Machine Learning Model Optimization: Quantization, Pruning, Distillation & More
Imagine you baked the most delicious cake 🎂 in the world. But it takes 6 hours to bake, uses a whole bag of flour, and the oven runs all night. Now imagine a way to make the same cake in 45 minutes, with half the flour, and the oven cools down in 10 minutes.
That is exactly what Model Optimization does for AI! 🚀
You already trained a great ML model — but it's too slow, too big,
or uses too much memory. Model Optimization is the art of making it
faster, smaller, and smarter — without ruining its quality.
📱 Your phone can't run a 70-billion-parameter AI model — it barely fits on a server rack!
⚡ Users leave if your app takes more than 2 seconds to respond.
💸 Cloud GPU bills for unoptimised models can cost thousands of dollars per day.
🌍 Optimised models use less electricity — better for the planet too! 🌱
🗺️ The Complete Model Optimization Universe
Model Optimization is not just one trick — it's a toolkit of techniques. Think of it like tuning a racing car 🏎️: you adjust the engine, reduce the car's weight, use better tyres, and program smarter gear shifts. Each change helps individually — together, they make the car unbeatable!
┌─────────────────────────────────────────────────────────────────────────┐ │ 🏆 MODEL OPTIMIZATION TECHNIQUES │ ├─────────────────────────────────────────────────────────────────────────┤ │ │ │ BEFORE TRAINING DURING TRAINING AFTER TRAINING │ │ ───────────────── ──────────────── ────────────── │ │ • Architecture • Learning Rate • Quantization │ │ Search (NAS) Scheduling • Pruning │ │ • Feature • Regularization • Knowledge │ │ Engineering (L1, L2, Dropout) Distillation │ │ • Data Augmentation • Early Stopping • LoRA / │ │ • Mixed Precision PEFT │ │ Training (FP16) • ONNX / TensorRT │ │ • Speculative │ │ ALWAYS: Decoding │ │ • Hyperparameter Tuning (Optuna, Ray Tune, Grid Search) │ │ │ └─────────────────────────────────────────────────────────────────────────┘
🎛️ Technique 1: Hyperparameter Tuning
Before a model even starts learning, you have to make some decisions: How fast should it learn? How many times should it see the data? How many layers should the neural network have?
These decisions are called Hyperparameters. The model doesn't learn them — you have to set them manually. And getting them right can be the difference between a model that is 70% accurate and one that is 95% accurate! 🎯
You're baking biscuits 🍪. The recipe has settings:
- Oven temperature = Learning Rate (too hot = burns, too cold = raw)
- Baking time = Number of Epochs (too short = undercooked, too long = overdone)
- Amount of flour = Batch Size (affects texture of learning)
The 3 Main Tuning Strategies
1️⃣ GRID SEARCH — Try EVERY possible combination (slow but thorough) Learning rates: [0.001, 0.01, 0.1] Max depths: [3, 5, 7] → Tests: 3 × 3 = 9 combinations total (like checking every box on a form) 2️⃣ RANDOM SEARCH — Try RANDOM combinations (faster, surprisingly effective) Randomly picks 10 combinations out of the 9000 possible ones → Often finds good answers with 10x less computing! 3️⃣ BAYESIAN OPTIMISATION — Smart search Uses past results to PREDICT which combination to try next → Like a detective 🕵️ who uses clues to find the answer faster → Tools: Optuna, Ray Tune, HyperOpt
We use Optuna — the most popular hyperparameter tuning library. It will automatically try different combinations of learning rate and tree depth, learn from each attempt, and zero in on the best settings for a Random Forest model. Think of it as a smart robot trying different oven temperatures until it bakes the perfect biscuit!
