Software Alternatives, Accelerators & Startups
Register | Login
t

tinygrad VS Lucebox

Compare tinygrad VS Lucebox and see what are their differences

Topolog
Topolog is a goal planner that models your plans as a directed graph and allows you to execute tasks in order, then schedules your days around them. featured
Contents:
  1. ยป Base Details
  2. ยป Videos
  3. ยป Reviews
  4. ยป Alternatives

tinygrad logo tinygrad

This may not be the best deep learning framework, but it is a deep learning framework.

Lucebox logo Lucebox

The computer for local AI
Not present
  • Lucebox Lucebox Thumbnail
    Lucebox Thumbnail //
    2026-06-13
  • Lucebox Lucebox Demo
    Lucebox Demo //
    2026-06-13

Lucebox is a plug-and-play computer built for running local AI models and agents at full speed. Inside the custom chassis, a Ryzen AI MAX+ 395 with 128GB of unified LPDDR5X memory is paired with an RTX 3090, and the two work together through an open-source inference engine hand-tuned for exactly this hardware.

The architecture is what makes it fast. Large models live in the 128GB unified memory tier, while the 3090's high-bandwidth VRAM acts as a fast tier. Speculative decoding (DFlash) and speculative prefill (PFlash) bridge the two, producing inference speeds up to 10x higher than llama.cpp on the same silicon and beating machines like the Mac Studio and DGX Spark at a fraction of their effective cost.

Getting started takes minutes, not weeks. The whole stack comes pre-installed, and a single CLI command deploys any open model. There is no driver configuration, no quantization trial and error, no environment debugging. The software is fully open source on GitHub (Luce-Org/lucebox-hub), with thousands of stars and dozens of contributors improving the kernels in the open.

For developers and teams, the payoff is threefold: top-of-class tokens per second at $4,900, complete data privacy since nothing touches the cloud, and a fixed hardware cost that replaces ever-growing API bills. If you want to run agents around the clock on hardware you own, Lucebox is the computer for it.

tinygrad

Website
tinygrad.org
Pricing URL
-
$ Details
-
Release Date
-
Edit details

Lucebox

Website
lucebox.com
Pricing URL
Official Lucebox Pricing
$ Details
paid $4900.0 / One-off ($4,900 - One time payment)
Release Date
2026 April
Startup details
Country
United States
State
California
City
San Francisco
Founder(s)
Alessandro Puppo
Employees
1 - 9
Edit details

tinygrad features and specs

  • Lightweight
    Tinygrad is designed to be minimalistic and easy to understand, making it a lightweight alternative to larger, more complex machine learning frameworks. This makes it easier to learn, modify, and extend for developers.
  • Educational
    The simplicity and clarity of tinygrad's codebase make it an excellent educational tool for individuals looking to understand the fundamentals of machine learning frameworks and backpropagation.
  • Pythonic
    Tinygrad is written in Python, which is highly popular and accessible to a wide range of developers. Its Pythonic nature ensures that it is easy to read and integrates well with other Python libraries and tools.
  • Minimal Dependencies
    By keeping dependencies to a minimum, tinygrad reduces overhead and potential compatibility issues, making it easier to set up and run on different systems.

Possible disadvantages of tinygrad

  • Limited Features
    Due to its minimalistic design, tinygrad lacks many of the advanced features and optimizations found in more comprehensive frameworks, which may limit its applicability for complex projects.
  • Performance
    Tinygrad may not be as optimized for performance as larger frameworks like TensorFlow or PyTorch, particularly for large-scale models and datasets, potentially leading to slower training times.
  • Community and Support
    As a smaller project, tinygrad has a smaller community and less official support compared to more widely adopted frameworks, which can make it more challenging to find resources and help.
  • Evolving Codebase
    Being a relatively new and evolving project, tinygrad may undergo significant changes, which can affect stability and require users to frequently adjust their code to keep up with updates.

