AI Inference
AI Inference refers to the process of using a trained Machine Learning model to make predictions or decisions based on new, unseen data. It is the operational phase following Model Training.
Hardware Acceleration
Efficient inference relies heavily on specialized hardware to reduce latency and power consumption.
- OpenAI Jalapeño: OpenAI’s first custom AI chip, codenamed “Jalapeño,” designed to optimize inference workloads.
- Architecture: Details on the design and first benchmark results are documented in OpenAI Jalapeño Custom AI Chip: First Benchmarks and Design.
- Strategic Impact: Custom silicon allows for greater control over performance per watt and cost efficiency in large-scale deployment.
Key Metrics
- Latency: Time taken to generate a single output token.
- Throughput: Number of tokens processed per second.
- Power Efficiency: Performance relative to energy consumption.