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// 1h agoTUTORIAL
GPU Memory Hierarchy Handbook Exposes Inference Bottlenecks
Tech with Mak’s handbook uses a 7B model on an H100 to show why fitting weights in HBM does not predict decode speed: streaming 14 GB at 3.35 TB/s implies only a theoretical ~239 tokens per second before overhead. It explains GPU memory hierarchy, locality, coalescing, tiling, GEMM, attention, and prefill versus decode.
// ANALYSIS
The key lesson is that GPU memory capacity is only the starting point; inference performance depends on data movement, reuse, latency hiding, and kernel efficiency.
- –Model size provides a rough memory requirement, not a throughput forecast
- –Decode is often constrained by weight and KV-cache traffic rather than raw tensor-core compute
- –Registers, shared memory, L1/L2 caches, and HBM have radically different performance characteristics
- –Quantization reduces both capacity pressure and the amount of data that must move per token
- –Kernel design techniques such as tiling and FlashAttention improve performance by keeping reusable data closer to compute
// TAGS
gpuinferencellmtraining-infragpu-memory-hierarchy-handbook
DISCOVERED
1h ago
2026-09-14
PUBLISHED
1h ago
2026-09-14
RELEVANCE
8/ 10
AUTHOR
techNmak