# Install: pip install optuna scikit-learn import optuna from sklearn.ensemble import RandomForestClassifier from sklearn.datasets import load_iris from sklearn.model_selection import cross_val_score data = load_iris() X, y = data.data, data.target # 🎯 Objective function: Optuna will call this many times, # changing n_estimators and max_depth each time def objective(trial): n_estimators = trial.suggest_int('n_estimators', 50, 300) max_depth = trial.suggest_int('max_depth', 2, 15) min_samples = trial.suggest_int('min_samples_split', 2, 10) model = RandomForestClassifier( n_estimators=n_estimators, max_depth=max_depth, min_samples_split=min_samples, random_state=42 ) # Cross-validation: 5-fold (tests on 5 different slices of data) score = cross_val_score(model, X, y, cv=5).mean() return score # Run 100 trials — Optuna learns from each one study = optuna.create_study(direction='maximize') study.optimize(objective, n_trials=100) print("🏆 Best Accuracy:", study.best_value) print("🎛️ Best Settings:", study.best_params) # Output → Best Accuracy: 0.9733 # Best Settings: {'n_estimators': 187, 'max_depth': 8, ...}
🛡️ Technique 2: Regularization (Fighting Overfitting)
Have you ever studied so hard for a specific exam that you memorised every past question word-for-word? Then a slightly different question appeared on the actual exam — and you were lost! That's exactly what Overfitting is in ML.
The model memorises the training data perfectly but fails on new, unseen data. Regularization is the discipline that prevents this. It's like a strict teacher who says: "Don't memorise — understand the concept!" 📚
WITHOUT Regularization: WITH Regularization: ───────────────────── ────────────────────────── Training Accuracy: 99% ✅ Training Accuracy: 92% ✅ Test Accuracy: 65% ❌ Test Accuracy: 91% ✅ (Model memorised training data) (Model learned general patterns!) ───────────────────────────────────────────────────────────────── The gap between train & test accuracy tells you if you're overfitting!
The Main Regularization Techniques:
L1 Regularization (Lasso) 🔪 — Aggressively pushes small weights to exactly zero, effectively removing unimportant features. Like a ruthless editor who deletes whole paragraphs.
L2 Regularization (Ridge) 🎚️ — Gently shrinks all weights towards zero but never completely removes them. Like turning down the volume on every feature equally.
Dropout 🎲 — Randomly "switches off" a percentage of neurons during each training step. Forces the network to not rely on any single neuron — like a football team training without their star player so everyone else gets better!
Early Stopping 🛑 — Monitors validation accuracy after every epoch. The moment it stops improving, training halts automatically — preventing the model from over-learning.
We build a neural network with Dropout layers added inside it. During training, these layers randomly switch off 30% of neurons each step. We also use Early Stopping — so training automatically stops when the model stops getting better on validation data. This prevents both overfitting and wasted training time!
import tensorflow as tf from tensorflow.keras import layers, callbacks # Build a neural network with Dropout Regularization model = tf.keras.Sequential([ layers.Dense(128, activation='relu', input_shape=(20,)), layers.Dropout(0.3), # 30% neurons randomly switched off each step layers.Dense(64, activation='relu'), layers.Dropout(0.3), # Again 30% dropout in second layer layers.Dense(1, activation='sigmoid') ]) model.compile(optimizer='adam', loss='binary_crossentropy', metrics=['accuracy']) # Early Stopping: stop training if val_loss doesn't improve for 10 epochs early_stop = callbacks.EarlyStopping( monitor='val_loss', patience=10, # Wait 10 epochs before giving up restore_best_weights=True # Roll back to the best checkpoint ) model.fit( X_train, y_train, epochs=200, validation_split=0.2, callbacks=[early_stop], verbose=1 ) # Training might stop at epoch 47 instead of 200 — saving time AND accuracy!
Never train for a fixed, arbitrary number of epochs (like always 100 or 500). Always use Early Stopping with
restore_best_weights=True.
Training too long is one of the most common beginner mistakes that silently kills model quality!
📦 Technique 3: Quantization
Imagine you're measuring the distance between two cities. You could say it's 384.7293847 kilometres (very precise). Or you could say 385 km — close enough for most purposes, and way simpler!
