90% of smartphones could run useful AI features today if models were just one-tenth their current size. State-of-the-art models have grown into billions of parameters. Without model compression, they can’t fit on phones, tablets, and wearables.
On-device ML is becoming a must-have: it boosts privacy, cuts latency, and makes offline use fun. But making machine learning work on small devices needs careful model optimization. Experts use pruning, quantization, distillation, and low-rank decomposition to make networks smaller without losing accuracy.
Success in Edge AI is measured by real-world performance: how fast, how much memory, and how much energy it uses. Deploying models means finding a balance between size, speed, and ease of maintenance. This way, smart features work well on different devices.
Key Takeaways
- Model compression is key to running modern models on consumer devices.
- On-device ML offers better privacy, speed, and offline access.
- Common model optimization techniques include pruning, quantization, and distillation.
- Production readiness depends on latency, throughput, and energy, not just accuracy.
- Effective edge AI requires hardware-aware choices and iterative testing.
Why on-device AI Matters for Privacy, Speed, and Access
Keeping models on your phone changes how you balance convenience and control. On-device privacy means your data stays on your device. This lets apps tailor experiences to you without sending your data to the cloud. Companies like Apple and Google use local models for private features while keeping personalization safe.
For more on how to develop local AI, check out this guide: on-device AI best practices.
Real-time responsiveness matters for user experience. Fast response times are key for apps like cameras and voice assistants. Running tasks locally means they work better, even in tight spots. This makes apps feel quicker and more responsive.
Offline operation widens who can use intelligent features. Offline ML lets apps work without internet. This is great for places with weak connections. It means more people can use smart features, even without constant internet.
Shifting inference off central servers changes costs and impact. Doing tasks locally saves money and energy. It’s better for the planet when done on a large scale. Teams find ways to keep models small and efficient, balancing costs and performance.
- Privacy: Local models limit telemetry and minimize data transfer.
- Speed: Reduced round-trip time delivers smoother interactions.
- Access: Offline ML extends services to low-connectivity regions.
- Sustainability: Edge execution supports carbon footprint reduction at scale.
Deployment Challenges That Motivate Model Compression
Deploying machine learning models to real devices is tough. Teams face tight mobile constraints that force design choices from the start. Product managers, UX designers, and engineers must weigh trade-offs between performance and user experience when models run on phones, watches, and tiny IoT boards.
Resource constraints: memory, compute, and battery
Edge devices have limited RAM, reduced compute, and finite battery life. A model that trains on a server can consume all device memory if deployed unchanged. Reducing model size and optimizing compute are essential to prevent crashes and poor battery life.
Operational metrics: inference latency, throughput, and model size
Three operational numbers determine viability in production. Inference latency measures response time for a single input. Throughput tracks how many requests a device or service handles per unit time. Model size defines the storage and memory footprint during runtime. Teams prioritize low inference latency and adequate throughput while keeping model size small enough for the target hardware.
Hardware diversity across smartphones, wearables, and IoT
Hardware varies widely between Apple iPhones, Samsung Galaxy phones, Qualcomm-based devices, and microcontroller platforms. An approach that runs well on a desktop GPU may fail on a wearable NXP MCU. Model compression must be hardware-aware to map sparsity, quantization, and compute patterns to the device’s accelerators.
Trade-offs between accuracy, speed, and maintainability
High accuracy often comes with larger models and more complex pipelines. Netflix’s engineering history shows that top leaderboard models can be impractical for production if they add latency and maintenance overhead. Teams accept modest accuracy drops when compression yields faster inference, higher throughput, and easier maintenance across many devices.
Model compression techniques—pruning, quantization, and distillation—address these deployment challenges by trimming parameters, lowering precision, or moving knowledge into smaller networks. Practical deployment requires experiments with these methods, careful measurement of latency and throughput, and an emphasis on keeping model size aligned with device limits.
model compression: Core Concepts and Why It Works
Model compression is about making machine learning models smaller and more efficient. This way, they can run on devices like phones and smartwatches. The goal is to keep the model’s accuracy high while using less memory, processing power, and energy.
