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AI · alternatives · 2026

24 alternatives to Shimmy – 5MB privacy-first, local alternative to Ollama (680MB)

⚡ Pure-Rust WebGPU inference engine — OpenAI-API compatible, GGUF native, runs on any GPU. No Python. No llama.cpp. Single binary. - Michael-A-Kuykendall/shimmy

Shimmy is a lightweight AI tool that runs locally on your computer, using just 5MB compared to Ollama's 680MB. It prioritizes privacy by keeping all data on your device rather than sending it to external servers. The… Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.

  1. 1

    Massive local model speedup on Apple Silicon with MLX

    Apr 2026 · ollama.com · its alternatives →

  2. 2

    We wrote our inference engine on Rust, it is faster than llama cpp in all of the use cases. Your feedback is very welcomed. Written from scratch with idea that you can add support of any kernel and platform.

    2025 · github.com · its alternatives →

  3. 3

    I built LocalGPT over 4 nights as a Rust reimagining of the OpenClaw assistant pattern (markdown-based persistent memory, autonomous heartbeat tasks, skills system). It compiles to a single ~27MB binary — no Node.js, Docker, or Python required. Key features: - Persistent memory via markdown files (MEMORY, HEARTBEAT, SOUL markdown files) — compatible with OpenClaw's format - Full-text search (SQLite FTS5) + semantic search (local embeddings, no API key needed) - Autonomous heartbeat runner that checks tasks on a configurable interval - CLI + web interface + desktop GUI - Multi-provider:…

    Feb 2026 · github.com · its alternatives →

  4. 4

    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 · its alternatives →

  5. 5

    The stack: two agents on separate boxes. The public one (nullclaw) is a 678 KB Zig binary using ~1 MB RAM, connected to an Ergo IRC server. Visitors talk to it via a gamja web client embedded in my site. The private one (ironclaw) handles email and scheduling, reachable only over Tailscale via Google's A2A protocol. Tiered inference: Haiku 4.5 for conversation (sub-second, cheap), Sonnet 4.6 for tool use (only when needed). Hard cap at $2/day. A2A passthrough: the private-side agent borrows the gateway's own inference pipeline, so there's one API key and one billing relationship…

    Mar 2026 · georgelarson.me · its alternatives →

  6. 6

    The easiest way to chat with local AI

    2025 · ollama.com · its alternatives →

  7. 7

    Ollama for classical ML models. AOT compiler that turns XGBoost, LightGBM, scikit-learn, CatBoost & ONNX models into native C99 inference code. One command to load, one command to serve. 336x faster than Python inference. - kossisoroyce/timber

    Mar 2026 · github.com · its alternatives →

  8. 8

    Hey everyone, We have been building SecureAI Tools -- an open-source application layer for ChatGPT and ChatPDF-like AI tools. It works with locally running LLMs as well as with OpenAI-compatible APIs. For local LLMs, it supports Ollama which supports all the gguf/ggml models. Currently, it has two features: Chat-with-LLM, and Chat-with-PDFs. It is optimized for self-hosting use cases and comes with basic user management features. Here are some quick demos: * Chat with documents using OpenAI's GPT3.5 model: https://www.youtube.com/watch?v=Br2D3G9O47s * Chat with documents…

    2023 · github.com · its alternatives →

  9. 9

    AI models that run on an inference cloud optimized for speed

    May 2026 · generalcompute.com · its alternatives →

  10. 10

    Your AI, fully offline with Zero data collection & 100% free

    May 2026 · lumichats.com · its alternatives →

  11. 11

    connect nodes to build AI pipelines, powered by Hugging Face

    2024 · gradio.app · its alternatives →

  12. 12WM

    Try it out! https://glhf.chat/ Hey HN! We’ve been working for the past few months on a website to let you easily run (almost) any open-source LLM on autoscaling GPU clusters. It’s free for now while we figure out how to price it, but we expect to be cheaper than most GPU offerings since we can run the models multi-tenant. Unlike Together AI, Fireworks, etc, we’ll run any model that the open-source vLLM project supports: we don’t have a hardcoded list. If you want a specific model or finetune, you don’t have to ask us for it: you can just paste the Hugging Face link in and…

    2024 · glhf.chat · its alternatives →

  13. 13

    Check out this impressive project that enables running LLMs entirely in the browser using WebGPU. Key features: - Zero token costs, no cloud infrastructure required - Complete data privacy through local processing - Simple 3-line code integration - Built on MLC and Transformer.js The benchmarks show smaller models can effectively handle many common tasks. Currently the project roadmap includes: - No-code AI pipeline builder - Browser-based RAG for document chat - Analytics/logging - Model fine-tuning interface

