Chapter 44 — AI Hardware Acceleration & On-Device AI

Overview

Optimize performance and cost with accelerators and compilers; enable private, low-latency on-device AI.

Modern AI workloads demand specialized hardware and optimized runtimes to achieve acceptable performance and economics. This chapter covers hardware acceleration strategies from cloud GPUs/TPUs to on-device inference, compiler optimizations, and the architectural tradeoffs that shape deployment decisions.

Topics

• GPU/TPU: memory, kernel fusion, mixed precision, batching.
• Compilers: TensorRT, XLA, ONNX Runtime, quantization and pruning.
• On-device: model size limits, privacy, offline-first design.

Deliverables

• Performance benchmarks, deployment profiles, and cost model.

Why It Matters

The right acceleration strategy can reduce cost per request, improve latency, and enable private on-device experiences—often unlocking use cases that are infeasible on CPU or in the cloud.

Business Impact:

• Cost Optimization: A well-tuned GPU deployment can be 10-100x cheaper per inference than CPU
• Latency: On-device models eliminate network round-trips, achieving <100ms response times
• Privacy: Local inference keeps sensitive data on-device, meeting regulatory requirements
• Scale: Proper batching and quantization enable serving millions of requests with manageable infrastructure
• ROI: Hardware acceleration investments typically pay back in 6-12 months through reduced cloud costs

Hardware Architecture Comparison

GPU vs TPU vs CPU: When to Use What

Hardware Specification Deep-Dive

graph TB
    subgraph "Cloud Acceleration Stack"
        A[Model Input] --> B{Batch Size}
        B --> C[GPU Memory]
        C --> D[Tensor Cores]
        D --> E[Kernel Fusion]
        E --> F[Mixed Precision FP16/BF16]
        F --> G[Output]
    end

    subgraph "On-Device Stack"
        H[Model Input] --> I[Quantization INT8/INT4]
        I --> J[NPU/DSP]
        J --> K[Model Cache]
        K --> L[Output]
    end

    style D fill:#90EE90
    style J fill:#FFB6C1

Hardware & Runtime Deep-Dive

GPU/TPU Optimization Strategies

Memory Bandwidth Optimization

Modern accelerators are often memory-bandwidth bound rather than compute-bound. Understanding data movement is critical:

# Example: Memory-efficient attention with Flash Attention
import torch
from flash_attn import flash_attn_func

def standard_attention(Q, K, V):
    # Memory: O(N²) - stores full attention matrix
    attention_scores = Q @ K.transpose(-2, -1) / math.sqrt(d_k)
    attention_probs = F.softmax(attention_scores, dim=-1)
    return attention_probs @ V

def flash_attention(Q, K, V):
    # Memory: O(N) - tiled computation, no materialized attention matrix
    # 2-4x faster, enables longer sequences
    return flash_attn_func(Q, K, V)

# Performance comparison
# Standard: 512 tokens = 2GB memory, 45ms
# Flash: 512 tokens = 0.5GB memory, 18ms
# Standard: 2048 tokens = OOM
# Flash: 2048 tokens = 2GB memory, 120ms

Tensor Cores and Mixed Precision

NVIDIA Tensor Cores provide 8-16x speedup for specific operations when using FP16/BF16:

import torch
from torch.cuda.amp import autocast, GradScaler

# Automatic Mixed Precision (AMP) training
model = YourModel().cuda()
optimizer = torch.optim.Adam(model.parameters())
scaler = GradScaler()

for batch in dataloader:
    optimizer.zero_grad()

    # Automatically use FP16 where safe
    with autocast():
        output = model(batch)
        loss = criterion(output, targets)

    # Scale loss to prevent underflow
    scaler.scale(loss).backward()
    scaler.step(optimizer)
    scaler.update()

# Results: 2-3x speedup, same accuracy, 40% memory reduction

Kernel Fusion and Operator Optimization

# Unfused operations - multiple kernel launches
def unfused_gelu(x):
    return x * 0.5 * (1.0 + torch.erf(x / math.sqrt(2.0)))

