d-Matrix Corsair - In-Memory Inference Accelerator
d-Matrix Corsair is an SRAM-based in-memory compute ASIC in production since June 2026, targeting 10x faster and 5x more power-efficient LLM inference vs GPU baselines.
Articles Tagged "AI Accelerator"
Cerebras Grew 92% - Investors Saw the Margins and Sold
Cerebras beat Q1 revenue estimates with 92% growth but a dramatic margin guidance cut sent shares down 20%, erasing nearly all gains since its landmark semiconductor IPO.
AMD Instinct MI450 - 2nm, 432 GB HBM4, 40 PFLOPS
AMD's CDNA 5 accelerator on TSMC 2nm with 432 GB HBM4 memory - the GPU behind OpenAI's 1GW deployment and Oracle's 50,000-chip supercluster.
AMD Instinct MI350P - CDNA 4 PCIe Inference Card
AMD Instinct MI350P brings CDNA 4 to a standard PCIe slot: 144GB HBM3E, 4 TB/s bandwidth, 2.3 PFLOPS MXFP8, and 600W passive cooling for air-cooled servers.
NVIDIA RTX Spark - ARM Blackwell Superchip for AI PCs
NVIDIA RTX Spark is a 20-core ARM + Blackwell GPU superchip delivering 1 petaFLOP FP4 and 128GB unified memory for AI-first Windows laptops and desktops.
Lumai Iris Nova: Optical AI Inference Server
Lumai's optical AI inference server uses light-based computing to run billion-parameter LLMs with up to 90% less power than GPUs.
Skymizer HTX301: 700B LLMs at 240W on PCIe
Skymizer's HTX301 uses six 28nm chips and 384 GB LPDDR5 to run 700B-parameter LLMs on a single PCIe card at just 240W.
Meta MTIA 450: 18.4 TB/s Inference Accelerator
Meta's MTIA 450 doubles HBM bandwidth to 18.4 TB/s and adds FlashAttention hardware acceleration for GenAI inference in 2027.
AMD Helios: 72-GPU Rack for AI at Scale
AMD Helios packs 72 Instinct MI455X GPUs and 31 TB HBM4 into a single rack delivering 2.9 FP4 ExaFLOPS for AI workloads.
Meta MTIA 400: GenAI Inference at Scale
Meta's second-gen ASIC delivers 6 PFLOPS FP8 and 288 GB HBM for GenAI and recommendation inference inside Meta's data centers.
Google TPU 8i - Low-Latency Inference for Agent Era
Google's TPU 8i is a purpose-built inference chip with 10.1 FP4 PFLOPs, 288GB HBM3e at 8,601 GB/s, and a Boardfly topology that cuts collective latency 5x for agentic AI workloads.
Google TPU 8t - AI Training at ExaFLOP Scale
Google's TPU 8t packs 12.6 FP4 PFLOPs and 216GB HBM3e per chip, scaling to 9,600-chip superpods with 121 ExaFLOPS and 2 petabytes of shared HBM for massive model training.
