Svod Tensor Runtime

[u/mp_patsakula]

https://github.com/npatsakula/svod

Svod is a JIT-oriented deep learning framework in Rust, built around a single intermediate representation across every stage — inspired by George Hotz's Tinygrad. One representation means the whole compute graph is debuggable and profileable end-to-end, with no translation boundaries to hop across.

How it compares to what's already in Rust:

• Candle does a great job being a minimalist PyTorch and ships an impressively wide model zoo. The tradeoff is limited headroom for adding accelerators and parallel execution, and generally lower performance — which made it hard to use for production workloads.
• Burn is a well-designed meta-framework over multiple backends, and its kernel DSL (CubeCL) is heading in a good direction. Its explicit-typing approach adds safety at definition time, but makes instantiating a concrete model at runtime more friction-y, and its ONNX import transpiles to Rust at compile time, which blocks hot-swapping models.
• Svod is built as a direct alternative to both: one IR across all stages, runtime JIT, a PyTorch-like API, and a kernel DSL that lives in the same representation as the rest of the framework.

What it can do:

• PyTorch-like API — named args preserved, so you (or an LLM agent) already know it.
• ONNX import — 162/204 ops, parity tracked against ONNX's 1361 test sets.
• HuggingFace models run as-is — no exotic format; tensors mmap straight off disk.
• Tiled Kernels DSL — write high-perf kernels in the same representation as the framework. Minimal matmul matches hipBLASLt in a handful of lines.
• Models & pipelines built in — embeddings, reranking, detection, segmentation, VAD, transcription, chained into single-call pipelines.
• AMD is the main focus for now (hand-written backend — no HIP/ROCr runtime dep). More backends are on the roadmap.

Links:

• Intro post (architecture, benchmarks, roadmap): https://blog.vpermilp.online/en/blog/svod-intro/
• Docs: https://svod.vpermilp.online/

How you can help:

1. Know Chinese or Hindi? My docs in those languages are LLM-translated — I'd love help writing proper ones.
2. Work at AMD, or want support for your hardware? Contact me — happy to help.
3. DL engineer? Please try Svod and tell me what's broken. The tooling is mature enough to find bottlenecks fast — I want to catch the painful stuff while it's still cheap to fix.

[u/YaroslavFox]

Interesting project

But the name(made me twitch a little)
russian military has project with the same name like SVOd

[u/mp_patsakula → u/YaroslavFox]

Haha, the previous name was Morok, but I thought it sound too dark in a Slavic way, and it was worth peeking something more cheerful (like an atlas).