airllm vs TradingAgents
A neutral, constraint-first comparison - live GitHub stats and typed relationships, not marketing.
| airllm | TradingAgents | |
|---|---|---|
| Tagline | Repository for running large language models with reduced memory usage on limited GPU hardware. | TradingAgents: Multi-Agents LLM Financial Trading Framework |
| Stars | 22k | 92k |
| Forks | 2.6k | 18k |
| Open issues | 106 | 279 |
| Language | Jupyter Notebook | Python |
| License | Apache-2.0 | Apache-2.0 |
| Last pushed | Jul 7, 2026 | Jul 5, 2026 |
| Categories | Model Training, Inference & Serving | AI Agents, LLM Frameworks |
airllm
AirLLM allows efficient inference of large language models like 70B parameter sizes using only a single 4GB GPU, without applying techniques such as quantization, distillation, or pruning. It supports various large-scale models and enhances performance capabilities through continuous updates focusing on model optimizations.
Jupyter Notebook
TradingAgents
A Python-based framework for developing multi-agent systems in the financial trading domain using large language models.
Python