Quantization does exactly this with a model's internal numbers (weights). It converts them from super-precise 32-bit floating-point numbers to much smaller 8-bit integers — with very little loss in accuracy.
ORIGINAL (FP32) QUANTIZED (INT8) ───────────────── ────────────────────────────────── Weight: 0.38472981 Weight: 49 (out of 0-255) Weight: -0.12934821 Weight: 113 Weight: 0.87123456 Weight: 222 ───────────────── ────────────────────────────────── Memory per weight: 32 bits Memory per weight: 8 bits Model size: 400 MB ──────► Model size: ~100 MB (75% smaller! 🎉) Speed: Normal ──────► Speed: 2–4× FASTER ⚡ Accuracy: 95.2% ──────► Accuracy: 94.8% (barely changed ✅)
Two Types of Quantization :
PTQ — Post-Training Quantization 🏁
You train the model normally at full precision (FP32), then quantize it after training.
Fast and easy — great starting point. Works well for most models.
QAT — Quantization-Aware Training 🧠
You simulate quantization errors during training itself.
The model learns to be "tough" against the precision loss.
More effort but higher final accuracy after quantization.
We take a trained PyTorch model and apply Post-Training Dynamic Quantization. This means we convert its internal numbers from 32-bit to 8-bit precision automatically — making the model smaller and faster to run, without needing to retrain it at all!
import torch import torch.nn as nn # Simple example model (pretend this is already trained) class SimpleModel(nn.Module): def __init__(self): super().__init__() self.fc1 = nn.Linear(784, 256) self.fc2 = nn.Linear(256, 128) self.fc3 = nn.Linear(128, 10) self.relu = nn.ReLU() def forward(self, x): x = self.relu(self.fc1(x)) x = self.relu(self.fc2(x)) return self.fc3(x) original_model = SimpleModel() # 📦 Apply Dynamic Quantization (INT8) — no retraining needed! quantized_model = torch.quantization.quantize_dynamic( original_model, {nn.Linear}, # Which layers to quantize dtype=torch.qint8 # Convert to 8-bit integers ) # Compare sizes import os torch.save(original_model.state_dict(), '/tmp/original.pth') torch.save(quantized_model.state_dict(), '/tmp/quantized.pth') orig_size = os.path.getsize('/tmp/original.pth') / 1024 quant_size = os.path.getsize('/tmp/quantized.pth') / 1024 print(f"Original size : {orig_size:.1f} KB") print(f"Quantized size: {quant_size:.1f} KB") print(f"Size reduction: {(1 - quant_size/orig_size)*100:.0f}% smaller! 🎉")
✂️ Technique 4: Model Pruning
Imagine a huge, overgrown tree 🌳 in your garden. Some branches are strong and full of leaves — they're doing great work. Other branches are thin, dry, and barely alive — they contribute almost nothing. A good gardener prunes away the dead branches, and the tree actually grows healthier and stronger!
Model Pruning does the same. A trained neural network has millions of connections (weights) — but many of them are nearly zero and contribute almost nothing to the final prediction. Pruning removes these useless connections, making the model smaller and faster.
BEFORE PRUNING: AFTER PRUNING (50%): ─────────────── ──────────────────────────── ●══════════●══════════● ●══════════● ══════════● ║ ║ ║ ║ ║ ║ ║ ║ ║ ║ ║ ●══╬══╬══╬═●══╬══╬══╬═● ● ● ● ║ ║ ║ ║ ║ ║ ║ ║ ●══════════●══════════● ●══════════● ● 10,000 connections 5,000 connections remain Model size: 80 MB Model size: ~45 MB ✅ Accuracy: 94.1% Accuracy: 93.8% (barely changed!)
Types of Pruning:
Unstructured Pruning — Removes individual weights scattered throughout the network. Very fine-grained. Achieves high compression but may need special hardware to see speed improvements.
Structured Pruning — Removes entire neurons, filters, or layers at once. Immediately gives you a smaller, faster model on any hardware. More aggressive but the speed gains are real and immediate.