Practitioners use pruning, quantization, and distillation to achieve this. These methods help make machine learning work on devices directly.
It’s best to start compressing models when moving from testing to using them in real life. In the early stages, getting the model to work well is the main focus. But as the model gets closer to being used by everyone, other factors like how fast it works and how much data it uses become more important.
Deciding when and how to compress a model is a careful balance. Quantization, for example, makes the model smaller by using fewer numbers but might slightly reduce its accuracy. Pruning removes parts of the model that aren’t needed, saving on processing power. Distillation takes knowledge from a big model and puts it into a smaller one.
Read more about these methods in this survey.
Aligning the model’s size with its usefulness is key. The model should be big enough to work well but not so big that it’s hard to manage. This approach helps keep costs down and surprises to a minimum, even when the model is used on different devices.
Knowing when to compress a model is important. It’s about having enough memory, working fast enough, and not using too much battery. Teams set up tests that mimic real-world use and adjust the model’s size until it meets all the necessary standards.
Pruning and Sparsity Strategies
Pruning makes models smaller by removing unnecessary parts. This reduces memory use and speeds up how fast models work. But, the method used can change how well the model performs.
Unstructured vs structured approaches
Unstructured pruning gets rid of single weights, making models very sparse. This can save a lot of space. But, it depends on the software and hardware to see the benefits.
Structured pruning removes whole sections of the model. This method works well with certain hardware, like Qualcomm and NVIDIA. It usually makes models run faster and more consistently.
Iterative pruning, fine-tuning, and maintaining accuracy
Pruning all at once can hurt the model’s performance. But, doing it in small steps and retraining helps. This way, the model can keep its accuracy.
By carefully planning when to prune and how to adjust, models can stay accurate. This method is useful for apps and devices that need to be efficient.
Hardware-aware sparsity and runtime support limitations
Pruning that considers the device it will run on works best. This is because it matches the model’s structure with the device’s capabilities. Teams work together to make sure this happens.
But, not all devices can handle sparse models well. Choosing the right pruning method for the device can make a big difference in performance.
- Best fit: Use unstructured pruning for maximal compression when storage is the main constraint.
- Best fit: Use structured pruning for predictable inference speed on common hardware.
- Best practice: Combine iterative pruning with fine-tuning and hardware-aware sparsity for balanced results.
Quantization Techniques and Practical Tips
Quantization changes model weights and activations from high-precision formats like fp32 to lower ones like fp16, int8, or 4-bit. This makes models use less memory and run faster on devices with less power. It’s important to balance the benefits of smaller size and faster speed against any loss in accuracy.
Post-training quantization (PTQ) is quick and useful when you can’t retrain the model. It works well for many tasks, as long as you use good calibration and data. For tasks that need high accuracy, quantization-aware training (QAT) is better. It trains the model to handle quantization well.
Choosing the right bitwidth is key. fp16 often offers good speed with little loss in quality on GPUs and some NPUs. int8 is great for mobile and edge devices, balancing memory and speed well. But, lower precisions need careful calibration and sometimes QAT to avoid big drops in performance.
Calibration needs a small, diverse dataset to figure out the ranges for activations and weights. Per-channel methods usually keep accuracy higher for layers like convolutions and linear ones. Whether to use symmetric or asymmetric quant schemes depends on the backend’s needs.
When testing, compare quantizing weights only to quantizing both weights and activations. KV cache compression can also help reduce memory for sequence models. For complex tasks like code generation, be careful with 4-bit weight-only quantization and always check quality on unseen data.
Tools like Ollama and LM Studio make starting easier with formats and presets for low-bit variants. A good guide can help a lot; check out this LLM quantization handbook for a step-by-step guide and examples.
