Groq

Purpose-built LPU inference chip and GroqCloud API for fast, low-cost AI.

Mountain View, CA, USA · Founded 2016

Groq: LPU Inference Cloud, Funding & Founders (2026)

Founded 2016 by ex-Google TPU designer Jonathan Ross, Groq makes the LPU inference chip and runs GroqCloud, serving 5M+ developers with fast open-source AI.

Groq is the AI inference cloud for developers who need to run open-source language models at the lowest latency available in 2026. Founded in 2016 in Mountain View, California, the company built its own Language Processing Unit chip that stores model weights in on-chip memory rather than external RAM. Its 13 global data centers host Llama 4, DeepSeek R1, and other leading open-source models.

Groq, founded in 2016 and headquartered in Mountain View, California, designed the Language Processing Unit (LPU), a custom chip built for AI inference rather than training. The LPU delivers 300-plus tokens per second on Llama models, far faster than typical GPU deployments. GroqCloud, its developer platform, serves more than five million users running open-source models including Llama 4 and DeepSeek R1 via an OpenAI-compatible API.

Founded: 2016 · HQ: Mountain View, CA, USA · Team: 300-400 · CEO: Adam Winter · Funding: $2.4B total raised across 7 rounds from 49 investors (Sep 2025: $750M led by Disruptive, Blackrock, Samsung, Cisco, Altimeter; Jun 2026: $650M led by Disruptive and Infinitum; Feb 2025: $1.5B Saudi Arabia infrastructure commitment) · Valuation: $6.9B (September 2025 Series D)

About Groq

Groq was founded in 2016 in Mountain View, California by Jonathan Ross, one of the designers of Google's original Tensor Processing Unit (TPU), and Douglas Wightman, a former Google X engineer. They left Google after concluding that GPU architecture, built for training, was a poor match for inference, the stage where trained models answer real user queries. Their thesis: purpose-built silicon could eliminate the latency that slows GPU-based inference, making AI responses feel instantaneous. Groq's flagship product is the Language Processing Unit (LPU), a custom chip that stores model weights in on-chip SRAM rather than external high-bandwidth memory, eliminating the bandwidth bottleneck that limits GPU inference speed. GroqCloud, its cloud product, is a token-as-a-service API running third-party open-source models including Meta's Llama 4 and DeepSeek R1 on Groq's own hardware, accessed through an OpenAI-compatible interface with per-token pricing and a free developer tier. In December 2025, Nvidia announced a non-exclusive licensing agreement for Groq's LPU inference architecture valued at approximately $20 billion. Groq retained its intellectual property and kept operating GroqCloud independently, while Nvidia gained rights to produce chips based on Groq's architecture. Nvidia unveiled the resulting Groq 3 LPU at GTC 2026 in March: a Samsung 4-nanometer chip targeting 1,500 tokens per second for agentic AI, with 150 terabytes per second of on-chip memory bandwidth, shipping in late 2026. Groq has raised approximately $2.4 billion across seven rounds from 49 investors. Its September 2025 round raised $750 million at a $6.9 billion post-money valuation, led by Disruptive with Blackrock, Neuberger Berman, Samsung, Cisco, D1 Capital, Altimeter Capital, 1789 Capital, and Infinitum participating. In February 2025, Saudi Arabia committed $1.5 billion toward Groq's infrastructure, tied to a new data center in Dammam. In June 2026, Groq closed an additional $650 million led by Disruptive and Infinitum for GroqCloud buildout. Groq earns revenue through IP licensing and cloud inference services. The Nvidia deal validated the hardware IP path at scale, while GroqCloud charges per million tokens consumed, with free access for individual developers and dedicated capacity for enterprise customers with SLA guarantees. Latka reported Groq's annual recurring revenue at approximately $172.5 million in 2025. The 2026 strategy centers on becoming the leading AI inference cloud rather than selling hardware to third-party operators. Jonathan Ross left in December 2025 to join Nvidia as Chief Software Architect, with President Sunny Madra also moving to Nvidia as part of the deal. Adam Winter, formerly a Vice President at Groq, became CEO in late 2025 and is executing the pivot to an inference neocloud strategy. Claire Hart serves as Chief Operating Officer, Chief Legal Officer, and Board Member. As of mid-2026, Groq employs approximately 300 to 400 people, down from a peak of about 575 in late 2025, reflecting the leadership transition. Groq's stated mission is to preserve human agency while building the AI economy by driving the cost of AI computation toward zero. Its research focus is inference hardware architecture, compiler technology, and systems software, not foundation model development. The LPU's defining feature is deterministic execution: memory access patterns are fixed at compile time, cutting the cache misses and latency variance that affect GPU inference under variable load. Groq operates 13 data centers across North America, Europe, the Middle East, and Asia-Pacific, including Helsinki (Finland), Sydney (Australia), and Dammam (Saudi Arabia). It holds SOC 2 Type II certification and maintains a trust center at trust.groq.com. Main competitors include AWS Inferentia, Together AI, Fireworks AI, Cerebras Systems, and Nvidia's NIM inference service. Groq wins on raw token speed for streaming and real-time agent workloads but lacks fine-tuning and proprietary model capabilities enterprise buyers sometimes require. The June 2026 raise funds expansion to 200 megawatts of capacity by the end of 2027.