# Fused kernel - single GPU operation
import triton
import triton.language as tl

@triton.jit
def fused_gelu_kernel(x_ptr, output_ptr, n_elements, BLOCK_SIZE: tl.constexpr):
    pid = tl.program_id(0)
    block_start = pid * BLOCK_SIZE
    offsets = block_start + tl.arange(0, BLOCK_SIZE)
    mask = offsets < n_elements

    x = tl.load(x_ptr + offsets, mask=mask)
    # Compute GELU in single kernel
    output = x * 0.5 * (1.0 + tl.erf(x / 1.41421356237))
    tl.store(output_ptr + offsets, output, mask=mask)

# Performance: Unfused=2.1ms, Fused=0.7ms (3x faster)

Batch Sizing Strategy

Key Insight: Optimal batch size balances throughput and latency. For user-facing apps, batch=8-32 is often ideal; for offline jobs, maximize batch size.

Quantization: Accuracy vs Performance Tradeoffs

Quantization reduces model precision from FP32/FP16 to INT8/INT4, dramatically improving speed and memory:

Quantization Methods Comparison

Practical Quantization Example

import torch
from transformers import AutoModelForCausalLM, AutoTokenizer
from auto_gptq import AutoGPTQForCausalLM, BaseQuantizeConfig

# Original model: 13B parameters × 2 bytes (FP16) = 26GB
model_id = "meta-llama/Llama-2-13b-hf"

# Method 1: 8-bit quantization with bitsandbytes
from transformers import BitsAndBytesConfig

quantization_config = BitsAndBytesConfig(
    load_in_8bit=True,
    llm_int8_threshold=6.0,
    llm_int8_has_fp16_weight=False,
)

model_8bit = AutoModelForCausalLM.from_pretrained(
    model_id,
    quantization_config=quantization_config,
    device_map="auto"
)
# Result: 13GB memory, 2.5x faster, <1% accuracy drop

# Method 2: 4-bit GPTQ quantization
quantize_config = BaseQuantizeConfig(
    bits=4,
    group_size=128,
    desc_act=False,
)

model_4bit = AutoGPTQForCausalLM.from_pretrained(
    model_id,
    quantize_config=quantize_config
)
# Result: 7GB memory, 4x faster, 2-3% accuracy drop

# Accuracy comparison on MMLU benchmark
# FP16: 54.2% accuracy
# 8-bit: 54.0% accuracy (-0.2%)
# 4-bit GPTQ: 52.8% accuracy (-1.4%)
# 4-bit AWQ: 53.5% accuracy (-0.7%)

Outlier Handling in Quantization

# Challenge: Outlier activations destroy quantization quality
# Solution: Mixed precision + outlier extraction

import torch.nn as nn

class MixedPrecisionLinear(nn.Module):
    def __init__(self, in_features, out_features, outlier_threshold=6.0):
        super().__init__()
        self.weight_int8 = nn.Parameter(torch.zeros(out_features, in_features, dtype=torch.int8))
        self.scale = nn.Parameter(torch.ones(out_features))
        self.outlier_cols = []
        self.outlier_weights = None

    def forward(self, x):
        # Separate outlier dimensions (keeps FP16)
        outlier_output = torch.matmul(x[:, self.outlier_cols], self.outlier_weights)

        # INT8 matmul for remaining dimensions
        regular_output = torch.matmul(x, self.weight_int8.float()) * self.scale

        return regular_output + outlier_output

# Maintains 99%+ accuracy while achieving 3-4x speedup

Compiler Optimization

Modern AI compilers perform graph-level optimizations that are infeasible to implement manually:

Compiler Stack Comparison

graph LR
    A[Model Definition] --> B{Framework}
    B -->|PyTorch| C[TorchScript]
    B -->|TensorFlow| D[XLA]
    B -->|ONNX| E[ONNX Runtime]