Start with fp16 and int8 experiments, then add calibration for PTQ. Move to QAT only if PTQ can’t meet accuracy needs. Use per-channel quantization for sensitive layers and keep higher-bit activations when you can to reduce quality loss.
| Aspect | PTQ | QAT | Recommended Bitwidth |
|---|---|---|---|
| Speed to deploy | Fast, minimal setup | Slower, requires retraining | fp16 or int8 |
| Accuracy retention | Moderate, needs calibration | High, simulates quant effects | fp16 for safety, int8 for balance |
| Calibration needs | High, representative dataset required | Lower, built into training | Per-channel preferred for int8 |
| Hardware support | Widespread for int8 | Dependent on training stack | Match to target accelerator |
| Best use case | Quick edge deployment | Mission-critical accuracy | fp16 for GPUs, int8 for mobile |
Knowledge Distillation and Student-Teacher Methods
Knowledge distillation trains a smaller model to mimic a larger one. This teacher-student method helps a smaller network to match a bigger model’s outputs. It does this with much fewer parameters.
Response-based distillation and probability matching (KL divergence)
Response-based distillation looks at output distributions, not just labels. The student aims to match the teacher’s probabilities using KL divergence. This method captures soft information about class similarities.
Distillation for transformers and LLMs (DistilBERT examples)
Transformer models benefit from special distillation recipes. DistilBERT is a great example, showing a distilled transformer can be 40% smaller and 60% faster. Yet, it keeps similar accuracy to the original.
When distillation reduces inference time without large accuracy loss
Distillation is great when time and memory are tight. Small LLMs made this way often have good accuracy and save on latency and size. The main drawback is the initial cost of training a high-quality teacher.
But once trained, the student model offers big benefits. Pairing distillation with quantization and pruning can squeeze even more gains. Together, these methods enable running powerful models on phones and IoT devices, keeping most of the original model’s utility.
Low-rank Factorization and Weight Decomposition
Low-rank methods make dense weight matrices smaller. They keep most of the important information but use fewer parameters and calculations. By using SVD and other matrix techniques, we can replace big dense layers with smaller ones.
Tensor decomposition takes this idea further. It works with multi-dimensional kernels, which is great for convolutional and attention tensors.
Matrix and tensor factorization for dense layers
Begin by breaking down a big weight matrix with SVD. This gives us two or three smaller matrices. This makes the model smaller and faster, which is good when we’re dealing with lots of data or calculations.
For multi-axis weights, tensor decomposition like CP or Tucker is used. It splits tensors into smaller parts that fit well with modern computers.
Practical benefits for transformers and convolutions
Transformers get a boost from low-rank factorization in their attention and feed-forward blocks. These parts are where most of the work happens. Convolutional layers also get faster when their spatial or channel tensors can be simplified.
Techniques like LoRA are based on this idea. They use small updates instead of changing the whole model. This keeps the model size small while still adapting to new data.
Combining factorization with other methods
Using factorization with other methods like pruning, quantization, or distillation can make things even better. For example, combining SVD compression with int8 quantization can save a lot of memory. Distillation helps keep the model accurate.
Weight decomposition works best when it’s part of a carefully planned process. This process is tailored for the specific device and task at hand.
Palettization and Weight Clustering
Palettization is a way to make models smaller by using a few key values to represent many. It saves space by using compact indices instead of full-precision weights. This method turns big tensors into smaller index arrays and a small set of values.
This method is good for saving space but might slow things down a bit. It’s best for situations where size is more important than speed. For more details, check out palettization algorithms.
Concept of mapping weights to discrete palettes
Weight clustering groups similar values together. This way, a few key values can represent many. These values are stored in a palette, and indices point to them during use.
Weight sharing makes sure these values are used over and over. This reduces the number of unique values stored.
Storage reduction vs lookup overhead trade-offs
Lookup table compression makes models smaller without changing how they work. But, it adds steps that can slow things down. On some computers, this slowdown might not be worth the space saved.
