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airllm

lyogavin/airllm

Inference for AirLLM 70B model with single 4GB GPU

GitHub

GraphCanon updated Sep 20, 2026 · GitHub synced Sep 20, 2026

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35k stars3.6k forksLast push Sep 18, 2026 Jupyter Notebook Apache-2.0

Decision brief

AirLLM is a repository that enables the inference of a 70B parameter model using a single 4GB GPU, with a focus on Chinese language models and generative AI.

Good fit when

When you need to perform inference on a 70B parameter model with limited GPU resources, such as a single 4GB GPU.
If your project involves Chinese language models and you require a lightweight solution for generative AI tasks.

Avoid when

If your project requires inference on models larger than 70B parameters or if you have access to more powerful GPUs than a 4GB model.
For projects that do not involve Chinese language models or generative AI tasks, as AirLLM is specifically optimized for these areas.

Requirements:: Min 4 GB RAM; Installation is straightforward via pip.

Observed Sep 18, 2026 · Source: enrich:decision_facts

airllm alternatives Trust report

airllm vs ollama airllm vs llmfit airllm vs vllm

Verify the decision

Maintenance and security

Full trust report

Maintenance: Very active (0d since push); As of Sep 18, 2026
Provenance: Not a fork · Personal account; As of Sep 18, 2026
Security (OSV): 4 low (4 low); As of Sep 18, 2026

Public GitHub metadata and optional OSV scans. Signals, not a guarantee. Trust methodology.

Install

git clone https://github.com/lyogavin/airllm

How it fits your stack(8)

Typed graph edges - alternatives, integrations, successors, and dependencies. Ranked by relationship type, not raw GitHub stars.

Alternative

ollama

Both AirLLM and ollama are tools designed for efficient LLM inference, though they may have different technical underpinnings or target use cases.

alternative

llama.cpp

Both airllm and llama.cpp offer lightweight GPU inference options for large language models, differing mainly in their implementation and optimization approaches.

alternative

vllm

Both AirLLM and vllm are aimed at making LLM serving easier, faster, and more cost-effective by optimizing inference on limited hardware resources.

alternative

Integrates

transformers

AirLLM's ability to run large models on small hardware can be used in conjunction with the transformers library, which provides a wide range of pre-trained language models and tools for working with them.

integrates

Chinese-LLaMA-Alpaca

Chinese LLaMA & Alpaca LLMs

flash-linear-attention

flash-linear-attention and AirLLM are related in that both aim for efficient large language model inference, with flash-linear-attention focusing more on the underlying attention mechanisms while AirLLM seeks to optimize for lightweight GPU usage.

Relationship graph

Optional deeper exploration of typed edges and category neighbours.

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Same-category neighbours not already linked as typed edges.

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Evidence and technical details

Sourced facts, taxonomy, compatibility claims, README excerpt, and machine-readable endpoints.

Overview

AirLLM is a repository that facilitates the inference of a 70B parameter model using a single 4GB GPU, focusing on Chinese language models and generative AI.

Capability facts

Languages: jupyter notebook
Source: github.language · Sep 18, 2026

Graph entities

Smolagentsframework

Compatibility

Sourced claims from the README excerpt - not unsourced marketing copy.

Python runtimePython

Source: README excerpt (regex_v1, Sep 18, 2026)

pip install airllm

Source link

README

1. Install package First, install the airllm pip package.

For agents

This page has a .md twin and JSON over the API.

/tools/lyogavin-airllm.md GET /api/graphcanon/tools/lyogavin-airllm GET /api/graphcanon/graph?tool=lyogavin-airllm

airllm

Decision brief

Good fit when

Avoid when

Verify the decision

Maintenance and security

Install

How it fits your stack(8)

Alternative

Integrates

Related

Relationship graph

Similar tools

Evidence and technical details

Overview

Capability facts

Categories

Graph entities

Compatibility

Tags

README

For agents