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Trillium TPU
Overview :
Trillium TPU is Google Cloud's sixth-generation Tensor Processing Unit (TPU), specifically designed for AI workloads, offering enhanced performance and cost-effectiveness. As a key component of Google Cloud's AI Hypercomputer, it supports large-scale AI model training, fine-tuning, and inference through an integrated hardware system, open software, leading machine learning frameworks, and flexible consumption models. Trillium TPU marks a significant advancement in AI, with impressive improvements in performance, cost-efficiency, and sustainability.
Target Users :
The target audience for Trillium TPU includes AI researchers, developers, and enterprises, particularly those organizations that require handling large-scale AI model training and inference. Its powerful performance and cost-effectiveness make it an ideal choice for businesses and researchers seeking efficient and scalable AI solutions.
Use Cases
AI21 Labs utilized Trillium TPU to accelerate the development of its Mamba and Jamba language models, providing more powerful AI solutions.
Google trained its latest Gemini 2.0 AI model using Trillium TPUs, showcasing its high performance in AI model training.
Trillium TPU excels in multi-step inference tasks, providing significant improvements in inference performance for image diffusion and large language models.
Features
Over 4 times the training performance improvement compared to the previous generation.
Up to 3 times the increase in inference throughput.
67% enhancement in energy efficiency.
4.7 times peak computational performance improvement per chip.
Doubling of high-bandwidth memory (HBM) capacity.
Doubling of inter-chip connectivity (ICI) bandwidth.
Deployment of up to 100K Trillium chips in a single Jupiter network architecture.
Up to 2.5 times improvement in training performance per dollar and up to 1.4 times improvement in inference performance per dollar.
How to Use
1. Log in to the Google Cloud platform and access the Trillium TPU service.
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2. Create or select a project, ensuring it has the necessary permissions to use Trillium TPU.
3. Configure the Trillium TPU resources as needed, including the number of chips and network architecture.
4. Deploy AI models to the Trillium TPU and initiate training or inference tasks.
5. Monitor task performance and utilize Google Cloud tools to optimize model and resource usage.
6. Adjust Trillium TPU resource configuration according to business needs to achieve the best balance of cost and performance.
7. After completing AI tasks, release any unneeded Trillium TPU resources to save costs.
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