    C --> F[TensorRT]
    D --> F
    E --> F

    F --> G[Optimized Inference]

    C --> H[TVM]
    D --> H
    E --> H

    H --> I[Multi-Backend Deployment]

    style F fill:#90EE90
    style H fill:#87CEEB

TensorRT Optimization Example

import tensorrt as trt
import pycuda.driver as cuda
import pycuda.autoinit

# Convert PyTorch model to TensorRT
def build_engine(onnx_path, fp16_mode=True, int8_mode=False):
    logger = trt.Logger(trt.Logger.WARNING)
    builder = trt.Builder(logger)
    network = builder.create_network(1 << int(trt.NetworkDefinitionCreationFlag.EXPLICIT_BATCH))
    parser = trt.OnnxParser(network, logger)

    # Parse ONNX model
    with open(onnx_path, 'rb') as model:
        parser.parse(model.read())

    config = builder.create_builder_config()
    config.max_workspace_size = 1 << 30  # 1GB

    if fp16_mode:
        config.set_flag(trt.BuilderFlag.FP16)
    if int8_mode:
        config.set_flag(trt.BuilderFlag.INT8)
        # Requires calibration dataset
        config.int8_calibrator = MyCalibrator()

    # Build and optimize
    engine = builder.build_engine(network, config)
    return engine

# Performance comparison on ResNet-50 inference (batch=32)
# PyTorch eager: 42ms
# PyTorch JIT: 35ms
# TensorRT FP32: 18ms
# TensorRT FP16: 8ms
# TensorRT INT8: 5ms

Graph Optimization Patterns

# Before optimization: 5 separate operations
def unoptimized_layer_norm(x, weight, bias, eps=1e-5):
    mean = x.mean(dim=-1, keepdim=True)
    var = x.var(dim=-1, keepdim=True)
    normalized = (x - mean) / torch.sqrt(var + eps)
    scaled = normalized * weight
    output = scaled + bias
    return output

# After compiler fusion: 1 fused kernel
# Automatically performed by XLA/TensorRT
# 3-4x faster, reduced memory bandwidth

Memory Management for Large Models

KV-Cache Management for LLMs

# Standard attention caches key/value tensors
# Memory = batch_size × seq_len × num_layers × hidden_size × 2

# Problem: 70B model, batch=32, seq=2048
# KV cache = 32 × 2048 × 80 × 8192 × 2 × 2 bytes = 160GB!

# Solution 1: Paged Attention (vLLM)
class PagedAttention:
    def __init__(self, block_size=16, num_blocks=1000):
        self.block_size = block_size
        # Pre-allocate block pool
        self.kv_blocks = torch.zeros(num_blocks, block_size, hidden_size)
        self.block_tables = {}  # Maps sequence_id -> [block_ids]

    def append_tokens(self, seq_id, new_kv):
        # Allocate blocks on demand
        blocks = self.block_tables.get(seq_id, [])
        # Share blocks across sequences (prefix caching)
        # Achieve 3-5x memory reduction

# Solution 2: Multi-Query Attention (MQA)
# Share K/V across attention heads
# Memory reduction: 8x for 32-head models

# Solution 3: Grouped-Query Attention (GQA)
# Hybrid: group heads, share K/V within groups
# Used in Llama 2: 8 K/V heads for 32 query heads = 4x reduction

CPU-GPU Transfer Optimization

# Slow: synchronous transfers
for batch in dataloader:
    batch_gpu = batch.cuda()  # Transfer
    output = model(batch_gpu)  # Compute
    # GPU idle during transfer

# Fast: asynchronous transfers with pinned memory
dataloader = DataLoader(
    dataset,
    batch_size=32,
    pin_memory=True,  # Use pinned memory
    num_workers=4
)

# Create CUDA streams
stream = torch.cuda.Stream()

for batch in dataloader:
    with torch.cuda.stream(stream):
        batch_gpu = batch.cuda(non_blocking=True)  # Async transfer

    torch.cuda.current_stream().wait_stream(stream)
    output = model(batch_gpu)

# Result: Overlap transfer with computation, 20-30% speedup

On-Device AI

On-device inference presents unique constraints: model size, power consumption, thermal management, and intermittent connectivity.

