# Mega4alik/ollm

Repository: https://github.com/Mega4alik/ollm
Canonical: https://ross.abutalabs.com/products/ollm
Language: Python
License: MIT
License Family: permissive
Last push: 2026-07-27T03:07:29+00:00

## Health v2 (maintenance only)
Score: 60/100 (v2, computed 2026-09-03T02:20:16.233290+00:00)
- activity 94, release rhythm 35, longevity 27
- inputs: {"age_days": 382, "days_push": 37, "days_rel": null, "gap_med": null, "n_releases_24m": 0}
- flags: no_releases
- formula: round(0.45*activity + 0.35*rhythm + 0.20*longevity); archived -> min(score, 10)

## Adoption (not part of the score)
Stars 2788, forks 260 (observed 2026-08-28T04:07:21.977248+00:00)

## What it is
oLLM is a lightweight Python library for large-context LLM inference built on Hugging Face Transformers and PyTorch. It offloads weights and KV cache to SSD to run models like Llama 3.1 8B or Qwen3-Next-80B at 100k context on consumer GPUs with as little as 8GB VRAM, without quantization.

## Use cases
- run 100k context LLM inference on an 8GB consumer GPU
- serve large-context offline workloads without quantizing models
- run an 80B parameter model from SSD with minimal VRAM
- do multimodal inference with audio or image inputs
- run Llama models with PEFT adapters at long context

## When to choose
- you have a small consumer GPU but large SSD and need long-context inference
- you want full-precision (fp16/bf16) inference without quantization
- your workload is offline/batch and throughput matters more than latency

## When to avoid
- you need low-latency interactive serving on high-end hardware
- you want quantized models for maximum speed
- you have no fast SSD or NVIDIA GPU available

## Facets
- artifact type: library
- maturity: active
- function: llm-inference, machine-learning, gpu-computing
- domain: large-language-models, machine-learning, deep-learning
- platform: python
- tags: large-context, vram-offloading, consumer-gpu, fp16, kv-cache-offload, multimodal, huggingface-transformers, gpu, linux

## Member repositories
- Mega4alik/ollm (main) score 60

## Provenance
- Observed fields: from GitHub, fetched 2026-08-28T04:07:21.977248+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 2026-08-30T08:15:31.145654+00:00, confidence not recorded.
  - readme: https://github.com/Mega4alik/ollm (fetched 2026-08-28T04:07:21.977248+00:00, sha 5dcf571efcba)
  - registry_pypi: https://pypi.org/pypi/ollm/json (fetched 2026-08-29T09:55:08.027859+00:00, sha 70d7e38d9290)
- Data as of 2026-08-30T08:39:29.467469+00:00.