Falcon 180B reached 180 billion parameters trained on 3.5 trillion tokens, marking it as one of the largest open-access large language models released as of 2024. The model scored 68.74 on the Hugging Face Open LLM Leaderboard at launch, outperforming LLaMA 2 70B. Developed by Technology Innovation Institute in Abu Dhabi, the model required approximately 7 million GPU-hours and up to 4,096 GPUs running simultaneously during training.
This article presents verified statistical data on Falcon 180B, covering model parameters, training infrastructure, benchmark results, deployment requirements, and licensing terms.
Falcon 180B Key Statistics
- Falcon 180B contains 180 billion parameters trained on 3.5 trillion tokens as of 2024
- The model achieved a 68.74 score on the Hugging Face Open LLM Leaderboard, the highest among open-access models at release
- Training consumed approximately 7 million GPU-hours using up to 4,096 GPUs simultaneously
- Inference requires 640 GB memory when quantized to FP16, equivalent to eight A100 80GB GPUs
- The training dataset consisted of 85% web data, 3% code, and 12% curated conversations and papers
Falcon 180B Model Architecture and Scale
Falcon 180B features 80 layers with a hidden dimension of 14,848 and a vocabulary size of 65,024 tokens. The model processes a context window of 2,048 tokens.
The architecture represents a significant scaling from its predecessor Falcon 40B, which had 60 layers and a hidden dimension of 8,192. This architectural expansion contributed to improved performance across benchmarks.
| Architecture Metric | Falcon 40B | Falcon 180B |
|---|---|---|
| Number of Layers | 60 | 80 |
| Hidden Dimension | 8,192 | 14,848 |
| Vocabulary Size | 65,024 | 65,024 |
| Context Window | 2,048 tokens | 2,048 tokens |
Source: TII and Hugging Face documentation
Falcon 180B Training Infrastructure and Resources
The training process for Falcon 180B consumed approximately 7 million GPU-hours. TII deployed up to 4,096 GPUs simultaneously during large-scale pretraining phases.
The 3.5 trillion token training dataset broke down into 85% web data, 3% code, and the remainder consisting of curated conversations and research papers. This composition prioritized broad exposure to varied text over narrow, task-specific data.
The infrastructure requirements positioned Falcon 180B among the most resource-intensive open-source AI models available for development and deployment.
Falcon 180B Benchmark Performance
Falcon 180B scored 68.74 on the Hugging Face Open LLM Leaderboard at the time of release. This placed it ahead of LLaMA 2 70B, which scored 67.87, and significantly above Falcon 40B at 58.07.
Performance comparisons to proprietary models showed Falcon 180B performing on par with Google’s PaLM 2-Large across multiple benchmarks. The model positioned between GPT-3.5 and GPT-4 in task performance, depending on specific benchmark criteria.
A supplementary study by Luo (2024) reported coefficient values of 1.96 ± 0.08 for the base model and 1.97 ± 0.10 for the chat variant when comparing model behavior to human baselines.
Falcon 180B Memory and Deployment Requirements
Running Falcon 180B requires substantial memory resources. Inference with FP16 quantization demands approximately 640 GB of memory, typically requiring eight A100 80GB GPUs.
With int4 quantization, the memory requirement drops to 320 GB, manageable with eight A100 40GB GPUs. These specifications create barriers for smaller organizations considering at scale.
| Quantization Type | Memory Required | GPU Configuration |
|---|---|---|
| FP16 | 640 GB | Eight A100 80GB GPUs |
| int4 | 320 GB | Eight A100 40GB GPUs |
Source: Pinecone and Machine Learning Archive
Falcon 180B Licensing Terms and Commercial Usage
Falcon 180B operates under the TII Falcon 180B TII License, derived from Apache 2.0 with additional constraints around hosting use. Commercial use is permitted, but hosting the model for others or deploying it as a public API may require separate permission from TII.
The licensing structure allows open access while maintaining control over certain deployment scenarios. Organizations planning production use must verify compliance with the specific license terms before implementation.
Falcon 180B Training Dataset Composition
The 3.5 trillion token pretraining dataset emphasized breadth over specialized curation. Web data comprised 85% of the total, with code representing 3% and curated conversations and research papers making up the remaining 12%.
This distribution reflects a training strategy focused on general language understanding rather than domain-specific optimization. The heavy reliance on web data suggests exposure to diverse topics and writing styles.
FAQ
How many parameters does Falcon 180B have?
Falcon 180B contains 180 billion parameters trained on 3.5 trillion tokens. The model was released by Technology Innovation Institute in Abu Dhabi in 2024.
What GPU resources are needed to run Falcon 180B?
Running Falcon 180B requires eight A100 80GB GPUs (640 GB total memory) for FP16 quantization or eight A100 40GB GPUs (320 GB) for int4 quantization.
How does Falcon 180B compare to other language models?
Falcon 180B scored 68.74 on the Hugging Face Open LLM Leaderboard, outperforming LLaMA 2 70B at 67.87. It performs comparably to Google’s PaLM 2-Large and ranks between GPT-3.5 and GPT-4.
What was the training cost for Falcon 180B?
Training Falcon 180B consumed approximately 7 million GPU-hours using up to 4,096 GPUs simultaneously during large-scale pretraining phases. The exact monetary cost was not publicly disclosed.
Can Falcon 180B be used commercially?
Yes, commercial use is permitted under the TII Falcon 180B TII License. However, hosting the model as a public API or service may require separate permission from Technology Innovation Institute.
Sources
Hugging Face Falcon 180B Model Card
