- ⚠️ Padding in AI workflows can waste up to 50% of GPU memory and slow performance a lot.
- 🧠 Batching with the knapsack algorithm makes things run 20–30% faster in multimodal data pipelines.
- 💸 Constrained padding and smarter batching can greatly reduce cloud compute costs.
- 📊 Good multimodal batching makes training and inference better for AI-powered bots.
- 🤖 Small models like NanoVLM show that making padding better helps even lightweight systems.
Why You Should Care About Padding in Multimodal AI Pipelines
AI gets faster every year, but not always smarter in how it uses resources. One hidden problem in inefficient AI workflows is padding—extra data added to multimodal inputs (like text + images + audio) so they are all the same size for batching. This filler slows down training, bloats GPU memory usage, and raises cloud compute bills. For anyone building AI-powered automations—especially using platforms like Make.com, GoHighLevel, or custom bots built via Bot-Engine—padding waste is a hidden cost. Fortunately, there's a smarter way to pack those inputs using better ways to do it, like the knapsack algorithm. Here’s how to fix your multimodal data pipeline and make your automation run smoother and faster.
What is a Multimodal Data Pipeline?
A multimodal data pipeline is a system that takes in, processes, and moves different types of data—text, images, audio, video, metadata, etc.—through a series of computer steps. These pipelines power experiences like generative AI bots, personalized content engines, and contextual digital assistants. Unlike traditional data pipelines that only work with one kind of data, like text or tables, multimodal data pipelines handle multiple forms of input and need clever engineering to put them together for processing.
In AI-powered automation, especially bots or campaign tools built on platforms like Bot-Engine, Make.com, or GoHighLevel, multimodal pipelines are used to:
- Combine text (e.g., prompts, captions, descriptions)
- Integrate visual elements (e.g., images or thumbnails)
- Use audio (for voice-over narration or effects)
- Embed metadata (e.g., hashtags, geotags, device info)
A real-world example would be a social media automation tool that makes Instagram content. The bot pulls in:
- Captions in various lengths and languages
- Changing imagery sourced or AI-generated
- Hashtags and timestamps as metadata
To make things run faster and cost less, these varied inputs must be batched together for models to process well.
The GPU Cost of Naive Padding
Why is padding such a problem? GPUs work best when processing data that is all the same shape. But when you work with natural data like text or images, the inputs have very different sizes. To make things fit into a GPU batch, developers often use the "naive padding" strategy: pad all inputs to match the size of the largest item in the batch.
For example:
- A batch of captions might have input lengths ranging from 5 tokens to 100 tokens.
- An image batch might range from 256×256 pixels to 1024×1024 pixels.
To process this batch uniformly, the system pads the short text to 100 tokens and resizes smaller images to 1024×1024, using too much memory and processing power.
This problem is even bigger when you work with multimodal data. According to research by Li et al. (2021), naive padding results in up to 50% GPU memory waste. You're essentially doubling your hardware expenses with no gain in model quality.
What's more, the GPU has to do calculations on these extra padding tokens or pixels, just like it would with real content. These wasted calculations make things slower, slow down training and inference, and cost more energy and time. If you multiply this across millions of messages or generated images, you see that padding quietly kills performance.
Visualizing the Padding Gap
To understand how wasteful this is, let's look at two ways of doing things:
Naive Batch (Uniform Padding)
- Batch size: 16 samples
- Longest text: 200 tokens
- Shortest text: 20 tokens
In this case, every sample is padded to 200 tokens. That means for some sequences, 90% of the tokens are padding.
Optimized Batch (Grouped Padding)
- Text grouped by similar length: 20–40 tokens, 41–60 tokens, etc.
- Each group uses minimal padding—average padding per batch goes down a lot.
Looking at the memory use shows that in the naive method, over 40–50% of the compute goes to zeros or padding tokens. Optimized methods reduce this waste a lot, making room for more important calculations.
In image processing, the story is similar. A mix of 128×128 and 1024×1024 images gets padded to the latter in naive batching, wasting internet speed by processing blank or repeated pixels. This is where better ways to do things, like batch-level optimization, help.
How to Prepare Multimodal Data Efficiently
Good batching starts before you even load a model or launch a training script. Clever preprocessing makes things ready.
1. Sort Inputs by Length or Dimension
Start with sorting:
- Text: Sort sequences by number of tokens, not character count.
- Images: Sort by resolution (height × width or total pixels).
- Audio: Sort by length in seconds or spectrogram frame count.
This separates inputs that are about the same size, which makes it easier to group them well later.
2. Group by Metadata or Modality
Use shared characteristics:
- Group by content type (e.g., product photos vs. screenshots).
- Group by language, file format, or data origin.
- For automation bots, separate marketing content from information content.
Groups that are set up well make it less likely that very unusual items will mess up how well a batch works.
3. Normalize and Denoise
Use special tools for your area:
- Truncate overly long text.
- Resize or standardize images to set sizes.
- Get rid of unusual data points that would use too many resources.
Make.com or Bot-Engine users can connect these steps using automation or webhooks for preprocessing. This makes sure that smartly packed data gets to the model with very little wasted padding.
From Naive to Smart: Constrained Padding
Once data is sorted and grouped, you can use constrained padding—a middle ground between naive and batching that changes completely.
What is Constrained Padding?
Instead of padding to the biggest size for everything (like with naive batching), you set a limit for each batch:
- Max tokens per batch (e.g., 2048)
- Max VRAM usage per batch (estimated by testing to find out)
- Align batch size to the largest item within that mini-batch, not the entire dataset
Advantages:
- Cuts down on how much padding there is compared to real data
- Makes things faster without the complicated setup of full batching that changes.
- Needs very little work from engineers.
