Groq vs Together AI

Hardware Specialization Returns For AI Inference Era

The a16z newsletter article revisits the historic debate between general-purpose and specialized chips, arguing that the pendulum has swung back in favor of specialized hardware for AI inference. It details how general-purpose CPUs won in the 1990s due to volume-driven performance gains, but shifts to GPUs and the end of Dennard scaling changed the landscape. The article highlights that specialized chips like Nvidia's LPU (formerly Groq), Cerebras, and d-Matrix are now complementary to GPUs, delivering better performance per watt for heterogeneous inference workloads. It reports that GPUs have surpassed CPUs in revenue, and that inference demand is expected to grow 24x while power availability only doubles by 2030. The author, from Gimlet, advocates for orchestrating diverse accelerators to maximize efficiency.

• GPUs generated more revenue than CPUs for chip companies.
• Nvidia acquired Groq, now known as Nvidia's LPU.
• Inference demand is projected to increase 24x from 2026 to 2030, while power availability only doubles.

Nvidia's $20B Groq Deal Faces Stockholder Lawsuit

A lawsuit filed by former Groq engineers Joshua Rubin and Benjamin Serebrin alleges that Nvidia's $20 billion deal with Groq "squeezed out" stockholders. The complaint, filed on October 2 at the Delaware Court of Chancery, claims the deal offered a "lowball" price and that Groq's board approved the transaction without a required stockholder vote. The lawsuit argues that a conflicted board cost stockholders billions of dollars, pointing to $17 billion allocated to a non-exclusive license and $3 billion in Nvidia RSUs for employees. Groq dismissed the lawsuit as "meritless," stating the licensing agreement with Nvidia delivered exceptional value. Nvidia has not commented. The deal, announced in December, involved a licensing agreement for Groq's inference technology, with CEO Jonathan Ross and other leaders joining Nvidia, but Groq remains an independent company.

• Former Groq engineers Joshua Rubin and Benjamin Serebrin filed a lawsuit on Oct. 2 alleging Nvidia's $20B deal with Groq shortchanged stockholders.
• The lawsuit claims Groq's board approved the transaction without a required stockholder vote and that the deal cost stockholders 'billions of dollars.'
• The deal allocated $17 billion for a non-exclusive license and $3 billion in Nvidia RSUs for moving employees.

Groq Founder: Poor Leadership Cost Company Years

Jonathan Ross, founder of AI chip maker Groq, admitted in a podcast that his management failures initially cost the company three to four years. A former Google engineer, Ross founded Groq in 2016 to develop Language Processing Units (LPUs) as alternatives to Nvidia GPUs. He acknowledged being 'a terrible leader' at the start, citing mistakes in hiring employees who were not self-sufficient and giving them too much responsibility, leading to stagnation. He learned to focus on people management and became more selective in hiring. In December 2025, Nvidia signed a $20 billion licensing and talent deal with Groq, after which Ross and other key engineers moved to Nvidia, where Ross now serves as chief architect for software. Groq continues as an independent company led by Adam Winter. Similar leadership challenges were shared by Figma CEO Dylan Field and Duolingo CEO Luis von Ahn.

• Jonathan Ross founded Groq in 2016 to develop AI inference chips (LPUs) as alternatives to Nvidia GPUs.
• Ross admitted that his poor leadership set back Groq by three to four years.
• In December 2025, Nvidia signed a $20 billion license and talent deal with Groq.

Introducing Together Link: Run open models inside the harness you already use

Together AI announces Together Link, a new product that allows running open models within existing harnesses, alongside new research blog posts on ThunderKittens for NVIDIA Vera Rubin NVL72, ThunderAgent, and ICML 2026 participation.

Open or Closed AI: Founders' Choices at TechCrunch Disrupt 2026

The article previews upcoming discussions at TechCrunch Disrupt 2026 (Oct 13-15, San Francisco) on AI model strategy. It highlights how startups increasingly use multiple models—open and proprietary—for different tasks, weighing cost, performance, and flexibility. Sessions feature leaders from Together AI, Pathway, Oumi, Nvidia, and Ricursive Intelligence. Topics include whether to build, customize, or rent AI models, optimizing AI hardware (chip design), and trade-offs between frontier APIs (like OpenAI, Google) and open-weight models. The article emphasizes that model choice shapes product differentiation, infrastructure needs, and long-term business viability. It's a conference promotion with substantive insights on AI architecture decisions.

• TechCrunch Disrupt 2026 takes place Oct 13-15 at Moscone West, San Francisco.
• The conference features 200+ sessions, 250+ speakers, and 300+ exhibiting startups.
• Sessions cover multi-model strategies, custom model development, and AI-designed hardware.

Together AI Product Team Shares AI-Native Workflow Stack

Together AI's product team, including CPO Charles Zedlewski and PMs Necoline Hubner and Pavneet Ahluwalia, outlined their AI-native product development stack in a podcast. The stack comprises four components: a shared context repository accessible by any AI harness, skills for feature research and PRD writing, an orchestrator for leadership, and agent evals. The team emphasizes collective productivity over individual gains, using multiple models like GLM and Kimi instead of relying solely on Anthropic. They validate products through agent-based tests, checking if AI agents can complete tasks using their documentation and API. The article also highlights Together AI's $8.3 billion valuation from its Series C round.

• Together AI is valued at $8.3 billion.
• Together AI's product team uses a four-component AI-native stack: shared context repo, skills, orchestrator, and agent evals.
• The team uses multiple AI models (e.g., GLM, Kimi) rather than only Anthropic's models.