Memory Bandwidth and Capacity: The Hidden Limits of AI Inference
Why model weights, KV cache, memory hierarchy and data movement can constrain accelerator performance.
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Special Coverage: Independent coverage of Research and practical analysis of AI inference acceleration technologies, including GPUs, NPUs, ASICs, FPGAs, memory bandwidth, interconnects, quantization, batching, software kernels, serving systems, performance-per-watt and total cost of production inference.
Published articles
Inference Performance per Watt: Why Efficiency Is a First-Class Metric
How power, cooling, utilization and useful tokens or tasks per joule affect the economics of production inference.
Read article →GPU vs NPU vs ASIC: Choosing an Inference Accelerator
Comparing programmable GPUs, neural processing units and application-specific silicon across workload and deployment needs.
Read article →What Is AI Inference Acceleration? Hardware and Software Explained
A guide to accelerating model execution and the role of processors, memory, interconnects and serving software.
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Quantization for Faster Inference: INT8, FP8 and Low-Precision Formats
The engineering trade-offs between lower precision, throughput, memory footprint and model quality.
Inference Batching and Scheduling: Improving Accelerator Utilization
How dynamic batching, request queues, concurrency and latency targets shape serving efficiency.
AI Inference Kernels: Fusing Operations and Reducing Data Movement
The role of optimized kernels, operator fusion and hardware-aware software in extracting performance.
Prefill vs Decode: Two Different Bottlenecks in LLM Inference
Understanding compute-heavy prompt processing and memory-sensitive token generation in large language models.
Disaggregated Inference: Separating Compute, Memory and Serving Roles
Architectural options for separating prefill and decode, pooling resources and managing network overhead.
Inference Benchmarking: Throughput, Tail Latency and Real Workloads
How to compare accelerators using representative models, realistic request patterns and transparent metrics.
Compiler and Runtime Optimization for AI Accelerators
How graph compilers, runtimes, libraries and model formats influence portability and accelerator efficiency.
Cooling and Power Delivery for Dense Inference Systems
Facility and rack-level considerations that affect accelerator density, uptime and total operating cost.
Edge Inference Acceleration: Smaller Systems, Tighter Constraints
Designing efficient accelerator platforms for devices, gateways, telecom edge and industrial environments.
Open Software and Portability in a Multi-Accelerator Market
Reducing lock-in through portable model formats, abstraction layers, standards and validated software stacks.
Building an Inference Accelerator Business Case
A framework for evaluating acquisition cost, utilization, energy, software effort, service quality and lifecycle risk.
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