Lucebox features and specs

  • Hybrid memory architecture
    128GB of LPDDR5X unified memory on the Ryzen AI MAX+ 395 holds large models, while the RTX 3090's 24GB of fast GDDR6X serves as a high-bandwidth tier. Speculative decoding across the two tiers delivers up to 10x faster inference than comparable single-tier machines.
  • Custom open-source inference engine
    Lucebox ships with hand-tuned CUDA kernels, DFlash speculative decoding, and PFlash speculative prefill (10x faster than llama.cpp), all open source with 2,000+ GitHub stars and an active contributor community.
  • One-command model deployment
    A single CLI pulls, configures, and serves any open model. No driver hunting, no quantization guesswork, no environment setup. Plug it in and run inference in minutes.
  • Pre-tuned for the exact hardware
    Unlike generic builds, the entire software stack is optimized for this specific chip pairing, so you get the full performance the silicon is capable of, out of the box.

tinygrad videos

+ Add

PyTorch vs Tinygrad vs Mojo: Which is better? | George Hotz and Lex Fridman

Lucebox videos

No Lucebox videos yet. You could help us improve this page by suggesting one.

Add video

Category Popularity

0-100% (relative to tinygrad and Lucebox)

tinygrad

Lucebox

Data Science And Machine Learning
100 100%
Data Science And Machine Learning
0% 0
Open Source
0 0%
Open Source
100% 100
Machine Learning
100 100%
Machine Learning
0% 0
Computer
0 0%
Computer
100% 100

Questions & Answers

As answered by people managing tinygrad and Lucebox.

What's the story behind your product?

Lucebox's answer:

I am the founder of Lucebox, focused on making local AI faster, more accessible, and easier to deploy. My goal is to give developers a powerful system that runs AI models efficiently while keeping data private. We are building hardware and software that help teams unlock the full potential of local AI.

Which are the primary technologies used for building your product?

Lucebox's answer:

CUDA 12+, C++17, Python 3.10+, GGUF, DFlash & PFlash, NVIDIA RTX 3090, AMD Ryzen AI MAX+ 395, Linux

User comments

Share your experience with using tinygrad and Lucebox. For example, how are they different and which one is better?
Log in or Post with

Social recommendations and mentions

Based on our record, tinygrad seems to be more popular. It has been mentiond 8 times since March 2021. We are tracking product recommendations and mentions on various public social media platforms and blogs. They can help you identify which product is more popular and what people think of it.

tinygrad mentions (8)

  • Running local models is good now
    Anybody used a tinybox? https://tinygrad.org/#tinybox The most "affordable" option is red v2 with 64GB GPU ram and costs $12,000. This is only ("only") 1.5x-3x the price of a beefy desktop (https://pcpartpicker.com/builds/), and could crush inference work even on bigger models. It could support coding tasks for a small team of developers, or run an AI agent for every person in your household... - Source: Hacker News / 18 days ago
  • Open Source AI Must Win
    Https://tinygrad.org/#tinybox I'm not sure exactly why you would buy through them vs rolling your own if you could afford the equivalent hardware. I'm a firm supporter of local inference though so good on them for doing something. - Source: Hacker News / 21 days ago
  • Was my $48K GPU server worth it?
    Buy one of these next time, https://tinygrad.org/#tinybox. At least geohot knows what he is doing. - Source: Hacker News / about 2 months ago
  • Tiny Corp's Exabox
    The specifications are listed here: https://tinygrad.org/. - Source: Hacker News / 3 months ago
  • Five Years of Tinygrad
    From [0]: "When we can reproduce a common set of papers on 1 NVIDIA GPU 2x faster than PyTorch. We also want the speed to be good on the M1. ETA, Q2 next year." [0] https://tinygrad.org/#tinybox. - Source: Hacker News / 6 months ago
View more

Lucebox mentions (0)

We have not tracked any mentions of Lucebox yet. Tracking of Lucebox recommendations started around Jun 2026.

What are some alternatives?

When comparing tinygrad and Lucebox, you can also consider the following products

PyTorch - Open source deep learning platform that provides a seamless path from research prototyping to...

Olares - Self-hosted home cloud OS for running apps, managing files, and securely accessing your services from anywhere.

micrograd - A tiny Autograd engine (with a bite! :)).

NVIDIA - We create the worldโ€™s fastest supercomputer and largest gaming platform.

TensorFlow - TensorFlow is an open-source machine learning framework designed and published by Google. It tracks data flow graphs over time. Nodes in the data flow graphs represent machine learning algorithms. Read more about TensorFlow.

PyCaret - open source, low-code machine learning library in Python

Loading...