AI BriefingFuriosaAI Demonstrates High-Speed Inference Performance Using Furiosa SDK
Key point
FuriosaAI demonstrated high-speed batch inference performance that efficiently processes large-scale LLM requests through its RNGD chip and SDK.
Details
The core challenge of AI inference is processing multiple LLM requests simultaneously while maintaining fast response times and high utilization. FuriosaAI's RNGD chip uses a Tensor Contraction Processor (TCP) architecture instead of the conventional GPU matrix multiplication approach, maximizing data processing efficiency and improving performance per watt.
In this demo, a large-scale quiz generation scenario was implemented using the Llama 3.1 8B (FP8 quantized) model. With just a single RNGD card, leveraging the Furiosa SDK, it supports Automatic Batching and real-time streaming, showing stable performance even when multiple users send requests simultaneously.
The Furiosa SDK provides the following key features:
- Building an OpenAI-compatible server that can be launched with a single command
- FuriosaLLM, which replaces vLLM to support high-performance serving
- Processing large volumes of parallel requests through automatic batching and scheduling
- Real-time system metrics including power consumption, chip temperature, and token throughput
Users can monitor Throughput, Efficiency, Power, and Temperature through a real-time dashboard, allowing them to instantly check the system's performance.
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