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PCIe, NVLink and CXL: Why More Memory Is Not Always More GPU Memory

PCIe, NVLink and CXL are not interchangeable ways to add GPU memory. PCIe commonly carries device I/O; NVLink connects supported processors through product-specific paths; CXL adds memory and cache-coherency protocols over a PCIe-derived physical interface. More addressable capacity does not automatically become local HBM, or deliver HBM-like access time. The useful question is not simply “Which connection is fastest?” It is which processor is accessing which memory, over what route, with what software support? A capacity number without that route can be misleading when planning AI infrastructure. Quick check: before accepting “more AI memory,” label the requesting processor, the memory’s physical location, the link/protocol, and the supported access mechanism. If any field is missing, do not count the capacity as interchangeable GPU HBM. Documentation checked October 24, 2026, KST. This is a source-based architecture explanation with original synthetic arithmetic—not a GPU, PCIe...

Calculate LLM p50, p95 and SLO Goodput from Request Logs in Python

To calculate LLM latency percentiles and SLO Goodput from request logs, keep two views: compute p50 and p95 for successful responses, then count requests that meet every declared latency threshold over a stated time window. Keep errors and timeouts in the all-attempt attainment denominator—not in a made-up zero-latency bucket. A dashboard can show a reasonable median while half the requests fail your definition of a useful response. This small Python exercise makes that difference visible, without a GPU, API key or paid LLM call. Start here: if p95 differs between tools, first compare their percentile method and included requests. Save the two files below and run python analyze_logs.py requests.jsonl . These are synthetic logs ; the analyzer really ran, but no model generated these timings. Checked October 23, 2026. Executed on Windows with CPython 3.14.3, using only the standard library. Thresholds and the 60-second window are teaching choices, not recommended production targets....

Fewer NVIDIA GPUs, Less Memory? Flapping Airplanes’ $1B Challenge

Flapping Airplanes could make AI cheaper per useful result, but the public material reviewed here does not establish that NVIDIA GPUs or large amounts of memory are no longer needed. Higher hardware efficiency can reduce resources for a fixed workload without reducing total demand for AI infrastructure. The interesting challenge is not whether software can improve a chip. It is whether a better system changes the economics of buying the next rack. That deserves a harder test than either declaring the GPU boom over or assuming efficiency must create unlimited demand. Checked October 23, 2026, KST. Company statements, earlier academic measurements and original hypothetical calculations are labeled separately. This is an infrastructure analysis, not a stock recommendation or a GPU benchmark performed by this blog. Contents and working definitions The announcement : financing is not a benchmark. Megakernel : a GPU execution program, not a count of chips. Memory efficiency : capacity, t...