Two inference upgrades are ready for critique: an auto‑quantizer showing ~90% GPU/RAM reduction and a ‘hyperizer’ with ~3× speed and ~60% token savings in early runs; a Gradio demo verifies results, and the link will be shared by reply or DM to keep discussion high‑signal and non‑promotional.
Assembling a public repo/product of reproducible upgrades—quantization, KV‑cache policy, routing, and eval harness—and would value specific pain points and workloads to prioritize next.
Examples: VRAM ceilings on single‑GPU nodes; throughput vs p95 latency under batching; long‑context costs and compression; 24/7 stability issues; human‑relevant evals that would convince practitioners.
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