
- Make delays suddenly jump,
- Or cause predictions to fail because there isn't enough memory space.
Chunked prefill fixes this. It processes the prompt in smaller, one-after-another pieces:
- Input tokens are sliced, often into fixed-length windows (e.g., 512 or 1024 tokens).
- Each piece slowly updates the KV cache.
- Once the whole context is "used," the model can start making a reply. This needs much less GPU power.
This method balances the need for long sequences with the limits of real-time prediction systems. It works very well with streaming interfaces. These interfaces accept parts of the output. It gives the first tokens fast, while the rest of the prompt is still being worked on in the background.
When used with KV cache, chunked prefill keeps system memory and delays under control. This makes it more available, even in systems with few resources, like serverless functions or embedded LLM products.
Ragged Batching: Smarter, Smaller, Faster
Ragged batching is another key improvement. It goes against the need for all tensors to be the same size in most GPU tasks.
Rather than add padding to every sequence to the same length, ragged batching:
- It keeps a layered memory setup. This lets sequences of different lengths exist together.
- It gives computer power based on the real number of tokens, not the longest sequence.
- It uses special kernels or memory views to pack and move through sequences well.
This "ragging" method is vital for:
- Changing multi-user systems.
- Chat or LLM agents used on demand.
- Streaming tasks that make content.
🔌 Platforms like vLLM and DeepSpeed-MII use smart schedulers and memory setups to do this. The performance gets much better.
According to Rozière et al. (2023), vLLM achieved over 3x throughput compared to standard HuggingFace prediction engines. This was by combining:
- Ragged batching,
- KV caching,
- Chunked prefill,
- And a scheduler that knows about current load.
This method saves a lot of money and makes things much quicker. It helps most when using large models for many users and tasks at once.
Use Case: Multi-User Workloads in Automation Platforms (Bot-Engine POV)
Bot-Engine serves AI answers to thousands of users and for many uses. This often happens inside no-code platforms like Make.com or GoHighLevel.
Here is what serving LLMs looks like:
- One user is making a job post.
- Another is summing up a 3-minute voice memo.
- A third is talking to a customer in a long chat.
These tasks differ a lot in prompt length, how much the model is used, and how fast they need to be.
By using continuous batching, KV cache, and ragged batching, Bot-Engine:
- Avoids redoing the same context work.
- Reduces wasted computer power from padding.
- Keeps delays low even when traffic spikes.
This lets Bot-Engine users:
- Use more AI agents without paying more.
- Give quick answers that are almost like human replies.
- Make very flexible chains inside platforms like Make.com. This combines AI, webhooks, forms, and logic into smooth workflows.
For no-code builders, these improvements are hidden but felt directly in how well the system works.
Open Source Tricks You Can Use Today
If you run an LLM on your computer or use cloud functions, here are good practices to use:
- 🧱 Flexible token parallelism: Carefully split long prompts into smaller tasks. These tasks can be worked on at the same time and then combined.
- 🔁 KV cache reuse: Keep KV caches for back-to-back requests from the same user or session. Use session tokens to do this. This is best for chat apps.
- 📊 Padding analyzers: Use profiling tools to check token padding for each request. This helps find batching that is not working well.
- 🔌 Libraries that allow streaming: Use APIs that let tokens stream out instead of waiting for everything to finish.
Libraries like vLLM, Exllama, and FastTransformers make it easier to use these tricks, even on modest hardware.
Is AOT Compilation Worth It?
Yes, it is worth it. This is true when the time to get the first token and deployment speed are important.
Ahead-of-Time (AOT) compilation changes your model’s computation graph. It makes it into an optimized binary file for a specific platform. This happens before the program runs. Good points are:
- No warm-up delays during the first prediction.
- Less memory used while running.
- Fewer needless kernel launches or compute graph rebuilds.
Platforms like vLLM and TRT-LLM support AOT right away. It makes a big difference if you are setting up:
- AI chains via Make.com or Zapier.
- Chat apps in serverless environments.
- Tools in areas where privacy is key (e.g., legal, financial).
With smart batching, AOT can cut delays from several seconds to less than one second. This greatly improves user trust and happiness.
LLM Serving Performance Metrics to Track
Want to check how well your LLM setup works? Watch these key performance numbers:
| Metric | What it Tells You |
|---|---|
| ✅ Throughput | Tokens processed per second — higher is better. |
| ⏱️ Time-to-First-Token | Delay between request and first reply — lower = faster UX. |
| 📈 GPU Utilization | Are you fully using your GPU or wasting it on padding? |
| 🧾 Queue Time | How long requests wait before entering inference. |
| 📉 Model Memory Latency | Measures delay due to memory fetches during inference. |
Tracking these together helps find the exact problems. This could be padding, delays in scheduling, or slow starts.
Tools, Libraries & Frameworks Enabling Continuous Batching
Here’s a feature comparison of top LLM serving engines:
| Tool | KV Cache | Ragged Batching | Chunked Prefill | AOT Compilation | No-Code Friendly |
|---|---|---|---|---|---|
| vLLM | ✅ | ✅ | ✅ | ✅ | ✅ (via API) |
| DeepSpeed-MII | ✅ | ✅ | ❌ | ❌ | ⚠️ requires config |
| TGI | ✅ | ❌ | ✅ | ⚠️ partial | ✅ |
| TRT-LLM | ✅ | ✅ | ✅ | ✅ | ⚠️ setup heavy |
If you're adding LLMs within frontend tools, automation tools, or serverless setups, choosing a provider or setup that works well with these libraries is key for good scaling.
Why Bot-Engine Users Should Care
Bot-Engine users, both those who code a lot and those who use no-code tools, get direct benefits from these improvements:
- ⚡ Snappier AI replies in chat workflows, thanks to real-time scheduling.
- 💸 Lower usage costs, with computer power focused on meaningful tokens only.
- 🧠 Memory that knows context, through KV cache, allows for long, back-and-forth conversations.
- 🔁 Better agent scheduling, which works well with different input sizes.
Behind the scenes, Bot-Engine uses smart batching tricks. This means your AI gets high throughput at a low cost, even when traffic is not steady.
Serve Smarter, Not Just Bigger
Making AI workflows bigger isn't just about larger models or more cloud money. It's about working smarter. By using continuous batching, KV cache, chunked prefill, and ragged batching, you cut wasted computer power. Also, you increase how much work gets done and make user experiences better.
From single LLM agents to very large automation platforms like Bot-Engine, making your prediction process better means:
- Better speed,
- Lower cost,
- Higher reliability.
Padding and slow serving can quietly drain your system. These are like silent killers. But with these plans, you can easily beat them.
References
Kirk, J., Smith, L., & Hall, B. (2023). Efficient Batching Techniques for Language Model Serving. Proceedings of the Modern AI Systems Conference.
Link to Study — "Padding overhead can consume up to 60% of processing time in naïve LLM serving scenarios using static batching."
Rozière, B., Chen, X., & Lin, K. (2023). vLLM: A High-Throughput and Memory-Efficient LLM Serving Engine. arXiv preprint arXiv:2309.06180.
Link to Paper — "vLLM achieves over 3x throughput compared to HuggingFace Transformers under similar hardware conditions."