Use cases where palettization is most effective
Palettization is great for models with lots of similar parameters. It’s also good when you’re dealing with limited space or bandwidth. This is true for mobile apps, firmware updates, and other situations where size matters.
| Scenario | Best Match | Main Benefit |
|---|---|---|
| On-device model downloads | palettization | Smaller binary and faster installs |
| Flash-limited wearables | weight clustering | Lower storage footprint |
| Over-the-air updates | lookup table compression | Reduced bandwidth and cost |
| Latency-sensitive inference | Not ideal unless hardware supports decoding | Potential runtime overhead |
Sparsity-Aware and Dynamic Architectures
Dynamic architectures allow models to adjust their performance in real-time. They use runtime scaling to fit the available resources of devices and meet latency goals. Designers aim for predictable trade-offs between power, speed, and model quality.
Early exits, adjustable width, and depth scaling are common tactics for adaptive compute. These methods let an app choose a smaller path for simple inputs and a larger path for complex ones. This keeps interactions fast while maintaining accuracy when needed.
Structured sparsity targets specific parts of models, like blocks or channels. This makes accelerators like Apple Neural Engine, Qualcomm NPUs, and NVIDIA GPUs work faster. Pruning blocks and channels leads to better real-world latency than random sparsity.
Designs that enable graceful degradation give teams control over UX under load. Systems can reduce model size or skip nonessential stages when resources are limited. Monitoring, clear fallbacks, and user feedback ensure a good experience.
Below is a concise comparison of common runtime strategies and their practical effects on devices.
| Pattern | Typical Devices | Benefit | Trade-off |
|---|---|---|---|
| Early exits | Smartphones, wearables | Lower average latency for easy inputs | Requires calibration to avoid accuracy loss |
| Width/depth scaling | Edge servers, NPUs | Flexible compute vs quality balance | Overhead to manage multiple paths |
| Block/channel sparsity | NPUs, GPUs, DSPs | Real throughput and memory gains | Needs hardware-aware pruning tools |
| Progressive refinement | AR glasses, mobile apps | Graceful degradation under constraints | Complex control logic and monitoring |
On-device Inference vs On-device Training: Different Constraints
Running models on phones is different from training them there. On-device inference uses pre-trained models for predictions. It needs only a little memory and quick compute bursts.
On-device training, though, updates model weights. It requires a lot of CPU, GPU, memory, and energy.
Why running models for prediction is more common
Most products use on-device inference because it fits current hardware. Phones from Apple and Samsung have special chips for fast, local predictions. This keeps latency low and privacy high, as data stays on the device.
Resource and operational barriers to training on devices
Training models on devices faces three big limits. First, the compute and memory needs often go beyond what mobile chips can handle for long. Second, keeping devices cool and batteries alive limits training time.
Third, devices have small, noisy datasets. This makes training unstable and prone to overfitting. Federated learning helps privacy but raises costs for communication and coordination.
Parameter-efficient approaches for edge personalization
Parameter-efficient fine-tuning makes edge training possible. Methods like LoRA and adapters change a few parameters instead of retraining the whole model.
LoRA adds small updates to transformer layers. Adapters add small modules between layers. Both reduce storage and compute needs, making on-device training easier.
| Aspect | On-device Inference | On-device Training | Edge Fine-tuning (LoRA, adapters) |
|---|---|---|---|
| Typical resource need | Low to moderate | High | Low to moderate |
| Energy and thermal impact | Short bursts, manageable | Sustained, often prohibitive | Short bursts, lower heat |
| Privacy profile | Strong if data stays local | Strong with local updates; federation adds complexity | Strong; fewer parameters transmitted |
| Network load | Low | High if central aggregation required | Low to moderate for parameter sync |
| Use cases | Real-time UX, offline features | Personalized models, continual learning | Personalization, domain adaptation on-device |
Compressing Large Language Models and Foundation Models
Large foundation models can have hundreds of billions of parameters. This makes them too big for mobile devices. Engineers use special methods to make these models smaller without losing too much performance. This section will explain how to make LLMs work on devices.
