Groq is said to be considering using 3D stacked DRAM in next-gen chip architectures, in a possible shift away from its current SRAM-based designs as SRAM capacity is reportedly increasing just 10-20% every two years. $NVDA $MU $AMD

Groq has raised $350 million in funding at a $3.5 billion valuation, roughly half what it was worth nearly a year ago before Nvidia struck a licensing deal with the startup and hired away much of its talent

Groq is now an @nvidia Cloud Partner. A milestone for the team and validation of what our customers already experience: Groq runs AI infrastructure to the highest standard. "Inference is becoming the largest and most critical layer of AI, and we intend to run it better than anyone." — Adam Winter, CEO of Groq

Groq has entered into a non-exclusive licensing agreement with Nvidia for Groq’s inference technology. GroqCloud will continue to operate without interruption. Learn more here:

$NVDA GROQ 3 LPX ENTERS FULL PRODUCTION NVIDIA is extending Vera Rubin NVL72 with Groq 3 LPX, a purpose-built accelerator for ultrafast token generation in agentic AI. Nebius $NBIS will be the first AI cloud to adopt it, with Groq also among the earliest planned adopters.

$NVDA GROQ 3 LPX ENTERS FULL PRODUCTION NVIDIA says Groq 3 LPX, its low-latency inference accelerator designed to extend Vera Rubin NVL72, is now in full production. In Artificial Analysis testing, Groq 3 LPX reached 3,400 output tokens/sec running Gemma 4 31B with a 100K-token context, with NVIDIA claiming 4x faster responsiveness than the nearest alternative for latency-sensitive agentic workloads. The architecture splits inference work between Rubin GPUs for large-scale context processing and LPX for fast token generation, targeting coding agents, multi-step reasoning and tool-use workloads. Nebius will be the first AI cloud to deploy Groq 3 LPX through Nebius Token Factory, with Groq also expected to be among the earliest adopters.

#NVIDIA-linked# #Groq# is reportedly exploring 3D-stacked DRAM for its next-gen AI chips, potentially moving beyond #SRAM# as AI models grow larger. Could this reshape AI inference chip design? 💡More: 🔗