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Rethinking Inference
New Solutions Give Better Alternatives to GPUs
Deploying AI inference on GPUs provides a well-known performance gain over CPUs, but it’s not always the best option. Advances in AI-focused ASICs and even FPGAs in recent years give more options both for datacenter and edge use cases.
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Advantages for Datacenter
- Need real-time, batch size 1 processing? Our Groq AI-based accelerators are simply a better fit here than GPUs, due to a more streamlined processing flow in the design of the chip.
- Need to scale to large volumes (10s or 100s of nodes)? Moving to larger numbers of GPUs suffers from scaling inefficiencies. Instead, a Groq AI chip-based solution has a near-linear scaling, while keeping low latency and effectively acting as one large compute core versus a network of chips and cores.
Advantages for Edge
- As latency is critical for edge AI, our partner EdgeCortix has created a combination of hardware (both FPGA-based and ASIC) and software to address low-latency inference.
- EdgeCortix chips provide many times higher performance-per-watt over GPUs, including in the sub-10 watt power range.