    2025 · github.com · its alternatives →

  14. 14

    An ultra-fast, single-binary MCP server written in Rust as a lightweight alternative to Node.js/Python. - StamManif/mcp-stama

    Aug 2026 · github.com · its alternatives →

  15. 15

    cuTile Rust provides a safe, tile-based kernel programming DSL for the Rust programming language. It features a safe host-side API for passing tensors to asynchronously executed kernel functions. - NVlabs/cutile-rs

    Jun 2026 · github.com · its alternatives →

  16. 16

    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 · its alternatives →

  17. 17

    I’ve been building a small hardware box that runs local LLMs like Mistral, Qwen and Llama, and exposes an OpenAI compatible API on your local network. There’s no cloud, no login system and no telemetry. I built it because a lot of small firms want ChatGPTbstyle tools but can’t use cloud AI for privacy or compliance reasons, and most don’t want to deal with GPU servers, drivers, Docker or model configs. The aim is to make local AI feel as simple as plugging in a router. Right now the box boots into a very simple web UI where you choose a model and start using it. The API follows the OpenAI…

    Nov 2025 · axis-one-psi.vercel.app · its alternatives →

  18. 18

    A pipeline that translates Rust GPU code into formal Coq models, as a foundation for memory model proofs - neelsomani/vericuda

    2025 · github.com · its alternatives →

  19. 19SA

    Hi HN, I’ve been working on Shibuya, a next-generation Web Application Firewall (WAF) built from the ground up in Rust. I wanted to build a WAF that didn't just rely on legacy regex signatures but could understand intent and perform at line-rate using modern kernel features. What makes Shibuya different: Multi-Layer Pipeline: It integrates a high-performance proxy (built on Pingora) with rate limiting, bot detection, and threat intelligence. eBPF Kernel Filtering: For volumetric attacks, Shibuya can drop malicious packets at the kernel level using XDP before they consume userspace resources.…

    Feb 2026 · ghostklan.com · its alternatives →

  20. 20AT

    I recently built a small open-source tool to benchmark different LLM API endpoints — including OpenAI, Claude, and self-hosted models (like llama.cpp). It runs a configurable number of test requests and reports two key metrics: • First-token latency (ms): How long it takes for the first token to appear • Output speed (tokens/sec): Overall output fluency Demo: https://llmapitest.com/ Code: https://github.com/qjr87/llm-api-test The goal is to provide a simple, visual, and reproducible way to evaluate performance across different LLM providers, including…

    2025 · llmapitest.com · its alternatives →

  21. 21

    Hi HN! Since the launch of JigsawStack.com, we've been trying to dive deeper into fully managed AI APIs built and fine tuned for specific use cases. Audio/video transcription was one of the more basic things and we wanted the best open source model at this point it is OpenAI's whisper large v3 model based on the number of languages it supports and its accuracy. The thing is, the model is huge and requires tons of GPU power for it to run efficiently at scale. Even OpenAI doesn't provide an API for their best transcription model while only providing whisper v2 at a pretty high price. I…

    2024 · github.com · its alternatives →

  22. 22

    Hello all, I'm a software developer. Over the last few months more and more of my work has turned into using coding agents instead of typing the whole code myself. Usually a few claude sessions at once, sometimes codex, one per feature or per revealed bug. I ran them in a split terminal for a few weeks, and quickly spotted two main problems. The first is that I couldn't easily tell which agent was stuck waiting on me and which was still working, so I'd cycle through sessions and checking on them. The second one: agents sharing a single branch step on each other. Two of them could be editing…

    Jul 2026 · shikigami.dev · its alternatives →

  23. 23

    Hi everyone, wanted to share about gline-rs, an inference engine for GLiNER models written in Rust. This family of lightweight language models proved to be efficient at zero-shot Named Entity Recognition (NER) and other tasks such as Relation Extraction, while consuming less resources than large generative models (LLMs). This implementation has been written from the ground up in Rust, and supports both span- and token-oriented variants (for inference only). The goal is to provide a production-grade and user-friendly API in a modern and safe programming language, including a clean and…

    2025 · github.com · its alternatives →

  24. 24

    Hey HN, we’re the developers of OpenLake, an open source storage engine for offloading LLM KV caches from GPU memory into a shared tier of RAM and NVMe. We built OpenLake because KV caches are outgrowing GPU memory. A single 256K token conversation on Gemma 4 31B produces approximately 43GB of KV state, more than half the memory of an 80GB H100. The problem becomes even harder across a cluster: a prefix cached on one GPU host is unavailable when the next request lands on a different GPU, forcing the new GPU to repeat work the fleet has already completed. Once the KV cache is offloaded,…

    Jul 2026 · github.com · its alternatives →

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