Rust AI Inference Stack: Burn vs. Candle in Production

A.R.C. Freshness Analysis

Tool Stack

1. 1
   Benchmark Your Python Baseline

   Before migrating anything, measure your current inference pipeline. Record latency (time to first token + total completion), memory usage, and cost per 1k requests. This is your migration target — you need to beat it to justify the switch.
2. 2
   Choose Your Framework: Burn or Candle

   These are not interchangeable — they solve different problems. Do you need training support, multi-backend flexibility, or are you porting exotic architectures? → Burn. Do you need inference-only on Hugging Face model weights, fastest path to production, smallest codebase to audit? → Candle. If you need local inference without systems programming investment → Ollama.
3. 3
   Set Up Your Rust Inference Environment

   Install Rust (rustup), add the framework as a dependency, and verify your compute backend compiles correctly. For Burn: cargo add burn with your backend feature flag. For Candle: cargo add candle-core candle-nn candle-transformers.
4. 4
   Load a Model and Run Your First Inference

   For Candle: pull weights from Hugging Face Hub using the hf-hub crate, load a tokenizer, run a forward pass. For Burn: import an ONNX model or train a small model from scratch to verify the pipeline. Confirm you are getting correct outputs before benchmarking.
5. 5
   Benchmark Rust Inference vs. Your Baseline

   Run the same load test you ran in Step 1 against your Rust inference endpoint. Compare p50/p95/p99 latency, memory usage, and throughput. Document the delta — this is the number that justifies or kills the migration.
6. 6
   Deploy to Production with a Fallback

   Ship the Rust inference service behind a feature flag or traffic split. Keep your Python path live. Ramp Rust traffic from 5% → 25% → 100% as you gain confidence. Monitor error rates at each step.
7. 7
   Run A.R.C. Audit on Your New Stack

   Full Stack Integrity Audit on your Rust inference setup. Assesses Architecture (backend-agnostic design, abstraction quality), Reliability (error rates, missing op coverage, escape hatch maturity), and Context (ecosystem momentum, contributor velocity, your team's Rust fluency).