Synogate’s LLM Accelerator is a custom-designed hardware solution that accelerates transformer-based large language models, enabling faster and more efficient inference. Our innovative approach reduces latency for faster time-to-complete-answer, making it ideal for applications such as natural language processing, chatbots, and virtual assistants.

Scalable from very compact solutions to rack-mountable servers, we create the ideal solution for any use case.

Get in touch to learn more

To harness the maximum of productivity that LLMs can offer, inference speed is critial. Faster token generation enables more responsive and interactive applications, such as real-time language translation and interpretation, instantaneous chatbots and virtual assistants, and interactive writing tools like grammar and spell checkers. This improved user experience can lead to increased customer satisfaction, loyalty, and ultimately, revenue. When assisting workers with their task, the speed at which prompts are answered greatly intensifies the interaction with the LLMs, maximising their effect on productivity. The competitive advantage gained by processing and responding to text-based data in real-time is substantial, particularly in industries like customer service, finance, and healthcare. Organizations that can provide instant answers to customer inquiries or process large volumes of financial text data will gain a significant edge over their competitors.

Consumer GPU vs data center GPU vs custom chip

Performance for running Large Language Models (LLMs) depends on memory bandwidth: for processing and creating Tokens, or fragments of words, an LLM needs to quickly read from memory the Billions of weights, or probabilities, which it is made of. Synogate’s LLM Accelerator is designed specifically for LLM inference, using memory bandwidth much more efficiently, enabling much faster response times. A drop-in replacement for dedicated hardware like Graphics Processing Units (GPUs), it works “out-of-the-box” with your model of choice. It requires minimal setup: no CUDA, no PyTorch, no adaptation of models or applications - simply connect it to your system and enjoy best value and user experience.

LLMs are different from previous forms of artificial intelligence in many ways. From a hardware perspective, a key difference is the bottleneck shifting from compute to memory bandwidth. This means that the limiting factor of hardware performance is speed at which models can be read from memory, not processing power. Architecture of GPUs and AI accelerators predates transformer architecture. They are also designed to accelerate a wide range of use cases, meaning that they are good, but not optimal for running LLMs at minimum latency and energy consumption. Custom digital circuit design allows extremely precise control over signal flow, allowing for extremely efficient memory bandwidth usage. With precise synchronization of calculations, we can harness the full potential of our hardware, delivering impressive performance and scalability.