A GPU deep-dive for AI engineers: token flow, memory math, quantization and multi-GPU sharding
kmeanskaran · x · 2026-11-13
A long-form tutorial from an inference engineering perspective, arguing that 2027's edge won't be RAG or agents but lower-level execution skills.
Covered topics:
- GPU components and how a token flows through hardware
- Compute vs memory as the two limits; what FLOPS means in practice
- Training vs inference hardware needs
- What CUDA kernels are
- Calculating GPU memory from model size
- Quantization to fit bigger models
- Multi-GPU setups and splitting large models
The author's thesis: as agent skills commoditize, inference, GPU and orchestration expertise keep you relevant.
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