Mission

To preserve human agency while building the AI economy by driving the cost of AI computation toward zero.

Products

Compliance

SOC 2 Type II

Links

Website · GitHub · Twitter · LinkedIn · Blog · Docs

Frequently Asked Questions

What is Groq and what do they build?

Groq is an AI inference technology company founded in 2016 in Mountain View, California that designs the Language Processing Unit (LPU), a custom chip built specifically for AI inference rather than model training. The LPU stores model weights in on-chip SRAM rather than external memory, enabling token generation speeds that GPU-based cloud services cannot match at the same cost. Groq's cloud product, GroqCloud, runs third-party open-source models including Meta's Llama 4, DeepSeek R1, and Mistral models on its own LPU hardware, accessible via an OpenAI-compatible API. Developers can sign up at console.groq.com and start calling models within minutes using the same API format as OpenAI's SDK. In December 2025, Nvidia licensed Groq's LPU architecture for approximately $20 billion, establishing Groq's technology as one of the most commercially validated inference architectures in the industry. GroqCloud serves enterprise customers across financial services, technology, and media sectors, in addition to its five-million-plus developer community. As of 2026, Groq operates 13 data centers across North America, Europe, the Middle East, and Asia-Pacific.

Who founded Groq and who is the CEO?

Groq was co-founded in 2016 by Jonathan Ross and Douglas Wightman. Jonathan Ross was one of the designers of Google's Tensor Processing Unit (TPU), the AI accelerator chip Google built internally starting around 2013, and he led hardware architecture at Groq from founding through December 2025. Douglas Wightman was an entrepreneur and engineer who had worked at Google X before helping start the company. Ross served as CEO for the company's first seven years and reflected publicly in July 2026 that early management errors, including hiring engineers who needed direction and then giving them too much autonomy, cost the company three to four years of progress. In December 2025, Ross left to join Nvidia as Chief Software Architect following the LPU licensing deal, with President Sunny Madra also moving to Nvidia. Adam Winter, a former Vice President at Groq, became CEO in late 2025 and is leading the company's shift to an inference neocloud strategy. Claire Hart joined as Chief Operating Officer, Chief Legal Officer, and Board Member.

How much funding has Groq raised?

Groq has raised approximately $2.4 billion across seven rounds from 49 investors as of mid-2026. In September 2025, the company closed a $750 million round at a post-money valuation of $6.9 billion, led by Disruptive and backed by Blackrock, Neuberger Berman, DTCP, Samsung, Cisco, D1 Capital, Altimeter Capital, 1789 Capital, and Infinitum. In February 2025, the Kingdom of Saudi Arabia committed $1.5 billion to Groq's infrastructure expansion, tied to a new GroqCloud data center in Dammam, Saudi Arabia. In June 2026, Groq announced $650 million in new growth capital, again led by Disruptive and Infinitum, representing continued investor confidence after the Nvidia licensing deal. In December 2025, Nvidia announced a non-exclusive licensing agreement for Groq's LPU inference architecture valued at approximately $20 billion, the largest deal in Nvidia's history; this is an IP licensing transaction rather than equity investment. Latka reported Groq's annual recurring revenue at approximately $172.5 million from its cloud product in 2025. Groq is not currently public and has not announced IPO plans.

What products does Groq make?

