Open source alternative to Hugging Face Inference Endpoints, Google Vertex AI Prediction and Fireworks AI
Last verified
AirLLM dramatically reduces inference memory usage, enabling large language models up to 2.8T parameters to run on low-end GPUs.
Developers who want to build and test applications using large language models but only have access to GPUs with limited VRAM (e.g., 4GB, 8GB) can use AirLLM to run powerful models locally.
User has access to high-VRAM GPUs (e.g., 24GB+) or cloud-based inference services.
Researchers exploring new LLM architectures or fine-tuning existing ones can leverage AirLLM to conduct experiments with larger models on more affordable or available hardware, reducing computational costs.
Research primarily focuses on model training from scratch on large clusters, where VRAM is not a bottleneck.
For deploying LLMs on edge devices or embedded systems where memory and computational resources are severely constrained, AirLLM provides a viable solution to run complex models efficiently.
Deployment targets are high-performance servers or cloud environments with ample resources.
Individuals interested in running the latest open-source LLMs on their personal computers (including Apple silicon Macs) for personal projects, learning, or local AI agents, without needing to purchase expensive professional-grade GPUs.
User is only interested in using cloud-based LLM APIs and does not require local inference.
The concrete problem AirLLM solves is the high VRAM requirement of large language models, which typically restricts their inference to expensive, high-end GPUs. This limitation prevents developers, researchers, and hobbyists with commodity hardware or edge devices from easily experimenting with, developing, or deploying state-of-the-art LLMs due to prohibitive hardware costs.
AirLLM allows large language models, including 70B Llama 3.x and 2.8T Kimi K3, to run on GPUs with as little as 4GB of VRAM by streaming one layer or expert at a time.
Implements block-wise quantization-based model compression, which can speed up inference by up to 3 times with almost ignorable accuracy loss.
Supports virtually all popular open LLMs from Hugging Face, including Llama, Qwen, DeepSeek, Mistral, Mixtral, Phi, Gemma, ChatGLM, Baichuan, InternLM, and Yi, through a unified AutoModel interface.
Enables running large language models on Apple silicon devices, making it accessible to a wider range of users.
Provides various configurations such as 4bit/8bit compression, profiling mode, custom layer saving paths, Hugging Face token integration for gated models, prefetching, and an option to delete original models to save disk space.
pip install airllmThis error most likely indicates that you have run out of disk space. The process of splitting and saving models layer-wise is very disk-consuming. You may need to extend your disk space or clear the Hugging Face cache.
This error typically occurs if you are trying to load Qwen or ChatGLM models using a Llama2 class. Ensure you use from airllm import AutoModel and then AutoModel.from_pretrained(...) to automatically detect the model type.
Some models are gated on Hugging Face and require an API token. You can provide your Hugging Face API token using the hf_token argument when initializing the model, for example: model = AutoModel.from_pretrained("meta-llama/Llama-2-7b-hf", hf_token='HF_API_TOKEN').
If a model's tokenizer lacks a padding token, you can either set one or, more simply, turn off the padding configuration by setting padding=False in your tokenizer call: input_tokens = model.tokenizer(input_text, ..., padding=False).
Alternative to
Together AI
High-throughput LLM inference server with OpenAI-compatible API
Alternative to
Fireworks AI
MIT-licensed LLM inference engine built for agentic coding workloads.
Alternative to
Baseten
One self-hosted cluster for all the models your agents need
Alternative to
Hugging Face Inference Endpoints
Local inference engine for DeepSeek V4 Flash/PRO and GLM 5.2
Alternative to
LM Studio
Run large language models locally on Mac, Linux, or Windows
Alternative to
Grok Bot
Self-hosted AI agent: persistent memory, multi-channel chat, model choice.
Featured
Alternative toClaude Managed Agents
Open-source agent deployment API. Self-host or use Omnara Cloud.
Alternative to
Superhuman
AI email assistant that drafts replies and clears your inbox
Alternative toGrok Bot
AI teammates you own: your keys, your model, your machine.
The projects we add and every new guide. Free, unsubscribe any time.
Claude Code to 
OpenCode
Jira to 
Windshift
Notion to 
AppFlowy
Slack to 
Mattermost
Linear to 
GitLab
Wispr Flow to 
Handy
CapCut to 
Concat
Grok Bot to 
rakazo
Superhuman to 
Inbox Zero
Airtable to 
AITable
Muse to 
OpenClaw
GitHub Copilot to 
Cline
Datadog to 
Netdata
Microsoft Teams to 
Rocket.Chat
Pinecone to 
Qdrant
Zapier to 
n8n
Devin AI to 
OpenHands
Firebase to 
Supabase
1Password to 
KeePassXCConfluence to 
Docmost
Contentful to 
Strapi
Adobe Premiere Pro to 
Shotcut
ChatGPT to 
AnythingLLM
LangSmith to 
Langfuse
