How GPUs Work: Compute vs Memory Bound Explained for LLM Engineers
trawasthi_ai · x · 2026-08-17
A technical explainer on GPU mechanics tailored for LLM engineers, demystifying compute-bound vs. memory-bound bottlenecks without the hardware manual.
- Core Mechanism: Weights reside in HBM and must travel to arithmetic units; this transfer is often the slowest step.
- Key Metric: Efficiency is measured by work per byte (arithmetic ops / bytes fetched from HBM).
- Hardware Specs: Uses the H100 as a case study to calculate break-even points based on peak compute and bandwidth.
- Implications: Provides the intuition needed to understand and optimize techniques like quantization, speculative decoding, and continuous batching.
Related event: LLM Inference Bottlenecks: GPU Architecture and Memory Bandwidth(2 posts)→
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