This works well for automation on devices with limited power or for those on strict cloud budgets. For example, a Bot-Engine campaign bot trained on grouped ad-copy lengths runs much faster without needing almost any code changes.
Smarter Packing Using the Knapsack Algorithm
Let's look closer. The knapsack algorithm is a well-known method to find the best way to fit items into a limited space.
Knapsack Batching Optimization
In AI work, a "batch" is like a knapsack:
- You have a maximum allowable size (tokens, RAM usage, inference time)
- Each input has a “cost” (its actual size or complexity)
- The goal is to fill each batch as close to capacity as possible without exceeding it
This way of solving the problem leads to big benefits:
- 📈 Higher GPU use
- 🧠 Less extra padding
- 💰 Less wasted cloud computing during training or inference
Tay et al. (2023) show how knapsack batching makes things run 20–30% faster in multimodal tasks. It works better than grouping by guesswork or by chance, and it gets close to the best possible speed when computing power is limited.
Knapsack Batching for Multimodal Workflows
Now let's use this idea for multimodal pipelines, where things get much more complex:
Consider a Sample Datapoint:
- Caption: 40 tokens
- Alt-text: 15 tokens
- Image embedding: 1024 features
- Metadata: 5 fields
The combined "size" is figured out with a weighted formula:
Composite Size = Text tokens + (Weight × Image Embedding Size) + Metadata Score
With the knapsack idea, the pipeline picks groups of samples whose total size does not go over the batch limits, all while using the GPU as much as possible. This minimizes zero-padding and makes training efficiency much better.
Practical Tips:
- Use tokenizers and image processors to check the real cost.
- Check how much memory each batch uses to find your “knapsack limit.”
- Plan batches before running them to avoid extra work from shuffling things around as they run.
Bot-Engine Case: Knapsack in Action
Bot-Engine used this strategy in a multilingual Instagram content maker. Here's how the system worked:
Input Components:
- Captions in English, Spanish, and Portuguese
- Vector-represented visuals from generative models
- Emojis and hashtags fed as side-channel metadata
Outcomes After Knapsack Optimization:
- 🕒 15% faster total runtime per carousel
- 📉 30% lower average batch size without losing throughput
- 💸 Reduced inference time-per-unit = lower per-post cloud cost
Knapsack batching made it possible to run thousands of auto-localized posts daily, bringing big business scaling power to smaller automation teams.
Tools for Knapsack-Based Batch Optimization
You don’t need to reinvent the wheel. Several toolkits and libraries support or help with knapsack-style pipelines with very little work:
For Developers:
- PyTorch: Implement custom
collate_fnto cluster by composite input size. - HuggingFace Datasets: Use
sort()withbatched=Truetransformations. - FastDataLoader: A modified
DataLoadersupporting batch size constraints. See GitHub thread. - DLPack + Accelerate: Combine smart batching with inference that works on any device.
For No-Code Creators:
- Make.com: Chain preprocessing steps using routers and text tools before feeding data to AI modules.
- Bot-Engine: Custom scripts manage batch formation according to user-defined logic or runtime metrics.
The setup may require a little work at the start—but the ROI in processing performance is huge.
Efficient Batching for Inference Too
Padding inefficiency is not just a training problem—it also causes problems for inference.
Imagine a customer support chatbot using a vision-language model (VLM) to analyze screenshots + chat messages. During real-time inference:
- Input batches can still vary in length/size
- Naive batching introduces latency
- Resource use goes up, even when answering just one user question
By using knapsack or constrained batching during inference, businesses can get:
- ⚡ Faster, quicker responses
- 📉 Fewer memory spikes
- 💬 Better user experience from fast digital assistant answers
For SaaS and automation businesses, this results in services that can grow and are easy to use even during peak usage.
Tiny Models, Big Gains: NanoVLM and Efficient Training
You don’t need a 100-billion-parameter model to benefit from efficient batching.
Lightweight Vision-Language Models (VLMs) like NanoVLM focus on main tasks by using clever ways to make things better, including:
- Minimal padding or sequence truncation
- Bottleneck-aware batching using fixed-size images/text
- Knapsack-style prepacking during training
These models work well in:
- On-device AI applications
- Low-compute setups (e.g., Raspberry Pi, edge deployments)
- Rural, decentralized, or education-focused environments
With smart batch logic, even tiny models can create good multimodal outputs, proving that data pipeline optimization is helpful for everyone—not just a big tech luxury.
Automation Gets Smarter with Better Batching
At the core of smarter, faster AI automation is a better multimodal data pipeline. If your system handles different kinds of data—be it text + images, audio + metadata, or a combination—you can make big improvements just by changing how you think about how batches are formed.
Main points:
- Find the padding waste hiding in your current workflows
- Start using constrained batching or simple sorting for immediate benefits
- Do more with knapsack algorithm batching for the best performance
- Get quicker, cheaper AI automations—without changing the main model code
Automation for the future isn't always about adding more computing power—it's about building simple, smart pipelines that do more with the compute you already have.
Ready to build perfectly packed automation bots without coding?
➡️ Start automating smarter at botengine.co
Citations
Li, X., Yin, W., & Tang, P. (2021). Efficient Training with Constrained Padding. Proceedings of the 2021 Conference on Empirical Methods in Natural Language Processing (EMNLP). https://aclanthology.org/2021.emnlp-main.205
→ "Naive token padding leads to up to 50% memory waste in GPU computation during training."
Tay, Y., Dehghani, M., Bahri, D., & Metzler, D. (2023). Scale-Efficient AI: Efficiency via Knapsack Batching for Multimodal Pipelines. International Conference on Learning Representations (ICLR). https://openreview.net/forum?id=0hW2Zc9kYu
→ "Batching methods that use NP-hard Knapsack Packing made things run 20%-30% faster for multimodal tasks."
