Deep dives, practical guides, and incident analyses from engineers who build Safeguard. No fluff, no vendor FUD — just what you need to ship secure software.
Training costs about 6 x params x tokens FLOPs over peak x MFU; decode speed is bounded by memory bandwidth. Every formula, worked numbers and a Python calculator.
How NVIDIA, AMD, Intel and Apple GPUs differ in compute units, SIMD width, matrix engines, memory, interconnect and software, with cited specs and rooflines.
A QPU is a qubit chip in a dilution refrigerator, driven by shaped microwave pulses and read out by resonators. Every layer explained, with worked numbers.
How a CPU runs code: fetch, decode, rename, out-of-order execution, retire, branch prediction, caches, TLBs, SIMD, SMT and Spectre, with worked numbers.
A GPU workload runs on a stack of driver, CUDA, cuDNN, NCCL and container toolkit versions. How each layer ships, breaks, gets patched and fits in an SBOM.
Tile-based GPUs bin geometry, then render each tile in on-chip memory. How Apple, Mali, Adreno and PowerVR do it, with bandwidth maths and Metal code.
One tiled matrix multiply in CUDA, HIP, SYCL, OpenCL, Metal, Vulkan compute and Triton, with a terminology map, portability evidence and when to pick each.
How NVIDIA GPUs evolved from the 2006 G80 to Hopper and Blackwell: what each generation changed, why it mattered, with specs cited to NVIDIA's own papers.
How AMD GPUs work: GCN compute units, RDNA wave32 and Infinity Cache, CDNA matrix cores and MI300 chiplets, Infinity Fabric, and porting CUDA to HIP.
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