Tinybox: Offline AI Device with 120B Parameters — George Hotz's Tinygrad Ships Red V2

2026-03-21T21:21:55.000Z·2 min read
George Hotz and the tinygrad team have begun shipping the Tinybox — a personal AI inference computer capable of running 120B+ parameter models entirely offline. Red V2 starts at $12,000, Green V2 Blackwell at $65,000.

Tinybox: Offline AI Device with 120B Parameters

George Hotz and the tinygrad team have announced that the Tinybox is now shipping — a purpose-built AI inference computer designed for running large language models entirely offline.

Available Models

Tinybox Red V2 — In stock, $12,000:

Tinybox Green V2 (Blackwell) — In stock, $65,000:

Exabox — Coming 2027, ~$10M:

Why It Matters

The Tinybox represents a growing movement toward personal, offline AI inference hardware:

  1. Privacy-first AI. No data leaves the machine. Enterprise and privacy-sensitive users can run powerful models without cloud dependencies.
  1. Democratization of AI compute. At $12,000 for the Red V2, running 120B parameter models is becoming accessible to research labs and well-funded individuals — a fraction of cumulative cloud GPU costs.
  1. Alternative to NVIDIA ecosystem. The Red V2 uses AMD GPUs with tinygrad software, reducing dependence on NVIDIA's CUDA monopoly.
  1. Open-source software stack. Built entirely on tinygrad (open source), allowing full customization and transparency.
  1. Scalability path. The Exabox concept shows tiny corp's ambition to scale from desktop to datacenter class.

Market Context

The Tinybox enters a market increasingly focused on inference-optimized hardware as AI model training costs stabilize and deployment becomes the bottleneck. Competitors include Apple Silicon for local inference, NVIDIA Jetson for edge AI, and cloud inference services like Groq and Cerebras.

The key differentiator is the combination of open-source software (tinygrad), competitive pricing, and offline-first design philosophy.

Source: tinygrad.org | HN Discussion

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