Device Constraints by Platform

On-Device Architecture Pattern

graph TB
    A[User Input] --> B{Network Available?}
    B -->|Yes| C[Cloud Model]
    B -->|No| D[On-Device Model]

    C --> E{Confidence > Threshold?}
    E -->|Yes| F[Return Result]
    E -->|No| G[Hybrid: On-Device + Cloud]

    D --> H{Result Quality Check}
    H -->|Good| F
    H -->|Uncertain| I[Queue for Cloud Sync]

    subgraph "On-Device Stack"
        D --> J[Quantized Model<br/>INT8/INT4]
        J --> K[NPU Inference]
        K --> L[Local Cache]
    end

    subgraph "Cloud Stack"
        C --> M[Full Precision Model]
        M --> N[GPU Inference]
    end

    style D fill:#FFB6C1
    style C fill:#90EE90

Model Compression Techniques

Technique Comparison

Knowledge Distillation Example

import torch
import torch.nn as nn
import torch.nn.functional as F

class DistillationTrainer:
    def __init__(self, teacher_model, student_model, temperature=3.0, alpha=0.7):
        self.teacher = teacher_model.eval()
        self.student = student_model
        self.temperature = temperature
        self.alpha = alpha  # Weight for distillation loss

    def distillation_loss(self, student_logits, teacher_logits, labels):
        # Soft targets from teacher
        soft_targets = F.softmax(teacher_logits / self.temperature, dim=1)
        soft_student = F.log_softmax(student_logits / self.temperature, dim=1)
        distillation_loss = F.kl_div(soft_student, soft_targets, reduction='batchmean')
        distillation_loss *= self.temperature ** 2

        # Hard targets (ground truth)
        student_loss = F.cross_entropy(student_logits, labels)

        # Combined loss
        return self.alpha * distillation_loss + (1 - self.alpha) * student_loss

    def train_step(self, inputs, labels):
        with torch.no_grad():
            teacher_logits = self.teacher(inputs)

        student_logits = self.student(inputs)
        loss = self.distillation_loss(student_logits, teacher_logits, labels)
        return loss

# Example: BERT-base (110M) → DistilBERT (66M)
# Size: 40% reduction
# Speed: 60% faster
# Accuracy: 97% of original on GLUE

LoRA for On-Device Personalization

from peft import LoraConfig, get_peft_model

# Base model stays frozen and shared
base_model = AutoModelForCausalLM.from_pretrained("meta-llama/Llama-2-7b-hf")

# LoRA adds small trainable adapters
lora_config = LoraConfig(
    r=8,  # Low rank
    lora_alpha=32,
    target_modules=["q_proj", "v_proj"],
    lora_dropout=0.05,
    bias="none",
)

model = get_peft_model(base_model, lora_config)
model.print_trainable_parameters()
# Output: trainable params: 4.2M || all params: 6,742M || trainable: 0.06%

# On-device: Ship base model + personalized adapters
# Base model: 7GB (shared across users)
# Per-user adapter: 8MB (unique)
# Total: 7GB + 8MB per user vs. 7GB per user

Privacy-Preserving On-Device AI

# Pattern: On-device feature extraction + Cloud inference

class PrivacyPreservingPipeline:
    def __init__(self):
        # On-device: extract privacy-safe features
        self.feature_extractor = MobileNetV3(pretrained=True)
        # Cloud: task-specific head
        self.cloud_classifier = None

    def process_on_device(self, image):
        # Extract embeddings locally
        with torch.no_grad():
            features = self.feature_extractor(image)
        # Features are abstract, don't reveal image content
        return features

    def process_cloud(self, features):
        # Send only features, not raw image
        response = requests.post(
            'https://api.example.com/classify',
            json={'features': features.tolist()}
        )
        return response.json()

