Reports
AI-generated structured vendor updates
Anthropic Starts Custom AI Chip Development, Talks Samsung 2nm, Aims for Compute Independence
Anthropic has initiated its own AI chip development and is in talks with Samsung for 2nm foundry services. The move aims to reduce reliance on NVIDIA GPUs, optimize inference costs, and strengthen its technology moat ahead of a potential IPO. It joins OpenAI, Google, and others in the custom ASIC race, signaling a shift from software to hardware competition.
OpenAI Slashes Inference Costs 50%, Runs ChatGPT on Hundreds of GPUs via System-Level Optimization
OpenAI reduces AI inference costs by over 50% through system-level optimizations: model quantization (FP16 to INT4/INT8), KV-Cache optimization, dynamic batching, and speculative decoding. Using only hundreds of NVIDIA GPUs to serve ChatGPT's unlogged-in traffic, inference gross margin jumps from 38% to 65%, nearing breakeven.
Etched Unveils Sohu Transformer ASIC: Claims 20x H100 Inference Throughput, Challenging NVIDIA's Grip
AI chip startup Etched emerges from stealth with Sohu, a Transformer-specific ASIC on TSMC N4P with 144GB HBM3E. By hardwiring attention mechanisms, it claims 20x throughput and 140x price-performance vs. H100 on Llama 70B. With $800M total funding and first racks shipping this summer, it directly challenges NVIDIA's inference dominance.
OpenAI and Broadcom launch Jalapeño inference ASIC: 9-month tapeout, 2027 mass production, targets GPU replacement
OpenAI and Broadcom unveil Jalapeño, a custom inference ASIC designed in 9 months using OpenAI's own LLMs. Early benchmarks show superior performance-per-watt vs. current GPUs. Mass production slated for 2027, signaling a major vertical integration move by the leading AI model company.
OpenAI and Broadcom Tape Out First Inference ASIC Jalapeño in 9 Months, Targeting NVIDIA Dominance
OpenAI and Broadcom unveil Jalapeño, their first custom inference ASIC, fabricated on TSMC 3nm and optimized for Transformer models. Targeting a 50% inference cost reduction, it taped out in 9 months and is slated for deployment in gigawatt-scale data centers by late 2026, marking OpenAI's strategic pivot to full-stack AI infrastructure and a direct challenge to NVIDIA's inference hegemony.
OpenAI and Broadcom unveil Jalapeño inference ASIC to bypass NVIDIA GPU dependency
OpenAI and Broadcom launch Jalapeño, a custom ASIC for LLM inference, achieving tape-out in 9 months. OpenAI designs architecture, Broadcom provides networking, Celestica handles integration. Planned for large-scale deployment by end-2026 with gigawatt-scale datacenters, aiming to cut inference costs and reduce NVIDIA dependency.
OpenAI and Broadcom Unveil Jalapeno Inference ASIC, Reshaping AI Hardware Landscape
OpenAI, in collaboration with Broadcom, has developed Jalapeno, a custom LLM inference accelerator. The chip uses a multi-chip module with HBM3E memory and achieved tape-out in just nine months. Designed for OpenAI's model stack, it aims to reduce inference costs and dependency on NVIDIA GPUs, with initial deployment planned for late 2026.
OpenAI and Broadcom Launch Jalapeño ASIC for LLM Inference, 9-Month Tapeout
OpenAI and Broadcom unveil Jalapeño, a custom ASIC for LLM inference, achieving tapeout in 9 months. The chip reduces data movement and claims superior performance per watt. Deployment planned by end of 2026, marking OpenAI's shift to integrated hardware-software infrastructure.