Custom Silicon

Nvidia's strategic $3.5 billion partnership with MediaTek signals a major shift toward edge AI and ARM-based custom silicon, defending its market position against Big Tech's in-house AI chips.

Alphabet is reportedly engineering a specialized AI chip aimed at drastically reducing the latency and cost of running Gemini models, marking a significant shift in the AI infrastructure landscape.

Following OpenAI's lead, Anthropic is exploring custom silicon with Samsung to reduce reliance on NVIDIA and optimize Claude's performance.

As the demand for specialized AI hardware skyrockets, industry giants like OpenAI and SpaceX are moving away from Nvidia's monopoly to build their own custom silicon, signaling a major shift in the LLM infrastructure landscape.

OpenAI's strategic shift toward custom silicon with the Jalapeño chip marks a turning point in the AI hardware landscape, reducing dependence on Nvidia and optimizing for large-scale inference.

OpenAI has officially entered the hardware arena with Jalapeño, a custom-designed ASIC developed in collaboration with Broadcom to optimize AI inference and reduce dependency on external silicon providers.

OpenAI partners with Broadcom to develop 'Jalapeño', a custom ASIC designed specifically to accelerate LLM inference and reduce operational costs.

Arm breaks its 35-year licensing-only model to produce a custom AI CPU in collaboration with Meta, signaling a major shift in the semiconductor landscape and AI infrastructure.

An in-depth technical exploration of Amazon's Trainium chip architecture, its strategic role in the $50 billion OpenAI partnership, and why industry giants like Apple and Anthropic are migrating away from NVIDIA dominance.

OpenAI's latest GPT-5.3-Codex-Spark model achieves a 15x speed increase in code generation by utilizing specialized, plate-sized custom chips, signaling a major shift away from Nvidia dependency.

Microsoft has introduced its latest custom AI silicon, the Maia chip, featuring 100 billion transistors and specialized 4-bit precision performance to drastically reduce LLM inference costs and latency.