Iterative Pruning — The gold standard ! Prune a little → fine-tune → prune a little more → fine-tune → repeat. Like slowly letting out air from a balloon while keeping its shape. 🎈
We use PyTorch's built-in pruning tool to remove the 40% smallest weights from a linear layer. These are the weights closest to zero — the "dry branches" that contribute the least. After pruning, we also permanently remove them so the model is actually lighter.
import torch import torch.nn as nn import torch.nn.utils.prune as prune model = SimpleModel() # From previous example # Step 1: Prune 40% of weights in fc1 (the smallest absolute values) prune.l1_unstructured(model.fc1, name='weight', amount=0.40) # Step 2: Check sparsity (how many weights are now zero) total = model.fc1.weight.nelement() zeros = (model.fc1.weight == 0).sum().item() sparsity = 100.0 * zeros / total print(f"Sparsity in fc1: {sparsity:.1f}%") # Output → Sparsity in fc1: 40.0% ✅ # Step 3: Make pruning permanent (actually remove the weights) prune.remove(model.fc1, 'weight') print("✅ Pruning complete! Model is now leaner.") # After this: fine-tune the model again for a few epochs # to recover the small accuracy loss from pruning
👨🏫 Technique 5: Knowledge Distillation
Meet Professor Einstein 🧠 — he knows everything about physics. Now imagine a student who studies under Professor Einstein for years, learning not just the textbook answers but also how the Professor thinks — his intuitions, his shortcuts, his reasoning patterns.
That student could become almost as brilliant as the Professor — but with a smaller brain and less storage space! That's Knowledge Distillation: a small student model learns from a large teacher model.
TEACHER MODEL (Large, slow, expensive):
─────────────────────────────────────────────────────────────
Input: Cat Photo 📷
Output: { Cat: 92%, Dog: 5%, Tiger: 2%, Lion: 1% }
↑
These SOFT probabilities carry rich knowledge!
The model knows "cat is somewhat dog-like" — valuable info!
STUDENT MODEL (Tiny, fast, cheap) learns FROM these soft outputs:
─────────────────────────────────────────────────────────────
Doesn't just learn "Cat = 1, Dog = 0"
Also learns the probability relationships the Teacher discovered!
Result: Student gets ~97% of Teacher's accuracy at 50% of its size 🎯
Famous Examples :
- DistilBERT — 40% smaller than BERT, 60% faster, retains 97% of performance
- TinyLlama — Distilled from Llama, runs on a laptop GPU
- MobileNet — Distilled image model that runs on your smartphone camera
- Phi-3 Mini (Microsoft, 2024–2026) — Tiny but powerful LLM via distillation
• You need to deploy AI on a mobile phone or edge device 📱
• You have a large, expensive model and need a cheaper version for production
• Pruning and quantization alone aren't enough to hit your size target
• You want to create a specialised small model from a large general-purpose one
We train a small student model to mimic a larger teacher model. The student learns from two things simultaneously:
1. The real labels (hard targets — actual correct answers)
2. The teacher's soft probabilities (soft targets — the rich knowledge the teacher has)
The combination makes the student far smarter than if it only saw hard labels.
import torch import torch.nn as nn import torch.nn.functional as F # Temperature parameter: higher = softer probability distribution TEMPERATURE = 4.0 ALPHA = 0.7 # 70% weight on teacher's knowledge, 30% on real labels def distillation_loss(student_logits, teacher_logits, true_labels): """ Combines two loss signals: 1. Soft loss → student learns from teacher's probability outputs 2. Hard loss → student also learns from real correct labels """ # Soft Loss: KL Divergence between student and teacher (softened) soft_student = F.log_softmax(student_logits / TEMPERATURE, dim=1) soft_teacher = F.softmax(teacher_logits / TEMPERATURE, dim=1) soft_loss = F.kl_div(soft_student, soft_teacher, reduction='batchmean') soft_loss *= (TEMPERATURE ** 2) # Scale back up # Hard Loss: Standard cross-entropy with real labels hard_loss = F.cross_entropy(student_logits, true_labels) # Blend: 70% teacher knowledge + 30% real labels total_loss = ALPHA * soft_loss + (1 - ALPHA) * hard_loss return total_loss print("✅ Distillation loss function ready!") print("Student model will learn from BOTH teacher wisdom AND real labels.")
