Lizard AI Runtime

The Windows AI runtime built for real inference

5 followers

The Windows AI runtime built for real inference

5 followers

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LLMs

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AI Agents

Most AI apps simply load a model. Lizard plans inference before the first token—optimizing memory, scheduling execution, and running GGUF models locally for faster, more stable AI on Windows.

Free

Launch tags:Windows•Developer Tools•Artificial Intelligence

Launch Team / Built With

Atlassian LoomTalk it through in Loom. Ship with AI

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Maker

📌

Here's a Product Hunt first comment that's written in a natural, engineering-focused style rather than sounding like marketing. --- 👋 Hi Product Hunt! I'm excited to introduce **Lizard AI Runtime**, our native Windows runtime built for local AI inference. While building local AI systems, we kept running into the same problem: most applications focus on loading models, but very little attention is paid to what happens between the prompt and every generated token. That's where performance is often won—or lost. Lizard was built around the runtime itself. Instead of treating inference as a black box, it plans execution based on your actual hardware, separates prefill from decode, optimizes available GPU memory, and keeps everything running locally. Some of the things we're especially proud of: 🚀 Native Windows AI runtime 🦎 Intelligent inference planning ⚡ Phase-aware execution (Prefill + Decode) 🧠 Smarter GPU memory utilization 🔒 100% local inference — no cloud dependency 📊 Built-in diagnostics and transparent runtime behavior This is just the beginning. We're also building technologies like **Caterpillar**, our execution engine for reducing orchestration overhead through pre-compiled compute graphs, as part of our long-term vision for faster, more efficient local AI. We'd genuinely love your feedback: * What local AI tools are you using today? * What's your biggest bottleneck—memory, latency, setup, or something else? * What would you like to see in a next-generation AI runtime? Thanks for checking out **Lizard**—we're looking forward to hearing what you think! 🦎

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3mo ago

Loaded a 7B GGUF model on my laptop and was honestly surprised how steady the tokens came through, no weird stuttering mid-response. The pre-planning angle actually shows in practice.

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3mo ago

Loaded a 7B GGUF locally and was actually surprised it didn't choke my laptop, scheduling seems smart. The pre-token planning is a real difference if you care about stable inference.

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3mo ago

Forum Threads

p/lizard-ai-runtime

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3mo ago

What Bottlenecks Your AI?

While building Lizard, we found that many performance bottlenecks weren't inside the model kernels-they came from memory planning, scheduling, CPU--GPU synchronization, and execution orchestration.

If you build or run local LLMs:

What's your biggest bottleneck today?
VRAM?
Token latency?
Model loading?
Context length?
Something else?

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