Alternatives
Products that do what Kuzco does
Open-source Swift package to run LLMs locally on iOS & macOS
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hey hn, I built an open-source Perplexity clone that can run local LLMs and cloud LLMs. It's fully self-hostable through Docker and uses ollama to support local LLMs. The demo video in the repository shows me running it locally with llama3 on my M1 Macbook Pro. I'm open to any suggestions or feedback, thanks!
2024 · github.com
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2023 · github.com
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2023 · github.com
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LLMStack is a low-code platform that can be used to build LLM apps, chatbots and integrate AI experiences into existing products/workflows. It comes with everything out of the box that one needs to build LLM apps locally. It can also be used in a multi-tenant setting, making it available for everyone to use in an enterprise. Some highlights of the platform: - Chain multiple LLM models allowing for complex pipelines - Includes a vector database and necessary connectors to help enrich LLM responses with private data - App templates tailored to specific use cases to quickly build LLM apps…
2023 · github.com
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Hi HN! I built LLMKube, a Kubernetes operator for deploying GPU-accelerated LLMs in production. One command gets you from zero to inference with full observability. Why this exists: Regulated industries (healthcare, defense, finance) need air-gapped LLM deployments, but existing tools are either single-node only (Ollama) or lack GPU optimization and SLO enforcement. LLMKube bridges the gap. What's working: - 17x speedup with NVIDIA GPUs (64 tok/s on Llama 3.2 3B vs 4.6 tok/s CPU) - One command: llmkube deploy llama-3b --gpu (auto CUDA setup, scheduling, layer offloading) -…
Nov 2025 · github.com
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Excited to launch a side-project that I've been working on over the past few weeks, LlamaChat: an open-source SwiftUI app which allows you to chat with your local LLaMA models right on your Mac. Powered by the excellent llama.cpp (and Swift wrapper, llama.swift), LlamaChat allows you to import your local LLaMA models (and Alpaca, GPT4All, and soon, Vicuna and Koala) and interact with them locally.
2023 · github.com
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npm for LLMs — install, run, and share AI models. We’ve built llmpm, a CLI tool that makes open-source LLMs installable like packages. llmpm install llama3 llmpm run llama3 You can also package models with your projects so others can reproduce the same setup easily. Website: https://llmpm.co GitHub:https://github.com/llmpm/llmpm-dev
Mar 2026 · llmpm.co
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Hi Hackernews, we're Maitreya, Prateek and Marmik. Over the past few months we've been working on building a platform to build, scale and monitor voice based LLM applications. Demo (https://www.youtube.com/watch?v=OSrOmyR7oQs) 1⃣ Open Source orchestration: We're open-sourcing our orchestration to quickly setup and create LLM based voice driven conversational applications https://github.com/bolna-ai/bolna/ 2⃣ Hosted API Platform: Exposing our managed solution via APIs to build voice driven applications…
2024 · bolna.dev
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I've built an airgapped Retrieval-Augmented Generation (RAG) system for question-answering on documents, running entirely offline with local inference. Using Llama 3, Mistral, and Gemini, this setup allows secure, private NLP on your own machine. Perfect for researchers, data scientists, and developers who need to process sensitive data without cloud dependencies. Built with Llama C++, LangChain, and Streamlit, it supports quantized models and provides a sleek UI for document processing. Check it out, contribute, or suggest new features!
2024 · github.com
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There's LLM Council and similar tools, but they use predefined model lineups. This one is different in a few ways that mattered to me: *Bring your own models.* Mix Ollama (local), OpenAI, Anthropic, Groq, Google — or any OpenAI-compatible endpoint — in whatever combination you want. A council of DeepSeek-R1 + llama2-uncensored + mistral-nemo is a very different deliberation than GPT-4o + Claude + Gemini. *Zero server, zero account, zero storage.* The app is purely static. API calls go directly from your browser to providers. Nothing touches a backend. No tokens, no sessions, no analytics.…
Feb 2026 · github.com
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Here is a production-first Keras-inspired LM framework, built with the advice of François Chollet (ex-Google, creator of Keras and ARC-AGI), our technical advisor. This system have already been deployed in production with our clients (which is why we have already every LLMOps practice implemented). It is also compatible with Jupyter and Marimo to integrate seamlessly in you Data Scientists workflows. You can try the code examples online on HF space and you can find more information in the documentation and FAQ. If you have any feedback for us don't hesitate to join our discord! More releases…
2025 · github.com
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Apr 2026 · github.com
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Hey HN! After using a combination of Unsloth and Axolotl a lot, and finding it generally painful to figure out the right performance tuning for things like batch sizing and multi-GPU sharding, I wrote a small Python lib that sets up known-good LoRA training configurations for Llama 3.1 8B and 70B Instruct, and includes helpers for distilling from larger models or training on serverless finetuning platforms, and includes a walkthrough for distilling DeepSeek-R1 into a Llama 3.1 8B LoRA... But you can use it for pretty much any finetuning task, not just distilling large models!
2025 · github.com
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Hello all, a couple of moons ago I ported karpathy's llama2.c code to run inference on the TinyStories 260K & 15M checkpoints on the on the PS Vita with the ability to download/delete the models on device. Runs showed that the 260K model ran at ~120 tok/s and at 15M ran at 1.8 tok/s, which probably could be a bit higher if it weren't a single threaded application. Had fun working on it as a weekend project, check it out for yourselves: https://github.com/callbacked/psvita-llm
2025 · github.com
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Osaurus is an open-source local inference runtime for macOS, written in Swift and optimized for Apple Silicon. It lets you run Apple Foundation Models locally — fully accelerated by the Neural Engine — while also exposing OpenAI- and Ollama-compatible endpoints, so you can connect your favorite apps, tools, or clients without any code changes. Key points: * Supports Apple Foundation Models natively * Compatible with OpenAI & Ollama APIs * ~7 MB binary, runs locally (no cloud, no telemetry) * MIT Licensed, open source Project: https://osaurus.ai Source:…
Oct 2025 · github.com
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I work on inference scheduling — KV cache-aware routing, load balancing across GPU workers, that kind of thing. I wanted something like k9s but for my inference stack. Nothing existed, so I built it. llmtop is a real-time terminal dashboard for LLM inference workers. It scrapes the Prometheus /metrics endpoints that vLLM, SGLang, and LMCache already expose and shows everything in one view: KV cache usage, queue depth, TTFT/ITL latencies (P50/P99 from histogram buckets), token throughput, prefix cache hit rates. Color-coded — red means go fix it. ``` brew install…
Mar 2026 · github.com
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