OpenViSTAI · Agents · Blockchain · Crypto
Submit a project
Menu
AI Infrastructure & Serving

Project Chronos

Project Chronos is an experimental, open-source Python stack for storage-aware mixture-of-experts inference and training. It predicts expert needs and stages model weights across SSD, RAM and GPU memory using lookahead routing, asynchronous prefetching and per-expert synchronization.

Explore the practical guide ↓

Independent OpenViST project profile, based on linked sources.

Project Chronos — OpenViST project profile in AI Infrastructure & Serving.
OpenViST project illustration. See the linked sources for product details.
Understand the project

Practical overview

When it fits

Use it when an MoE inference or training experiment needs explicit storage-aware expert loading, or when you want to investigate lookahead routing and asynchronous prefetching rather than relying only on experts already resident in GPU memory.

Who it is for

  • Developers prototyping SSD-and-DRAM hybrid inference for mixture-of-experts models.
  • Researchers exploring lookahead routing, asynchronous expert loading or storage-aware model training.
  • Teams evaluating a six-stage workflow from pretraining through alignment and distillation.

Documented uses

  • Prototype MoE inference that loads predicted experts from SSD through RAM toward GPU memory.
  • Explore prompt-prefill expert prediction and decode-time lookahead prefetching.
  • Train models using the documented six-stage pipeline.
  • Use the documented interface to configure, run inference, export models, benchmark and monitor I/O.
Before you choose

Requirements & limits

The public GitHub repository identifies the project as open source under Apache-2.0. The README lists Python 3.10+ and PyTorch 2.4+ requirements; optional vLLM serving is specified for Linux with CUDA.

  • The project labels itself experimental, so its documented design and performance statements should be treated as project claims rather than general guarantees.
  • The README reports 35 ms+ of pipeline slack per token under a simulated 30 ms SSD delay; this is a stated scenario, not a universal performance result.
  • The optional vLLM serving path is documented for Linux and CUDA only.
  • Chronos GGUF artifacts use a Chronos architecture identifier; stock Ollama or llama.cpp builds need an architecture adapter, according to the README.
  • The web UI includes configuration, training, inference, export, benchmarking, auto-tuning and I/O monitoring tools.

Check current requirements in the official documentation. Source-based guidance does not mean OpenViST has run or independently tested this project.

Source-based practical guide

Install Project Chronos from source

Prepare a local source installation using the repository's documented development extra.

Before you start

  • Python 3.10 or later
  • PyTorch 2.4 or later
  • A local Git installation, implied by the documented git clone command

Steps

  1. Clone https://github.com/FonaTech/Project_Chronos.

  2. Change into the Project_Chronos directory.

  3. Install the project and development dependencies with pip install -e ".[dev]".

Expected outcome

An editable local installation with the documented development dependencies.

Guide limitations

  • Installation does not itself run inference or training.
  • The project also uses minimind as its MoE kernel; the README says it is cloned into the Chronos cache on first import if it is not found locally.
Documentation 1 ↗
COMPARE OPTIONS

Compare options for Model inference and serving

These projects document a common task and share a project role. This is a source-based comparison, not an execution benchmark or a guarantee of interchangeability.

ProjectDocumented purposePlatformAccess and requirements
Project ChronosPrototype MoE inference that loads predicted experts from SSD through RAM toward GPU memory.Linux, Self-hosted, ServerThe public GitHub repository identifies the project as open source under Apache-2.0. The README lists Python 3.10+ and PyTorch 2.4+ requirements; optional vLLM serving is specified for Linux with CUDA.
MorphingdbImport a libtorch model and create predictions from values in a PostgreSQL table.Docker, Linux, Self-hosted, ServerThe README documents a Docker image and a build-from-repository option, followed by connecting to PostgreSQL and enabling the pgdl extension.
Gateway — pyadavSend chat-completion requests to a provider through a common server endpoint.ServerThe repository is public and identifies the project as Apache-2.0 licensed. README installation instructions include binaries, platform package managers and Docker.
Evidence and transparency

Sources & review

Official repository ↗
Source record details
Source availability
Active
Sources checked
8 October 2026
Original website title
FonaTech/Project_Chronos · Official homepage title · retained in its source language
How we review sourcesSuggest a correction