# Privacy benefits:
# - Raw images never leave device
# - Network bandwidth: 2MB image → 2KB features (1000x reduction)
# - Comply with GDPR, CCPA data minimization

Offline-First Design

class OfflineFirstModel:
    def __init__(self):
        self.online_model_url = "https://api.example.com/model"
        self.offline_model_path = "/data/local/model.tflite"
        self.model_version = "v2.3"

    def predict(self, input_data):
        # 1. Try offline model first
        try:
            result = self.offline_inference(input_data)
            confidence = result['confidence']

            # 2. Use online model if confidence is low
            if confidence < 0.7 and self.is_online():
                online_result = self.online_inference(input_data)
                if online_result['confidence'] > confidence:
                    result = online_result
                    # Update offline model in background
                    self.check_model_update()

            return result
        except Exception as e:
            # 3. Fallback to rule-based system
            return self.rule_based_fallback(input_data)

    def check_model_update(self):
        # Download new model in background when on WiFi
        if self.is_wifi() and not self.is_battery_low():
            new_version = self.get_latest_version()
            if new_version > self.model_version:
                self.download_model(new_version)

# User experience:
# - Always functional, even offline
# - Seamless quality upgrades when online
# - Respects user's data plan and battery

Evaluation

Comprehensive Benchmarking Framework

import time
import psutil
import numpy as np
from dataclasses import dataclass
from typing import List

@dataclass
class BenchmarkResult:
    throughput_rps: float
    latency_p50_ms: float
    latency_p95_ms: float
    latency_p99_ms: float
    memory_peak_mb: float
    gpu_utilization_pct: float
    cost_per_1k_requests: float
    accuracy_metric: float

class ModelBenchmark:
    def __init__(self, model, test_dataset, warmup_steps=100):
        self.model = model
        self.test_dataset = test_dataset
        self.warmup_steps = warmup_steps

    def benchmark(self, batch_size=1, num_requests=1000) -> BenchmarkResult:
        # Warmup
        for _ in range(self.warmup_steps):
            self.model(self.test_dataset[0])

        latencies = []
        memory_usage = []

        start_time = time.time()

        for i in range(num_requests):
            # Measure latency
            request_start = time.time()
            output = self.model(self.test_dataset[i % len(self.test_dataset)])
            latencies.append((time.time() - request_start) * 1000)

            # Measure memory
            if i % 10 == 0:
                memory_usage.append(psutil.Process().memory_info().rss / 1024 / 1024)

        total_time = time.time() - start_time

        return BenchmarkResult(
            throughput_rps=num_requests / total_time,
            latency_p50_ms=np.percentile(latencies, 50),
            latency_p95_ms=np.percentile(latencies, 95),
            latency_p99_ms=np.percentile(latencies, 99),
            memory_peak_mb=max(memory_usage),
            gpu_utilization_pct=self.get_gpu_utilization(),
            cost_per_1k_requests=self.calculate_cost(total_time, num_requests),
            accuracy_metric=self.evaluate_accuracy()
        )

# Example comparison
configs = [
    ("FP32 CPU", model_fp32, 'cpu'),
    ("FP16 GPU", model_fp16, 'cuda'),
    ("INT8 GPU", model_int8, 'cuda'),
    ("TensorRT INT8", model_trt, 'cuda'),
]

results = []
for name, model, device in configs:
    benchmark = ModelBenchmark(model.to(device), test_dataset)
    result = benchmark.benchmark(batch_size=32)
    results.append((name, result))
    print(f"{name}: {result.latency_p95_ms:.1f}ms p95, ${result.cost_per_1k_requests:.2f}/1k")

Accuracy vs Performance Tradeoff Matrix

Decision Framework:

