MegaTrain enables full-precision training of 100B+ parameter LLMs on a single H200 GPU by storing parameters and optimizer states in 1.5TB host memory and streaming layers as transient compute tasks. The arXiv paper details a pipelined double-buffered engine overlapping prefetch, gradient computation, and offload across CUDA streams plus stateless layer templates that eliminate persistent autograd graphs. This delivers 1.84× the throughput of DeepSpeed ZeRO-3 for 14B models and supports 7B models at 512k context on GH200 hardware. Original coverage correctly notes the bandwidth optimizations yet understates how these techniques fundamentally invert the GPU-centric paradigm that has concentrated training capability inside hyperscaler clusters. Rajbhandari et al. (2019) introduced ZeRO-3 offloading in "ZeRO: Memory Optimizations Toward Training Trillion Parameter Models" (https://arxiv.org/abs/1910.02054), yet retained notable performance penalties from frequent CPU-GPU synchronization; MegaTrain's double buffering and template binding measurably close that gap while preserving FP32 precision instead of moving to quantization. Dettmers et al. (2023) demonstrated in "QLoRA: Efficient Finetuning of Quantized LLMs" (https://arxiv.org/abs/2305.14314) that consumer-grade hardware could handle adaptation of smaller models; MegaTrain synthesizes these threads to enable full pre-training at frontier scale, exposing that prior literature focused on inference or fine-tuning rather than end-to-end training democratization. The pattern across these works reveals an accelerating shift from device memory as the limiting resource to host memory bandwidth as the new bottleneck, one MegaTrain mitigates without sacrificing numerical stability. If sustained, the approach lowers the capital barrier from thousands of H100 GPUs—typical for Llama-3-class training—to a single enterprise GPU plus ample DRAM, enabling university labs and independent collectives to iterate on 100B-scale models. This trajectory directly challenges the compute monopolies currently held by entities controlling multi-GW data-center footprints, potentially redistributing the locus of frontier discovery beyond a handful of well-resourced organizations.