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.
A one page brief on why open source needs fixing at the source, what Gold Open Source and Gold Registry do, and the goal we have set for January 2027.
Weights, KV cache, chips per replica, decode limits and cost per million tokens for 1B, 10B, 100B and 1T models on 13 AI chips, all worked in Python.
Levers that cut LLM cost per million tokens, with worked math: chip choice, batching, quantization, caching, MoE, distillation, spot and serving stacks.
The fastest LLM inference keeps batches small, splits weights across many chips, and uses speculative decoding. The physics, chips and measured results.
Every metric for today's AI chips side by side: dense and sparse throughput by precision, HBM, interconnect, power, rack scale, software, prices and MLPerf results.
Worked estimates for training 1B, 10B, 100B and 1T parameter LLMs on H100, B200, MI300X, TPU and Trainium2: FLOPs, chip-hours, cost, energy and memory.
When does buying AI accelerators beat renting? A worked TCO model with capex, power, PUE, cooling, staff, cloud prices and break-even utilization by chip.
Worked numbers for a 1 trillion parameter model, dense and mixture of experts: training memory, parallelism on NVL72, HGX and TPU pods, serving layouts, interconnect, megawatts and cost.
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.
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