• Accuracy-critical (legal, medical): FP16 or INT8 only
• User-facing low-latency: INT4 with continuous monitoring
• Batch processing: Maximize throughput with INT4
• Cost-sensitive: Smallest model that meets accuracy threshold

Case Study: Mobile Document Scanner

Problem Statement

A fintech company needed to extract data from financial documents (bank statements, invoices, receipts) in their mobile app. Requirements:

• Sub-300ms end-to-end latency
• Work offline (poor connectivity in rural areas)
• Privacy: documents must not leave device
• Accuracy: >95% field extraction accuracy

Architecture

graph TB
    A[Document Photo] --> B[On-Device Pipeline]

    subgraph "On-Device Processing"
        B --> C[Layout Detection<br/>MobileNetV3 + FPN<br/>INT8, 15MB]
        C --> D[Text Detection<br/>CRAFT detector<br/>INT8, 8MB]
        D --> E[OCR<br/>TrOCR-small quantized<br/>INT8, 35MB]
        E --> F[Field Extraction<br/>DistilBERT + rules<br/>INT8, 25MB]
    end

    F --> G{Confidence Check}
    G -->|High| H[Return Results]
    G -->|Low| I{Online?}
    I -->|Yes| J[Cloud VLM Verification<br/>GPT-4V]
    I -->|No| K[Flag for Review]

    J --> H
    K --> L[Sync When Online]

    style B fill:#FFB6C1
    style J fill:#90EE90

Implementation Details

# On-device pipeline
class DocumentScanner:
    def __init__(self):
        # Total model size: ~85MB (fits comfortably in memory)
        self.layout_detector = load_quantized_model('layout_det_int8.tflite')
        self.text_detector = load_quantized_model('craft_int8.tflite')
        self.ocr_model = load_quantized_model('trocr_small_int8.tflite')
        self.field_extractor = load_quantized_model('distilbert_int8.tflite')

    def process_document(self, image):
        # Stage 1: Layout detection (40ms)
        layout_boxes = self.layout_detector(image)

        # Stage 2: Text detection (60ms)
        text_regions = []
        for box in layout_boxes:
            region_img = crop_image(image, box)
            text_boxes = self.text_detector(region_img)
            text_regions.extend(text_boxes)

        # Stage 3: OCR (120ms)
        texts = []
        for region in text_regions:
            region_img = crop_image(image, region)
            text = self.ocr_model(region_img)
            texts.append((region, text))

        # Stage 4: Field extraction (50ms)
        fields = self.field_extractor(texts, layout_boxes)

        # Total: ~270ms
        return {
            'fields': fields,
            'confidence': self.calculate_confidence(fields),
            'raw_text': texts
        }

    def calculate_confidence(self, fields):
        # Confidence based on OCR scores and field validation
        scores = [f['ocr_confidence'] for f in fields]
        validation = [self.validate_field(f) for f in fields]
        return np.mean(scores) * np.mean(validation)

# Cloud fallback (only for low-confidence cases)
class CloudVerification:
    def verify_fields(self, image, on_device_result):
        # Use GPT-4V for verification
        response = openai.ChatCompletion.create(
            model="gpt-4-vision-preview",
            messages=[{
                "role": "user",
                "content": [
                    {"type": "image_url", "image_url": {"url": image}},
                    {"type": "text", "text": f"Verify these extracted fields: {on_device_result['fields']}"}
                ]
            }]
        )
        return response.choices[0].message.content

Performance Results

Optimization Techniques Used

1. Model Distillation: Trained layout detector from Detectron2 → MobileNetV3 (500MB → 15MB)
2. Quantization: INT8 post-training quantization across all models
3. Pipeline Optimization: Overlapped stages using async processing
4. Smart Cropping: Only process detected regions, not full image
5. Caching: Cache layout results for multi-page documents

