AI Hardware and Inference Optimization Cheat Sheet

Updated 2026-05-18

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Running AI models efficiently requires understanding hardware architecture, memory systems, and software optimization techniques that bridge the gap between training and deployment. Modern AI hardware has evolved from general-purpose GPUs to specialized accelerators with dedicated tensor cores, high-bandwidth memory, and custom instruction sets optimized for matrix operations. Whether deploying in the cloud or at the edge, choosing the right combination of hardware capabilities, quantization formats, and inference frameworks determines latency, throughput, cost, and energy efficiency—often with orders of magnitude differences between optimal and naive configurations.

What This Cheat Sheet Covers

This topic spans 14 focused tables and 93 indexed concepts. Below is a complete table-by-table outline of this topic, spanning foundational concepts through advanced details.

Table 1: GPU Architecture Components for AITable 2: AI Accelerator Chip LandscapeTable 3: Edge and Mobile AI ProcessorsTable 4: Quantization and Numeric Precision FormatsTable 5: Parallelism Strategies for Distributed Training and InferenceTable 6: Inference Optimization TechniquesTable 7: Memory Technologies for AI HardwareTable 8: Inference Serving Frameworks and RuntimesTable 9: Model Compression TechniquesTable 10: GPU Programming Platforms and ToolsTable 11: Inference Performance MetricsTable 12: Attention Optimization MethodsTable 13: Benchmarking and Evaluation StandardsTable 14: Hardware Architectures by Generation

Table 1: GPU Architecture Components for AI

GPU architecture for machine learning centers on parallel processing units optimized for matrix operations. CUDA cores handle general parallel tasks, while tensor cores accelerate AI-specific workloads with specialized matrix multiply-accumulate operations. Memory hierarchy—from on-chip registers to HBM—determines how quickly data flows to compute units, with bandwidth often becoming the bottleneck in inference-heavy workloads.

Component	Example	Description
CUDA Cores	`10,752 cores in RTX 4090`	General-purpose parallel processing units that execute floating-point and integer operations; handle tasks like data preprocessing and non-matrix computations
Tensor Cores	`512 Tensor Cores in H100`	Specialized hardware units that accelerate matrix multiply-accumulate (MMA) operations for AI workloads; deliver up to 3× higher throughput than CUDA cores for deep learning inference
Streaming Multiprocessor (SM)	`132 SMs in H100 with 4 warp schedulers each`	Cluster of cores plus shared memory that executes warps (groups of 32 threads); each SM contains CUDA cores, tensor cores, registers, and L1 cache for context-switch-free multitasking
Warp Scheduling	`4 warp schedulers per SM`	Hardware scheduler that selects which group of 32 threads to execute each clock cycle; hides memory latency by switching between warps with zero overhead using massive register files

Table 1: GPU Architecture Components for AI

Component	Example	Description
CUDA Cores	`10,752 cores in RTX 4090`	General-purpose parallel processing units that execute floating-point and integer operations; handle tasks like data preprocessing and non-matrix computations
Tensor Cores	`512 Tensor Cores in H100`	Specialized hardware units that accelerate matrix multiply-accumulate (MMA) operations for AI workloads; deliver up to 3× higher throughput than CUDA cores for deep learning inference
Streaming Multiprocessor (SM)	`132 SMs in H100 with 4 warp schedulers each`	Cluster of cores plus shared memory that executes warps (groups of 32 threads); each SM contains CUDA cores, tensor cores, registers, and L1 cache for context-switch-free multitasking
Warp Scheduling	`4 warp schedulers per SM`	Hardware scheduler that selects which group of 32 threads to execute each clock cycle; hides memory latency by switching between warps with zero overhead using massive register files