🔧 Technique 6: LoRA & PEFT (For Large Language Models)
This is one of the hottest techniques. Imagine you have a brilliant doctor who knows general medicine. You want to teach them to become a specialist in, say, heart surgery. Do you send them back to medical school for 8 years? NO! You give them a focused 6-month specialisation course. Much smarter!
LoRA (Low-Rank Adaptation) does this for LLMs. Instead of retraining all billions of parameters (expensive!), it freezes the original model and only trains a tiny set of small "adapter" matrices that are plugged in beside the original weights.
FULL FINE-TUNING (expensive): ─────────────────────────────────────────────────────────── Llama 3 (8B parameters) — all 8 billion weights updated GPU required: 8× A100 80GB | Time: days | Cost: $$$$$ LoRA FINE-TUNING (smart & cheap): ─────────────────────────────────────────────────────────── Llama 3 (8B parameters) — FROZEN ❄️ (not touched) New tiny adapters: only ~20 MILLION parameters trained GPU required: 1× RTX 4090 | Time: hours | Cost: $ Result: 0.25% of the parameters → similar fine-tuning quality! 🤯
PEFT (Parameter-Efficient Fine-Tuning) is the family name — LoRA is the most popular member. Other PEFT techniques include Prefix Tuning, Prompt Tuning, and IA³. .LoRA is the default approach for fine-tuning any LLM.
We use Hugging Face PEFT library to add LoRA adapters to a language model. The original model weights stay frozen — only our tiny new adapter layers (about 0.5% of total parameters) get trained. This is how you fine-tune a powerful LLM on a single laptop GPU!
# Install: pip install peft transformers from peft import LoraConfig, get_peft_model, TaskType from transformers import AutoModelForCausalLM # Load a base language model (e.g. a small GPT or Llama variant) base_model = AutoModelForCausalLM.from_pretrained("gpt2") # Configure LoRA settings lora_config = LoraConfig( task_type=TaskType.CAUSAL_LM, r=16, # Rank of adapter matrices (smaller = fewer params) lora_alpha=32, # Scaling factor for adapter weights lora_dropout=0.05, # Dropout for regularization target_modules=["c_attn"] # Which layers to apply LoRA to ) # Apply LoRA — wraps the original model with tiny adapter layers peft_model = get_peft_model(base_model, lora_config) # See how few parameters we're actually training! peft_model.print_trainable_parameters() # Output → trainable params: 294,912 || all params: 124,734,720 # trainable%: 0.24% ← Only 0.24% being trained! 🤯
⚡ Technique 7: Mixed Precision Training
When your GPU trains a model, it does millions of maths operations per second. By default, all these calculations use 32-bit floating-point (FP32) precision. But many of these calculations don't need to be that precise!
Mixed Precision Training uses 16-bit (FP16 or BF16) for most calculations (faster, uses half the memory) while keeping FP32 for the sensitive parts (gradients, loss scaling) that really need precision.
NORMAL TRAINING (FP32 only): ───────────────────────────────────────────────────────── GPU Memory used: 24 GB | Training speed: 1.0× (baseline) Max batch size: 32 images per step MIXED PRECISION TRAINING (FP16 + FP32): ───────────────────────────────────────────────────────── GPU Memory used: 14 GB | Training speed: 1.8× FASTER ⚡ Max batch size: 64 images per step (2× more data per step!) Final accuracy: Same as FP32 ✅ (no quality loss!)
We enable Automatic Mixed Precision (AMP) in PyTorch. The
autocast context manager automatically decides which operations
should use FP16 (fast) and which must stay in FP32 (accurate).
