Layer-Streaming Telemetry Harness
Benchmark massive MoE LLMs under strict 0GB VRAM limits
Layer-Streaming Telemetry Harness launched on June 3, 2026 with 2 votes.
What it does
An open-source MIT diagnostic tool to track peak system RAM, active VRAM allocation, and data-transfer layer execution when offloading extreme 284B parameter models onto commodity hardware footprints. Built for hardware-agnostic architecture audits.
Contribute to Aubyte-Admin/layer-streaming-telemetry-benchmark development by creating an account on GitHub.
An empirical telemetry benchmarking tool designed to track VRAM, system RAM, and token throughput during sequential layer-streaming inference and training runs. This repository contains the telemetry logging harness, baseline loader, and training memory profiler using Qwen-0.5B to verify code execution, tokenizer mapping, and memory diagnostics. Benchmarks conducted on a CPU container to verify edge-AI limits. Used to verify tokenizer correctness and baseline memory cache pipelines. Profiles training step memory scaling (forward + backward + AdamW optimization passes). Note: Peak System RAM RSS represents process high-water mark memory. When executed with GPU accelerators enabled, active…from github.com
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