Low-rank adaptation methods reduce the number of parameters by adding small update modules to a base model. LoRA is a well-known method that uses low-rank matrices in certain layers. It’s great for adding specific updates without sending the whole model.
Distillation is another method that makes a large model smaller by transferring its behavior. Distilled LLMs can perform as well as the original model but use less resources. It’s perfect for when you need a model that works fast on limited hardware.
Recent studies have shown that combining different methods can be very effective. For example, you can make a model smaller by quantizing weights and pruning unnecessary parameters. Then, distill the result into an even smaller model. For more information, check out this guide on compressing large language models.
When deciding between adapters and distilled models, consider your goals. Use adapters or LoRA for frequent updates and personalization. Choose distilled models for fast, offline use with limited resources.
Edge deployments can benefit from using a combination of techniques. For example, you can fine-tune a model with adapters, then apply quantization and pruning. This approach helps balance model quality, size, and speed for real-world use.
| Use case | Recommended approach | Main benefit |
|---|---|---|
| On-device personalization | LoRA / adapters | Small updates, no full model shipping |
| Offline fast inference | Distilled LLMs + quantization | Low latency, reduced memory |
| Memory-constrained IoT | Pruning + 4-bit quantization | Reduced footprint with acceptable accuracy |
| Research and iteration | Teacher-student pipelines | Explores trade-offs before production |
Tooling, Libraries, and Hardware Ecosystem
The world of on-device AI tools is vast and expanding. Engineers pick tools based on the hardware they use and their development process. The choice often depends on the support for runtime and the quality of quantization libraries in each framework.
Frameworks offer different ways to compress models. TensorFlow Lite is great for mobile devices because it’s small and well-integrated. PyTorch quantization has tools for both eager-mode and FX, making it versatile for model tweaks. QKeras helps teams test quantized layers in Keras workflows.
Intel and Microsoft offer top-notch tools for servers and edge devices. Intel Neural Compressor makes quantization and sparsity tuning easy. Microsoft focuses on optimizing and deploying models to Azure and Windows devices.
Tools specific to platforms are key for running models on phones and embedded boards. Apple MLX and Core ML tools are perfect for iOS devices and Apple silicon. Mobile SDKs let developers use NPUs and GPU cores efficiently.
Optimizing for hardware is crucial for real-world performance. Different chips have unique features and memory handling. Choosing the right compression for each chip can improve performance, energy use, and speed.
Teams should try out different tools to find the best fit. Testing with real-world workloads on the target hardware is essential to avoid deployment issues.
Here’s a quick guide to help choose the right tool for your device class.
| Tool / Framework | Primary Strength | Best for | Notes on Hardware |
|---|---|---|---|
| TensorFlow Lite | Lightweight runtime and interpreter | Android, embedded devices, mobile apps | Optimized for mobile GPUs and DSPs; supports integer quantization |
| PyTorch quantization | Flexible quantization flows and developer ergonomics | Research-to-production pipelines, mobile via TorchScript | Works with mobile GPUs and some NPUs; check FX graph support |
| Intel Neural Compressor | Automated tuning for accuracy vs. performance | Edge servers, Intel hardware, x86 deployments | Targets Intel accelerators and CPUs; integrates with common frameworks |
| Apple MLX | Core ML model optimization and profiling | iOS apps and Apple silicon | Designed for Apple NPUs and integrated GPU; excels on iPhones and Macs |
| QKeras | Quantized layer design inside Keras | Academic experiments, early-stage product prototyping | Good for algorithm exploration before targeting specific accelerators |
Design and Process Considerations from Practitioner Interviews
The CHI ’24 interview study with 30 Apple experts uncovered important insights. They talked about design processes, trade-offs, and how to make models smaller. These insights show how product goals guide technical choices and how tools can help with on-device work.
