Custom Rust Inference Engines (Candle, mistral.rs & CUDA)
Build ultra-low overhead, memory-safe inference harnesses in Rust using Candle, mistral.rs, In-Situ Quantization (ISQ), and custom CUDA Driver FFI. Part of the free Inference & Harness Engineering Academy — every lesson below is open to everyone, no signup required.
// LESSONS IN THIS MODULE
- 01Why Rust for Inference Harnesses?20 min · 175 XP
The Hidden Cost of Python Runtimes in Production Python has historically been the lingua franca of AI research. However, in high-throughput enterprise...
- 02Candle & Memory-Mapped Safetensors25 min · 200 XP
Zero-Copy Tensor Loading in Rust Hugging Face's Candle is a minimalist, performant ML framework for Rust. Using memory-mapped ( mmap ) Safetensors fil...
- 03mistral.rs, In-Situ Quantization & Native MCP Serving25 min · 225 XP
In-Situ Quantization (ISQ) and Native MCP in Rust While traditional quantization workflows require offline conversion tools (e.g. llama.cpp quantize o...
- 04Writing Custom CUDA Kernels via Rust FFI (cudarc & PTX)25 min · 250 XP
Bypassing Framework Overhead with Direct CUDA FFI While frameworks like Candle provide rich high-level tensor abstractions, maximizing inference throu...