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I've been building ZSE (Z Server Engine) for the past few weeks — an open-source LLM inference engine focused on two things nobody has fully solved together: memory efficiency and fast cold starts.

The problem I was trying to solve: Running a 32B model normally requires ~64 GB VRAM. Most developers don't have that. And even when quantization helps with memory, cold starts with bitsandbytes NF4 take 2+ minutes on first load and 45–120 seconds on warm restarts — which kills serverless and autoscaling use cases.

What ZSE does differently:

Fits 32B in 19.3 GB VRAM (70% reduction vs FP16) — runs on a single A100-40GB

Fits 7B in 5.2 GB VRAM (63% reduction) — runs on consumer GPUs

Native .zse pre-quantized format with memory-mapped weights: 3.9s cold start for 7B, 21.4s for 32B — vs 45s and 120s with bitsandbytes, ~30s for vLLM

All benchmarks verified on Modal A100-80GB (Feb 2026)

It ships with:

OpenAI-compatible API server (drop-in replacement)

Interactive CLI (zse serve, zse chat, zse convert, zse hardware)

Web dashboard with real-time GPU monitoring

Continuous batching (3.45× throughput)

GGUF support via llama.cpp

CPU fallback — works without a GPU

Rate limiting, audit logging, API key auth

Install:

----- pip install zllm-zse zse serve Qwen/Qwen2.5-7B-Instruct For fast cold starts (one-time conversion):

----- zse convert Qwen/Qwen2.5-Coder-7B-Instruct -o qwen-7b.zse zse serve qwen-7b.zse # 3.9s every time

The cold start improvement comes from the .zse format storing pre-quantized weights as memory-mapped safetensors — no quantization step at load time, no weight conversion, just mmap + GPU transfer. On NVMe SSDs this gets under 4 seconds for 7B. On spinning HDDs it'll be slower.

All code is real — no mock implementations. Built at Zyora Labs. Apache 2.0.

Happy to answer questions about the quantization approach, the .zse format design, or the memory efficiency techniques.

My partner, Nick Hodges, and I, David Millington, have been on the Internet for a very long time -- since the Usenet days. We’ve seen it all, and have long been frustrated by bad comments, horrible people, and discouraging discussions. We've also been around places where the discussion is wonderful and productive. How to get more of the latter and less of the former?

Current moderation tools just seem to focus on deletion and banning. Wouldn’t it be helpful to encourage productive discussion and teach people how to discuss and argue (in the debate sense) better?

A year ago we started building Respectify to help foster healthy communication. Instead of just deleting bad-faith comments, we suggest better, good-faith ways to say what folks are trying to say. We help people avoid: * Logical fallacies (false dichotomy, strawmen, etc.) * Tone issues (how others will read the comment) * Relevance to the actual page/post topic * Low-effort posts * Dog whistles and coded language

The commenter gets an explanation of what's wrong and a chance to edit and resubmit. It's moderation + education in one step. We want, too, to automate the entire process so the site owner can focus on content and not worry about moderation at all. And over time, comment by comment, quietly coach better thinking.

Our main website has an interactive demo: https://respectify.ai. As the demo shows, the system is completely tunable and adjustable, from "most anything goes" to "You need to be college debate level to get by me".

We hope the result is better discussions and a better Internet. Not too much to ask, eh?

We love the kind of feedback this group is famous for and hope you will supply some!