FAQ & Roadmap

Roadmap

2026 Q1 — Torch Inductor in Rust

• 2026 and beyond
  • Q3: teeny-llm — vLLM-style inference, 2x faster
  • Q4: performance optimization
• 2027 and beyond
  • Q2: embedded support
  • Q3: sparsity/quantization support
  • Q4: observability/metrics

(See the repository README for the current, authoritative roadmap — this chapter may lag behind.)

FAQ

Why create this project when PyTorch, TensorFlow, and tinygrad already exist?

Those frameworks excel at development and deployment on large-scale infrastructure. We believe the future of AI lies in devices of all sizes — from edge devices to massive clusters. Existing frameworks are relatively heavy; TensorFlow Lite comes closest to this vision, but a modern, memory-safe language was preferred over C.

At the same time, we didn’t want to sacrifice distributed training or other advanced features offered by larger projects — hence teenygrad: a lightweight but powerful alternative.

I’m interested in contributing — how do I get started?

See Contributing to Teenygrad.