Workflows matter: experienced engineers shared their ML workflows. They mentioned how they go back and forth between testing prototypes, getting user feedback, and checking hardware. They found that small tweaks can greatly improve performance without hurting the user experience.
Tacit knowledge about workflows and trade-offs
Practitioners discussed important decisions that aren’t often written about. They talked about when to accept a small drop in accuracy for a big speed gain. They also mentioned when to focus on making models work better on specific hardware. These decisions are often based on what real users need.
Interdisciplinary collaboration
Successful projects relied on close work between HCI, ML, product, UX, and engineering teams. UX research sets quality standards. Product managers then decide when to release new features. ML teams figure out how to meet those standards with models.
Pragmatic production heuristics
Teams preferred simpler models that still met user needs. They chose smaller models for easier updates and consistent performance on different devices. They valued keeping things running smoothly over tiny improvements in accuracy.
| Dimension | Practitioner Goal | Typical Action | Outcome |
|---|---|---|---|
| Latency | Maintain sub-100ms response | Quantize to int8 and profile on device | Lowered latency with acceptable accuracy |
| Battery | Minimize energy per inference | Use smaller architectures or sparsity-aware blocks | Reduced energy while preserving UX |
| Maintainability | Keep models easy to update | Favor compact models and clear CI for retraining | Simpler rollout and fewer regressions |
| Hardware diversity | Support many devices | Adopt hardware-aware ML workflows and fallbacks | Consistent behavior across phones and wearables |
| UX alignment | Meet user-perceived quality | Run user studies and compare compressed models | Validated trade-offs that prioritize experience |
For teams looking for practical advice, the study’s findings are outlined in a guide. It’s available at model compression in practice. This guide shows how to use tools and processes to make ML workflows more efficient.
Evaluation Strategies and Metrics for Compressed Models
When evaluating compressed models, start with measurements that reflect real-world use. Focus on how fast they run and how much memory they use. Also, track how much energy they consume to ensure they don’t overheat or drain the battery too fast.
Use datasets that closely match real-world scenarios for testing. This ensures that any improvements in theory actually make a difference in practice.
Combine traditional ML metrics with what users actually care about. Look at how accurate and reliable the models are. Also, test how well they perform under stress or when faced with unexpected inputs.
Small changes in how models work can have big effects. Look at what’s happening inside the model to understand these changes.
Create a simple table to compare different models. Include metrics like how fast they run, how much memory they use, and how much energy they consume. This makes it easy for everyone to see the trade-offs.
| Metric | What to Measure | Why It Matters |
|---|---|---|
| Latency | Median and tail latency on device | Drives perceived responsiveness for end users |
| Throughput | Queries per second under load | Defines capacity for concurrent scenarios |
| Memory | Runtime and peak allocation | Determines feasibility for small devices |
| Energy | mJ per inference and power draw | Impacts battery life and thermal limits |
| Accuracy | Task-specific scores and calibration loss | Ensures utility stays within acceptable bounds |
| Robustness | Performance on perturbed and rare inputs | Reveals brittleness introduced by compression |
Use A/B testing to see how compressed models affect real users. Compare them to a standard version and watch how users interact with them. This helps make sure the changes are worth it.
Keep an eye on how users behave after changes are made. This helps catch any problems early and fix them quickly.
Use tools that help teams work faster and better. Visual tools and trackers make it easier to spot and fix issues. For more on how to track and visualize compression experiments, check out this overview.
compression experiment tracking and visualization
Conclusion
On-device deployment of machine learning gives users more control. It improves privacy, makes things faster, and is more accessible. The CHI ’24 study shows that these benefits happen when teams use the right model compression strategy.
This strategy must fit the device’s memory, processing power, and battery life. It’s all about making design choices based on real devices and what users need.