Groq makes two core products: the Language Processing Unit (LPU) chip and GroqCloud, the API platform that runs AI models on LPU hardware. The LPU is a dedicated inference accelerator that stores model weights in on-chip SRAM, delivering hundreds of tokens per second on large language models at lower cost than GPU cloud alternatives for streaming and real-time workloads. GroqCloud is an OpenAI-compatible API that lets developers call open-source models including Meta's Llama 4, DeepSeek R1, Mistral, Gemma, and others without managing any hardware. Pricing is per million tokens consumed, with a free tier for developers and enterprise plans featuring dedicated capacity and SLA guarantees. Groq also offers the GroqCloud Console at console.groq.com, a web interface for testing models, checking usage, and managing API keys. The second-generation Groq 3 LPU, developed in partnership with Nvidia and manufactured on Samsung's 4nm node, targets 1,500 tokens per second for agentic AI and is expected to ship in late 2026. Groq does not build or train its own language models; all models on GroqCloud are third-party open-source models compiled to run on Groq hardware.

Where is Groq headquartered and how big is the team?

Groq is headquartered in Mountain View, California, with its engineering and business teams based primarily at that location. As of mid-2026, Groq employs approximately 300 to 400 people, down from a peak of around 575 in late 2025. The decrease reflects the December 2025 departure of founder Jonathan Ross, President Sunny Madra, and other members who joined Nvidia as part of the licensing deal. Groq has not publicly announced layoffs; the headcount shift appears to be primarily attrition and reorganization connected to the Nvidia transition and the company's strategic pivot to a pure-cloud model. Beyond its Mountain View headquarters, Groq has operational presence tied to its 13 global data centers across North America, Europe, the Middle East, and Asia-Pacific. The company opened its European data center in Helsinki, Finland in 2025 and its first Asia-Pacific site in Sydney, Australia. Groq lists open roles in cloud engineering, sales, and operations at groq.com/careers-at-groq.

What is Groq's mission or research focus?

Groq's stated mission is to preserve human agency while building the AI economy by driving the cost of AI computation toward zero. The company interprets this concretely as making AI inference so fast and inexpensive that the cost of a generated response becomes negligible for most applications. Unlike frontier labs such as Anthropic or OpenAI, Groq does not conduct research on model training, safety alignment, or foundational model capabilities; its technical work is entirely in inference hardware architecture, chip design, and compiler technology. The LPU's deterministic execution model, where memory access patterns are computed at compile time rather than at runtime, is the core architectural feature that distinguishes Groq from GPU-based inference. Groq publishes technical documentation on the LPU architecture and the GroqCloud API but does not produce academic safety or alignment research. The company does not have a responsible scaling policy or safety framework of its own, as it does not train the models it serves. Its governance commitment is to SOC 2 Type II compliance and to the data handling practices documented at trust.groq.com.

Is Groq compliant with SOC 2, GDPR, HIPAA?

Groq holds SOC 2 Type II certification, confirmed via its public announcement in mid-2024 and documented at its trust center at trust.groq.com. This certification covers security, availability, and confidentiality controls across Groq's cloud infrastructure, audited by an independent third party. For GDPR compliance, Groq's European data center in Helsinki, Finland means EU-based workloads can be directed to European infrastructure, and Groq provides privacy terms that address EU data protection obligations. Groq has not publicly confirmed ISO 27001 or HIPAA-eligible status as of mid-2026, so customers in regulated industries should verify current certification status directly at trust.groq.com before committing. The GroqCloud API processes user-submitted prompts and responses, and Groq's privacy policy states that this data is not used to train or fine-tune models. Data retention periods and enterprise zero-retention options are described in GroqCloud enterprise terms and should be confirmed with Groq's sales team for specific compliance requirements. Check the trust center for the most current list of certifications, as Groq's compliance posture may have expanded since its initial SOC 2 Type II announcement.

Who are Groq's main competitors?

Groq's main competitors in the AI inference cloud market are Together AI, Fireworks AI, AWS Inferentia via Amazon Bedrock, Nvidia's NIM inference service, and Cerebras Systems. Against Together AI and Fireworks AI, Groq's primary advantage is raw token generation speed on open-source models; Groq frequently appears in developer benchmarks as the fastest option for Llama and DeepSeek models, trading off a somewhat narrower model selection. Against AWS Inferentia and major cloud provider inference services, Groq wins on speed but loses on integration depth, enterprise support, and the ability to combine inference with cloud-native managed storage and vector databases. Cerebras Systems is the most architecturally similar rival, also building custom inference silicon with on-chip memory, and competes for the same developer segment that prioritizes latency above all else. Nvidia's NIM service, now incorporating Groq 3 LPU-derived chips, creates a unique dynamic where Groq's own architecture is available through a much larger distribution partner. Groq has no offering today for customers needing to fine-tune models, run proprietary models, or combine inference with cloud-native managed services. Tenstorrent is another emerging rival targeting low-latency inference silicon, representing the broader competitive threat from dedicated inference chip companies entering the market in 2025 and 2026.