artificial intelligence (ai) continues to evolve rapidly, but with that evolution comes a number of technical challenges that must be overcome for the technology to truly flourish. One of the most pressing challenges today lies in inference performance. Large language models (LLMs), such as those used in GPT-based applications, are computationally intensive. The bottleneck occurs during inference, the stage in which trained models generate answers or predictions. This stage often faces limitations due to the limitations of current hardware solutions, making the process slow, energy-intensive, and cost-prohibitive. As models get larger, traditional GPU-based solutions fall increasingly short in terms of speed and efficiency, limiting the transformative potential of ai in real-time applications. This situation creates the need for faster and more efficient solutions to keep pace with the demands of modern ai workloads.
Cerebras Systems inference becomes 3x faster! Call 3.1-70B at 2100 tokens per second
Cerebras Systems has made significant progress, ai/blog/cerebras-inference-3x-faster” target=”_blank” rel=”noreferrer noopener”>claiming that their inference process is now three times faster than before. Specifically, the company has achieved a staggering 2,100 tokens per second with the Llama 3.1-70B model. This means Cerebras Systems is now 16 times faster than the fastest GPU solution currently available. This type of performance jump is similar to a generation-long upgrade in GPU technology, like going from the NVIDIA A100 to the H100, but it's all achieved through a software upgrade. Furthermore, it's not just the larger models that benefit from this increase: Cerebras offers 8 times the speed of GPUs running the much smaller Llama 3.1-3B, which is 23 times smaller in scale. These impressive advances underscore the promise that Cerebras brings to the field, making efficient, high-speed inference available at an unprecedented pace.
Technical improvements and benefits
The technical innovations behind Cerebras' latest performance leap include several internal optimizations that fundamentally improve the inference process. Critical cores like matrix multiplication (MatMul), reduce/spread, and element operations have been completely rewritten and optimized for speed. Cerebras has also implemented asynchronous wafer I/O computation, which allows communication and data computation to overlap, ensuring maximum utilization of available resources. Additionally, advanced speculative decoding has been introduced, which effectively reduces latency without sacrificing the quality of the generated tokens. Another key aspect of this improvement is that Cerebras maintained 16-bit precision for the original model weights, ensuring that this increase in speed does not compromise the accuracy of the model. All of these optimizations have been verified through meticulous artificial analysis to ensure that they do not degrade output quality, making the Cerebras system not only faster but also reliable for enterprise-grade applications.
Transformative potential and real-world applications
The implications of this performance increase are far-reaching, especially when considering the practical applications of LLMs in sectors such as healthcare, entertainment, and real-time communication. GSK, a pharmaceutical giant, has highlighted how Cerebras' improved inference speed is fundamentally transforming its drug discovery process. According to Kim Branson, senior vice president of ai/ML at GSK, Cerebras' advances in ai are enabling intelligent research agents to work faster and more effectively, providing a critical advantage in the competitive field of medical research. Similarly, LiveKit, a platform that powers ChatGPT's voice mode, has seen a drastic improvement in performance. Russ d'Sa, CEO of LiveKit, commented that what used to be the slowest step in their ai process has now become the fastest. This transformation is enabling instant voice and video processing capabilities, opening new doors for advanced reasoning, intelligent real-time applications, and enabling up to 10 times more reasoning steps without increasing latency. The data shows that the improvements are not just theoretical; They are actively reshaping workflows and reducing operational bottlenecks across industries.
Conclusion
Cerebras Systems has once again demonstrated its dedication to pushing the boundaries of ai inference technology. With a three-fold increase in inference speed and the ability to process 2,100 tokens per second with the Llama 3.1-70B model, Cerebras is setting a new benchmark for what is possible in ai hardware. By focusing on software and hardware optimizations, Cerebras is helping ai transcend the limits of what could previously be achieved, not only in speed but also in efficiency and scalability. This latest leap means more real-time intelligent applications, stronger ai reasoning, and a more fluid and interactive user experience. As we move forward, these types of advancements are critical to ensuring ai remains a transformative force across industries. With Cerebras at the helm, the future of ai inference looks faster, smarter, and more promising than ever.
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