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Sketchpad: A Pure Rust Deep Learning Inference Framework Supporting Image, Video Generation, and Large Language Models

This article introduces the Sketchpad project, a deep learning inference engine based on Rust and the Burn framework. It supports image generation models like Stable Diffusion, SDXL, and Flux; video generation models such as CogVideoX and Mochi; and various large language models including LLaMA, Mistral, and Qwen, providing a new option for AI applications that prioritize performance and safety.

Rust深度学习推理引擎Burn框架Stable Diffusion视频生成大型语言模型多模态AI内存优化

Published 2026-06-17 02:15Recent activity 2026-06-17 02:24Estimated read 5 min

Sketchpad: A Pure Rust Deep Learning Inference Framework Supporting Image, Video Generation, and Large Language Models

Section 01

Sketchpad: Pure Rust Deep Learning Inference Framework Overview

Project Basic Info

Author/Maintainer: rhi-zone
Source: GitHub (link)
Core: A pure Rust deep learning inference framework built on the Burn framework.
Supported Tasks: Multi-modal (image generation, video generation, large language model inference)
Key Features: Multi-backend deployment, memory optimization techniques, no dependency on Python runtime or ONNX Runtime.

This framework aims to provide a high-performance and memory-safe alternative for AI application deployment.

Section 02

Background: Rust's Role in AI Inference

Deep learning inference has long been dominated by Python and C++:

Python: Dynamic typing and GIL restrict concurrency performance.
C++: Memory safety issues lead to high maintenance costs.

Rust, with its balance of performance, memory safety, and concurrency, is emerging in AI infrastructure. Sketchpad leverages Rust to avoid Python/ONNX dependencies, offering a new technical path for AI deployment.

Section 03

Core Architecture & Multi-Backend Support

Architecture

Sketchpad is built on the Burn framework, which uses compile-time graph optimization to achieve near-native execution efficiency without sacrificing flexibility.

Multi-Backend Support

CPU: Based on Rust's ndarray library (no external dependencies, edge-friendly).
CUDA: Directly calls NVIDIA GPU via CUDA driver (reduces cross-language overhead).
WebGPU: Supports browser/native execution via WebGPU standard (future-proof cross-platform).
libtorch: Binds to PyTorch's C++ library for easy model migration.

Rust's traits and generics enable zero-cost abstraction across backends.

Section 04

Supported Multi-Modal Models

Image Generation

Stable Diffusion (1.x/2.x), SDXL, Flux (Flow Matching), SD3, PixArt, SANA.

Video Generation

CogVideoX (diffusion transformer), Mochi (3D U-Net), LTX-Video, Wan.

Large Language Models

Transformer-based: LLaMA, Mistral, Qwen, Gemma, Phi, DeepSeek (MoE).
Non-Transformer: RWKV (linear attention), Mamba (SSM), Jamba (hybrid).

Section 05

Memory Optimization Techniques

To address production memory challenges:

VAE Tiling: Splits images into blocks to reduce peak memory for high-resolution content.
Model Offloading: Unloads parameters to CPU/disk when GPU memory is insufficient.
Quantization: Supports INT8/INT4 low-precision inference for edge devices.

Section 06

Project Status & Future Directions

Current Status

Experimental stage (not production-ready, needs full testing).

Future Plans

Improve test coverage and CI/CD workflow.
Integrate more quantization schemes.
Explore distributed inference support.
Follow Rust's async ecosystem for high-concurrency services.

Section 07

Conclusion & Key Takeaways

Sketchpad demonstrates Rust's potential in AI infrastructure, combining safety and performance. It offers an alternative to Python/C++ for teams prioritizing modern tech stacks.

While Rust's AI ecosystem is less mature, Sketchpad mitigates this via libtorch/ONNX support. It's a valuable reference for Rust-based AI solutions.

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