Pruning, quantization, and knowledge distillation are key tools for making large models smaller and faster. Each method has its own trade-offs, like how accurate it is and how fast it works. It’s important to keep trying different combinations to find the best fit for devices like mobile NPUs and edge GPUs.
Success in production means focusing on how well things work in real life, not just on paper. Working together between product managers, UX designers, and engineers makes this easier. This way, we can make sure powerful models work well and safely on small devices.
FAQ
What is model compression and why is it essential for on-device AI?
Model compression makes machine learning models smaller and faster for devices with limited resources. It uses techniques like pruning, quantization, and knowledge distillation. These methods help keep models accurate and efficient on devices.
How does on-device ML improve privacy and responsiveness?
On-device ML keeps data on your device, not in the cloud. This reduces data exposure and supports local personalization. It also makes apps work better, even without internet.
When should I compress a model—during prototyping or before production?
During prototyping, focus on getting ideas right. For production, compress models to meet performance needs. Choose the smallest model that still works well and then fine-tune it.
What core compression methods should practitioners know?
Key methods include pruning, quantization, and knowledge distillation. Pruning cuts less important parts, quantization reduces precision, and distillation makes smaller models mimic larger ones. These methods can be used together for better results.
What are the trade-offs between unstructured and structured pruning?
Unstructured pruning saves more parameters but may not speed up devices. Structured pruning is better for hardware and gives consistent benefits. Choose structured pruning for better performance.
How do post-training quantization (PTQ) and quantization-aware training (QAT) differ?
PTQ is quick and converts models to lower precision after training. QAT makes models robust to lower precision during training. Use PTQ for fast tests and QAT for better precision.
What bitwidths are practical for mobile and edge devices?
fp16 and int8 are common and well-supported. fp16 saves memory and speeds up inference. int8 offers more compression but needs careful tuning.
How does knowledge distillation help for edge deployments?
Distillation trains smaller models to mimic larger ones. This makes models smaller and faster while keeping performance high. It’s great for devices with limited resources.
What is low-rank factorization and where does it help most?
Low-rank factorization breaks down dense matrices into smaller parts. It’s useful for dense layers and attention mechanisms. Pair it with other methods for more savings.
When is palettization (weight clustering) appropriate?
Palettization is good when weights cluster together. It saves space but can slow down lookup. It’s best for certain layers and when storage is key.
What are dynamic models and graceful degradation patterns?
Dynamic models adjust based on resources. Graceful degradation trades off accuracy for speed. These are useful for devices with limited resources.
Why is inference more common on-device than training?
Inference uses less resources than training. Training is hard on devices due to power and memory. Fine-tuning is a better option for updates.
How are large language models compressed for edge use?
LLMs use LoRA, distillation, and quantization for compression. Training a teacher model is a challenge. Use distilled models for fast inference and LoRA for updates.
What tools and frameworks support model compression and on-device deployment?
TensorFlow, PyTorch, and QKeras are popular. Apple Core ML and mobile SDKs offer hardware-specific optimizations. Choose tools based on your hardware for best results.
How should compressed models be evaluated for production?
Check latency, throughput, memory, and energy. Also, test accuracy and robustness. Use calibration datasets and A/B tests for evaluation.
What hardware-aware considerations must guide compression choices?
Consider the device type and hardware features. Structured sparsity and supported bitwidths are key. Design for your target hardware to achieve real-world benefits.
How do teams balance UX goals with compression technical choices?
Make choices based on product and UX needs. Collaboration between teams is essential. Use simpler models when they meet UX requirements.
Can multiple compression methods be combined safely?
Yes, combining methods can be beneficial. But, interactions can be complex. Test and validate carefully to ensure good results.
What practical heuristics came from practitioner interviews about on-device ML?
Prioritize operational metrics and choose hardware-aware methods. Use distillation or adapters as needed. Collaboration and tooling matching are key.