# nebuly-ai/optimate

A collection of libraries to optimise AI model performances

Repository: https://github.com/nebuly-ai/optimate
Canonical: https://ross.abutalabs.com/products/optimate
Homepage: https://www.nebuly.com/
Language: Python
License: Apache-2.0
License Family: permissive
Topics: ai, analytics, artificial-intelligence, deeplearning, large-language-models, llm
Last push: 2024-07-22T02:07:03+00:00

## Health v2 (maintenance only)
Score: 23/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 0, release rhythm 8, longevity 100
- inputs: {"age_days": 1663, "days_push": 773, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: none
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 8329, forks 616 (observed 2026-08-28T04:10:20.320420+00:00)

## What it is
OptiMate is a collection of Python libraries from Nebuly AI for optimizing AI model performance, including Speedster for inference acceleration, Nos for Kubernetes GPU utilization, and ChatLLaMA for fine-tuning and RLHF alignment. The repository is now in a legacy phase and is no longer actively maintained.

## Use cases
- reduce inference costs for deep learning models on gpus and cpus
- speed up model inference latency
- fine-tune llms with rlhf alignment
- maximize gpu utilization on kubernetes clusters
- optimize ai models for specific hardware

## When to choose
- you need proven model optimization techniques and are comfortable maintaining the code yourself
- you want to study or fork inference acceleration or RLHF fine-tuning implementations

## When to avoid
- you need actively maintained tooling with official support
- you want production-ready optimization libraries with updates for new hardware and model architectures

## Facets
- artifact type: library
- maturity: abandoned
- function: machine-learning, llm-inference, llm-training, benchmarking
- domain: machine-learning, deep-learning, large-language-models, developer-tools
- platform: python
- tags: model-optimization, inference-optimization, fine-tuning, rlhf, gpu-cost-reduction, legacy, gpu, linux

## Member repositories
- nebuly-ai/optimate (main) score 23

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:10:20.320420+00:00.
- Health v2: computed from the inputs above; adoption is never an input.
- Inferred fields (summary, facets, guidance): AI-extracted, prompt v1, taxonomy v1, on 2026-08-29T17:27:45.762192+00:00, confidence not recorded.
  - readme: https://github.com/nebuly-ai/optimate (fetched 2026-08-28T04:10:20.320420+00:00, sha 5ca9b1cf6783)
  - homepage: https://www.nebuly.com/ (fetched 2026-08-29T08:27:37.738653+00:00, sha 451fcd929878)
- Data as of 2026-08-30T08:39:29.467469+00:00.