The GradScaler prevents the tiny FP16 gradients from vanishing to zero.
Just 3 extra lines of code = almost 2× training speed!
import torch from torch.cuda.amp import autocast, GradScaler model = SimpleModel().cuda() optimizer = torch.optim.Adam(model.parameters(), lr=1e-3) scaler = GradScaler() # Prevents FP16 gradient underflow for epoch in range(10): for batch_X, batch_y in dataloader: batch_X, batch_y = batch_X.cuda(), batch_y.cuda() optimizer.zero_grad() # autocast automatically uses FP16 where safe with autocast(): output = model(batch_X) loss = criterion(output, batch_y) # Scaled backward pass (avoids FP16 precision issues) scaler.scale(loss).backward() scaler.step(optimizer) scaler.update() print("✅ Training with Mixed Precision — 1.8× faster, same accuracy!")
🚢 Technique 8: Model Export & Runtime Optimization (ONNX + TensorRT)
You've trained your model in Python. But in production, you might need to run it on NVIDIA GPUs, ARM chips (phones), Intel CPUs, or custom AI accelerators. Each has a different "language". ONNX is the universal translator!
ONNX (Open Neural Network Exchange) converts your model into a universal format that any hardware/software can understand and optimise. TensorRT (by NVIDIA) then supercharges it specifically for NVIDIA GPUs — fusing operations, optimising memory layout, and applying hardware-specific tricks you couldn't do in PyTorch or TensorFlow.
OPTIMISATION PIPELINE (Standard):
PyTorch Model
│
▼ (export)
ONNX Format ←── Universal: works on ANY hardware
│
▼ (compile for GPU)
TensorRT Engine ←── Hardware-specific: maximum NVIDIA GPU speed
│
▼
2–5× faster inference vs original PyTorch! ⚡⚡⚡
Other Runtime Options:
• ONNX Runtime → Great for CPUs and cloud
• TorchScript → Stay within PyTorch ecosystem
• Apache TVM → For custom/embedded hardware
• Core ML → Apple devices (iPhone, Mac)
🔲 Train model in PyTorch or TensorFlow
🔲 Apply Quantization (INT8 or FP16)
🔲 Export to ONNX format
🔲 Optimise with TensorRT (for NVIDIA GPU) or ONNX Runtime (for CPU)
🔲 Benchmark latency and throughput — measure, don't guess!
🔲 Set up monitoring for model drift in production
🔮 Technique 9: Speculative Decoding (For LLMs)
This one is brand new and mind-blowing! 🤯 When a Large Language Model generates text, it predicts one word at a time — painfully slow for long responses.
Speculative Decoding uses a tiny fast "draft" model to guess several words ahead, then the big model verifies the whole sequence in one shot. If the draft was correct, you skip ahead multiple tokens at once!
NORMAL GENERATION (one word at a time — slow 🐢): ───────────────────────────────────────────────────── "The" → wait → "cat" → wait → "sat" → wait → "on" → wait → "the" (5 separate expensive model calls for just 5 words) SPECULATIVE DECODING (batch verification — fast 🚀): ───────────────────────────────────────────────────── Small model GUESSES: "cat sat on the mat" (5 words at once, fast!) Big model CHECKS: ✅ ✅ ✅ ✅ ✅ (one batch check!) Result: 5 words accepted in ~1.2 model calls instead of 5! Speed gain: 2–3× faster generation 🎉
This technique is used by Google Gemini, Anthropic Claude, and OpenAI GPT in production to deliver fast responses even for very large models!
🔄 The Complete End-to-End Optimization Flow
Now let's put it all together into one complete journey — from a raw, unoptimised model to a production-ready, blazing-fast system:
STEP 1: 🎯 Define Your Problem
What metric matters? (Accuracy? Latency? Memory? Cost?)
Where will it run? (Cloud GPU? Mobile? Edge device?)
