AI · alternatives · 2026

24 alternatives to Goku – WASM (wllama)-powered LLM inference and model manager

Below are 24 products that do a similar job, ranked by how close each is in meaning and then by launch-day votes.

1IB
In-Browser Graph RAG with Kuzu-WASM and WebLLM▲158
We show the potential of modern, embedded graph databases in the browser by demonstrating a fully in-browser chatbot that can perform Graph RAG using Kuzu (the graph database we're building) and WebLLM, a popular in-browser inference engine for LLMs. The post retrieves from the graph via a Text-to-Cypher pipeline that translates a user question into a Cypher query, and the LLM uses the retrieved results to synthesize a response. As LLMs get better, and WebGPU and Wasm64 become more widely adopted, we expect to be able to do more and more in the browser in combination with LLMs, so a lot of…
2025 · blog.kuzudb.com · its alternatives →
2
Tiny-vLLM – high performance LLM inference engine in C++ and CUDA▲205
Build your own high performance LLM inference engine in C++ and CUDA - a smaller version of vLLM - jmaczan/tiny-vllm
May 2026 · github.com · its alternatives →
3
Data Robot▲92
Build Better Predictive Models — Faster
2014 · datarobot.com · its alternatives →
4
Talking Machines: OpenAI and Gaussian Processes▲56
Featuring Ilya Sutskever, director of research for OpenAI
2016 · thetalkingmachines.com · its alternatives →
5
Intel® GPU D-series▲76
Memory-rich AI inference without HBM
4d ago · intel.com · its alternatives →
6
Reika▲72
A coding agent CLI designed around small local models first
17d ago · reikacode.com · its alternatives →
7
Beam▲136
Highly efficient agentic 501B parameter open model
18d ago · reflection.ai · its alternatives →
8IG
Inference GUIs for 12 SoTA ML models▲9
2020 · gradiohub.com · its alternatives →
9
Lloyal▲109
Turn open-weight models into AI apps people can download
23d ago · lloyal.ai · its alternatives →
10
Dreamscale Labs▲80
Run the best robot AI models over the cloud
11d ago · dreamscalelabs.com · its alternatives →
11
Talking Machines - Solving intelligence and machine learning fundamentals▲56
Guest: Nando de Freitas (Oxford, Google DeepMind)
2015 · thetalkingmachines.com · its alternatives →
12
A Node Editor for LLMs with Semantic Kernel▲8
2023 · docs.anyassistant.ai · its alternatives →
13
KTransformers–236B Model and 1M Context LLM Inference on Local Machines▲20
Hey Hacker News! We are excited to share our open-source project, KTransformers, a flexible framework designed for cutting-edge LLM inference optimizations! Leveraging state-of-the-art kernels from llamafile and marlin, KTransformers seamlessly enhances the performance of HuggingFace Transformers, making it possible to operate large 236B MoE models or extremely long 1M context locally with promising speed. KTransformers is a Python-centric framework designed with extensibility at its core. By implementing and injecting an optimized module with a single line of code, users gain access to a…
2024 · github.com · its alternatives →
14
Viveka: filter LLM output against a Lean-verified Advaita Vedanta model▲7
Witness-Centered Filter Layer for LLM Applications - SpecStudio-net/Vivek
Jun 2026 · github.com · its alternatives →
15LM
LLaMArch – Minimal, Building Blocks for Large Language Models▲5
2024 · gpavanb1.github.io · its alternatives →
16
Run 500B+ Parameter LLMs Locally on a Mac Mini▲17
Hi HN, I built OpenGraviton, an open-source AI inference engine that pushes the limits of running extremely large LLMs on consumer hardware. By combining 1.58-bit ternary quantization, dynamic sparsity with Top-K pruning and MoE routing, and mmap-based layer streaming, OpenGraviton can run models far larger than your system RAM—even on a Mac Mini. Early benchmarks: TinyLlama-1.1B drops from ~2GB (FP16) to ~0.24GB with ternary quantization. At 140B scale, models that normally require ~280GB fit within ~35GB packed. Optimized for Apple Silicon with Metal + C++ tensor unpacking, plus…
Mar 2026 · github.com · its alternatives →
17
Free Inference Engineer and Model Training Roadmap▲16
Free, gamified roadmaps for LLM engineering: an Inference Engineering path (KV caches, CUDA kernels, production vLLM serving) and a Model Training path (pretraining on a budget, scaling laws, SFT/DPO/GRPO) — 185 tasks with auto-verified milestones instead of a paper certificate.
Aug 2026 · inferquest.org · its alternatives →
18TS
Tablr – Supabase with AI Features▲6
2025 · tablr.dev · its alternatives →
19RL
Refuel-LLM, a large language model for data annotation and enrichment▲9
2023 · app.refuel.ai · its alternatives →
20
I open-sourced a $1M engine for closing loops in embedding space▲8
Hey folks, I’m the creator of WFGY — a semantic reasoning framework for LLMs. After open-sourcing it, I did a full technical and value audit — and realized this engine might be worth $8M–$17M based on AI module licensing norms. If embedded as part of a platform core, the valuation could exceed $30M. Too late to pull it back. So here it is — fully free, open-sourced under MIT. --- ### What does it solve? Current LLMs (even GPT-4+) lack *self-consistent reasoning*. They struggle with: - Fragmented logic across turns - No internal loopback or self-calibration - No modular thought units - Weak…
2025 · github.com · its alternatives →
21
PromptFlow – Build LLM flows as directed graphs▲6
2023 · github.com · its alternatives →
22
Dev, train and *deploy* machine learning models on Cubonacci free beta▲5
2020 · cubonacci.com · its alternatives →
23EV
Efemarai – Visualizing and debugging ML models▲8
2021 · efemarai.com · its alternatives →
24
KANN – mid-scale deep learning and autodiff in ANSI C (4 files; 4k LOC)▲5
A lightweight C library for artificial neural networks - attractivechaos/kann
2018 · github.com · its alternatives →

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Ranked by how close each launch is in meaning, then by votes. Prices were read from each product’s own site when checked and can change. Refine with your own description →