Alternatives
Products that do what Lumabri – Run Moe Models on a P2P Swarm with Colibri does
Run huge MoE models from a swarm of peers, with the colibri engine. Pure C. - JustVugg/lumabri
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Hi HN, I built a specialized inference engine for running 4-bit Gemma 4 26B-A4B-IT on any M-series Mac using about 2 GB of RAM. It is called TurboFieldfare and is written in Swift and Metal. I have always adored on-device AI. It feels like magic that you can run a powerful NN on your Mac or iPhone. So I wanted to push the limits a bit and run a model whose weights don’t fit in memory. The model’s 4-bit quantized weights occupy roughly 14 GB, which makes running it with conventional inference tools almost impossible on an 8 GB or even 16 GB Mac once the OS, applications, and KV cache are…
Jul 2026 · github.com
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A while ago I started working on Colibrì to see if it was possible to run huge LLMs on a normal computer. The project grew far beyond what I expected, thanks in large part to the HackerNews community. That led me to a new question: What if we stopped thinking about one computer? This is the idea behind Lumabri. Instead of requiring a single machine to store and run an entire huge model, Lumabri treats a network of normal computers as a shared pool of resources. One machine might provide disk space, another compute, another a different part of the model. If a required block or expert isn’t…
27d ago · github.com
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Run Qwen3.8-Flash-Next (125B MoE, 104 GB at 4-bit) on Macs with a fraction of that RAM by streaming experts from SSD. MLX + Swift, Ollama-compatible API. - carloslfu/slotstream
5d ago · github.com
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Open-source unified interface for agent harnesses
21d ago · harnessrouter.ai
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- 9RA
Aug 2026 · github.com
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We built a model router that plugs into coding agents (e.g. Claude Code, Codex, Cursor, etc.) and intelligently sends requests to the best model to serve them. Here's a quick demo of running it locally: https://www.youtube.com/watch?v=isKhAyivtfM. At Weave, we write most of our code with AI, and it's been getting more expensive. This came to a head when Opus 4.7 was released and, thanks to its tokenizer changes, our costs shot up. We knew we didn't need Opus for everything but we didn't want to lose out on the intelligence for the cases where you really need it. So we decided…
Jun 2026 · github.com
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Aug 2026 · deepgrove.ai
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Working on Mac, Linux, and Windows now. I include a simple GUI to find new models and get things built and set up. It is working quite well across a few models for me. The GitHub README and DESIGN.md files go into detail of the how/why and it's working remarkably well so far. https://github.com/notactuallytreyanastasio/shoehorn
19d ago · notactuallytreyanastasio.github.io
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OrchestraML▲82From English prompt to deployed ML model with human approval
Jun 2026 · orchestra-ml.vercel.app
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An ultra-fast, single-binary MCP server written in Rust as a lightweight alternative to Node.js/Python. - StamManif/mcp-stama
23d ago · github.com
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The open-source AI workstation for coding, ops, and life
Apr 2026
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The model has 3B active parameters. We put the code, homepage, paper and model links here: - Code: https://github.com/bytedance/Lance - Homepage: https://lance-project.github.io/ - Paper: https://arxiv.org/abs/2605.18678 - Model: https://huggingface.co/bytedance-research/Lance p.s. Lance is a research project, not a polished product. The model was trained using fewer than 128 GPUs.
May 2026 · github.com
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runNburn is an Apache-2.0 Rust inference engine for quantized GGUF models that are too big for your fast memory. The core idea: weights stay file-backed (mmap), host residency stays under an explicit byte budget (--ram-budget), and GPU caches are sized from detected free/total VRAM — never from device-name presets. There is no conversion step, no sidecar cache files, no silent requantization. The GGUF on disk is the single source of truth. The result that made me want to post this: Tencent's Hy3 (295B total / 21B active sparse MoE, a single 97.8 GiB Q2_K GGUF) runs on my desktop…
Jul 2026 · github.com
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