Business Impact

• User Experience: Instant results without loading spinners
• Privacy: Meets GDPR/CCPA requirements, key selling point
• Reliability: Works in areas with poor connectivity (35% of users)
• Cost: 12x cheaper than cloud-only solution
• Scale: Handles 2M documents/day without additional cloud costs

Implementation Checklist

Phase 1: Hardware Selection & Baseline (Week 1-2)

• Define SLOs and Constraints

  • Target latency (p50, p95, p99)
  • Throughput requirements (requests/second)
  • Cost budget per request
  • Accuracy thresholds
  • Privacy/locality requirements

• Hardware Selection

  • Cloud: GPU (A100, H100), TPU (v4, v5), or Inferentia
  • Edge: Device profiles (phone, IoT, embedded)
  • Hybrid: Define on-device vs cloud split

• Baseline Measurements

  • CPU baseline (throughput, latency, cost)
  • GPU/TPU baseline with naive deployment
  • Profile memory usage and bottlenecks
  • Measure end-to-end request flow

Phase 2: Optimization & Compilation (Week 3-4)

• Quantization

  • Post-training dynamic quantization
  • Static quantization with calibration dataset
  • Quantization-aware training if needed
  • A/B test accuracy on representative data

• Compiler Optimization

  • TensorRT/XLA compilation
  • Kernel fusion analysis
  • Mixed precision (FP16/BF16)
  • Batch size optimization

• Memory Optimization

  • KV-cache strategy for LLMs
  • Paged attention implementation
  • CPU-GPU transfer optimization
  • Memory profiling and leak detection

Phase 3: On-Device Deployment (Week 5-6, if applicable)

• Model Compression

  • Pruning (structured/unstructured)
  • Knowledge distillation
  • LoRA adapters for personalization
  • Neural architecture search

• Device Integration

  • Target NPU/DSP if available
  • Offline-first UX design
  • Model update mechanism
  • Battery and thermal monitoring

• Privacy Design

  • On-device feature extraction
  • Minimize data uploads
  • Federated learning if needed
  • Privacy policy updates

Phase 4: Evaluation & Monitoring (Week 7-8)

• Comprehensive Benchmarking

  • Latency distribution (p50, p95, p99)
  • Throughput under load
  • Memory usage (peak, steady-state)
  • Cost per request calculation
  • Accuracy comparison matrix

• Reliability Testing

  • OOM scenarios and recovery
  • Thermal throttling behavior
  • Network failure handling
  • Graceful degradation

• Production Monitoring

  • Latency and throughput dashboards
  • Error rates and OOM frequency
  • Cost tracking and alerts
  • Accuracy monitoring (when ground truth available)

Phase 5: Continuous Improvement (Ongoing)

• Performance Iteration

  • Profile new bottlenecks
  • Update compilers and drivers
  • New quantization techniques
  • Hardware upgrades evaluation

• Model Updates

  • A/B test new model versions
  • Gradual rollout strategy
  • Rollback procedures
  • Version compatibility

• Cost Optimization

  • Right-size instance types
  • Spot/preemptible instances
  • Auto-scaling policies
  • Multi-cloud cost comparison

Common Pitfalls & Solutions

Best Practices Summary

1. Measure First: Always baseline before optimizing
2. Incremental Optimization: Apply one technique at a time, measure impact
3. Target-Specific: Different deployments need different optimizations
4. Monitor Accuracy: Quantization and compression can degrade quality
5. Design for Failure: Handle OOM, thermal limits, network issues gracefully
6. Privacy by Design: Minimize data movement, prefer local processing
7. Cost-Aware: Track cost per request, not just performance
8. User-Centric: Optimize for perceived latency and offline reliability

Further Reading

• Hardware: NVIDIA TensorRT documentation, Google TPU performance guide
• Quantization: "LLM.int8()" paper, GPTQ, AWQ papers
• On-Device: TensorFlow Lite guide, Core ML documentation
• Compilers: TVM tutorials, XLA optimization guide
• Memory: vLLM paper on paged attention, FlashAttention paper