│
STEP 2: 🏗️ Train a Baseline Model
Standard training, no optimisation yet
Measure: accuracy, size, inference time
│
STEP 3: 🎛️ Hyperparameter Tuning (Optuna / Ray Tune)
Find the best learning rate, architecture, etc.
Squeeze all performance out of your model design
│
STEP 4: 🛡️ Apply Regularization During Training
Dropout + L2 + Early Stopping → clean, generalisable model
│
STEP 5: ✂️ Post-Training Pruning
Remove 20–50% of low-value weights
Fine-tune for 2–5 more epochs to recover accuracy
│
STEP 6: 📦 Quantization (PTQ or QAT)
Convert FP32 → INT8 or FP16
Validate accuracy drop is acceptable (usually <1%)
│
STEP 7: 👨🏫 Knowledge Distillation (if needed)
If model is still too large for deployment target →
Train a smaller student model using the optimised model as teacher
│
STEP 8: 🚢 Export to ONNX → TensorRT / ONNX Runtime
Hardware-level optimisation for your target platform
│
STEP 9: 📊 Benchmark & Monitor
Measure: latency (p50, p95, p99), throughput, memory usage
Set up drift monitoring in production
│
STEP 10: 🏆 DEPLOY! Your model is production-ready! 🎉
📊 All Techniques at a Glance
| Technique | What it does | Typical Gain | Difficulty |
|---|---|---|---|
| Hyperparameter Tuning | Finds the best model settings | +5–20% accuracy | ⭐ Beginner |
| Regularization | Prevents overfitting | Stable, generalised model | ⭐ Beginner |
| Quantization | Reduces numeric precision | 75% smaller, 2–4× faster | ⭐⭐ Intermediate |
| Pruning | Removes useless connections | 30–70% size reduction | ⭐⭐ Intermediate |
| Knowledge Distillation | Trains a smaller student model | 50% smaller, 97% accuracy | ⭐⭐⭐ Advanced |
| LoRA / PEFT | Fine-tune LLMs cheaply | 99.7% fewer trainable params | ⭐⭐ Intermediate |
| Mixed Precision | Uses FP16 during training | 1.5–2× faster training | ⭐ Beginner (3 lines!) |
| ONNX + TensorRT | Hardware-level inference speed | 2–5× faster inference | ⭐⭐ Intermediate |
| Speculative Decoding | Faster LLM text generation | 2–3× faster token generation | ⭐⭐⭐ Advanced |
✅ ALWAYS DO These:
• Start with hyperparameter tuning — biggest improvement for least effort
• Always measure baseline performance before optimising
• Apply techniques one at a time — measure after each step
• Validate accuracy after every optimization — don't assume it's fine
• Use Early Stopping in every training run without exception
• Stack techniques: Prune → Quantize → ONNX for multiplicative wins
• Never deploy a model you haven't benchmarked on target hardware
• Never skip validation after pruning or quantizing — accuracy can silently crash
• Never over-prune in one shot — always use iterative pruning
• Never ignore model drift in production — models degrade over time!
• Never assume a bigger model is better — a well-optimised small model often wins
• Never use Grid Search on large models — waste of GPU time and money
🎯 Final Recap — All Optimization Techniques
- 🎛️ Hyperparameter Tuning — Find the perfect "recipe settings" using Optuna
- 🛡️ Regularization — Prevent memorisation with Dropout, L1/L2, Early Stopping
- 📦 Quantization — Shrink numbers from 32-bit to 8-bit → 75% smaller
- ✂️ Pruning — Cut dead-weight connections → smaller, faster model
- 👨🏫 Knowledge Distillation — Teach a tiny student from a giant teacher
- 🔧 LoRA / PEFT — Fine-tune LLMs with 0.25% of the normal cost
- ⚡ Mixed Precision — Use FP16 during training → 1.8× speed, same accuracy
- 🚢 ONNX + TensorRT — Hardware-optimised inference → 2–5× faster
- 🔮 Speculative Decoding — LLM text 2–3× faster using draft-then-verify
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