As AI workloads become increasingly complex, a new front in the artificial intelligence arms race has emerged: AI inference. While training large models like GPT-4 requires enormous compute power, it is inference, the real-time application of these models, that determines performance at scale in real-world environments. The demands of inference have driven the development of custom silicon solutions, as both startups and tech giants strive to optimize speed, efficiency, and cost.

The Custom Silicon Surge

Traditional CPUs and GPUs, though powerful, are not optimized for the specific needs of AI inference workloads, which prioritize low latency and energy efficiency over raw training throughput. This has sparked a wave of innovation in purpose-built AI accelerators.

Companies like Groq, SambaNova Systems, and Syntiant are developing specialized chips designed to outperform general-purpose processors. Groq, for instance, has introduced a deterministic architecture capable of delivering consistent low-latency inference, targeting enterprise and edge applications. Meanwhile, SambaNova’s reconfigurable dataflow architecture offers flexibility and efficiency across various AI tasks.

Big Tech Joins the Fray

Tech giants are also heavily invested in custom AI hardware. Google‘s Tensor Processing Units (TPUs) have been a cornerstone of its AI infrastructure since 2015, now powering services like Search, Translate, and Bard. The TPU v5e, launched in 2023, is engineered specifically for scalable AI inference, offering up to a 2.3x increase in inference performance per dollar over its predecessor.

Amazon is equally committed, with its Inferentia2 chips delivering up to 4x higher throughput and 10x lower latency compared to their first-generation models. These chips power Amazon Bedrock and AWS AI services, significantly reducing operating costs for inference at scale.

Microsoft and Meta are following suit with their in-house silicon efforts, highlighting the strategic value of owning the full AI stack, from model development to hardware deployment.

The Model Makers: OpenAI, Anthropic, and Beyond

As large language models evolve, so do the infrastructure demands behind them. OpenAI, Anthropic, and others are pushing the envelope of what’s possible with generative AI. While not silicon designers themselves, they influence the hardware landscape significantly through their architectural choices and performance requirements.

OpenAI’s GPT models, for instance, have served as benchmarks for AI chip optimization. Anthropic, with its Claude family of models, has similarly contributed to shaping AI inference needs. In fact, Anthropic recently cited the high cost of inference as a key driver for pursuing more efficient model architectures and potentially supporting future hardware innovation.

Industry Growth and Investment

The AI hardware market is booming. According to a 2024 report from Omdia, global revenue for AI-specific silicon is projected to reach $71 billion by 2027, up from $28 billion in 2023. This surge is driven largely by the growing demand for inference capabilities in edge devices, autonomous systems, and enterprise applications.

Notably, Syntiant is focusing on ultra-low-power inference chips tailored for wearables and IoT, where battery life is paramount. Their neural decision processors (NDPs) enable voice and sensor-based AI directly on-device, bypassing the need for cloud-based inference.

Implications for Talent and Leadership

This custom silicon renaissance isn’t just a technological shift, it’s a leadership challenge. As startups scale and Big Tech deepens its hardware-software integration, the industry is witnessing a critical need for executive talent with hybrid expertise across AI architecture, semiconductor design, and systems engineering.

At SLG Partners, we work with emerging leaders and seasoned executives driving this intersection of innovation. Whether it’s placing a VP of Silicon Engineering at a high-growth AI startup or sourcing a CTO with deep inference deployment experience, the demand for visionary leadership in this space has never been greater.

Conclusion

The future of AI is being defined not only by model breakthroughs but by the hardware that enables those models to run effectively at scale. With AI inference taking center stage, custom silicon has become a strategic asset. As innovation accelerates, the need for world-class leadership to navigate this fast-evolving domain will remain paramount.

Looking to hire leaders in AI hardware or inference at scale? Get in touch with SLG Partners to explore how we help companies build high-performance teams at the cutting edge of technology.