Groq closes $350 million Series A led by Disruptive, with NVIDIA participation, at $3.5 billion valuation

Groq announced on August 17, 2026 that it closed a $350 million Series A funding round led by Disruptive, with planned participation from NVIDIA, valuing the AI inference infrastructure company at $3.5 billion. Combined with the $650 million it raised in June 2026, Groq has now brought in $1 billion in funding over roughly two months.

What's new

• A $350 million Series A led by Disruptive, a global tech investment firm, with NVIDIA participating
• New valuation: $3.5 billion
• Total recent funding — this round plus June 2026's $650 million — now totals $1 billion
• Groq operates 13 data centers across North America, Europe, the Middle East, and Asia Pacific
• The company says it serves more than six million developers, Fortune 500 enterprises, and thousands of AI-native companies
• Capital will fund build-out of inference capacity; Groq plans to scale from 54 megawatts to more than 200 megawatts in 2027
• Alex Davis, Groq Executive Chairman and CEO of Disruptive: "We are building Groq into the world's leading AI inference cloud... Inference will without a doubt become the largest and most critical layer of AI infrastructure"
• The round remains subject to customary closing conditions

Context

Groq built its business on the LPU (Language Processing Unit), a custom chip architecture designed specifically for low-latency LLM inference, positioning itself as an alternative to running inference on general-purpose GPUs. Rather than compete head-on with NVIDIA, Groq has built a partnership around it: the company is a certified NVIDIA Cloud Partner, and NVIDIA is now both a technology supplier and, per this announcement, a backer of Groq's funding. This is Groq's second major raise in under three months, following the $650 million round it closed in June 2026.

Why it matters

The pace and size of Groq's fundraising — $1 billion inside a single quarter — reflects how acute the inference-capacity crunch has become as AI usage shifts from training frontier models to serving them at scale in production. NVIDIA's willingness to back an inference specialist it also sells chips to shows the company hedging across the compute stack: profiting from GPU sales while gaining direct exposure to inference-layer economics through investment. For enterprise AI buyers, a well-capitalized independent inference provider scaling capacity means more competition for the hyperscalers on price and latency for production LLM workloads.