Groq LPU (with GroqCloud)
Business Profile
Core Value Proposition
Groq provides fast, low-cost AI inference through its Groq LPU hardware and GroqCloud platform, delivering consistent performance at scale.
Target Customer
Developers, AI teams, and enterprises building or deploying AI/inference workloads; users of GroqCloud; teams evaluating model deployments at scale.
Key Differentiator
First chip purpose-built for inference (LPU) with a single-core, on-chip SRAM architecture, direct chip-to-chip connectivity, a purpose-built compiler for deterministic execution, air-cooled design, and on-prem as well as cloud deployments via GroqRack.
Implementation Timeline
Integration can be rapid, with examples noting compatibility and adoption in “two lines” of code for OpenAI-compatible access; GroqCloud emphasizes fast start with templates and APIs.
Pain Points
- 1High cost and unpredictable latency of AI inference, especially at scale
- 2Reliance on GPUs and non-deterministic performance in real-world workloads
- 3Difficulty achieving low-latency, scalable inference across large models and diverse modalities
Key Features
- 1Groq LPU: custom silicon designed specifically for inference
- 2GroqCloud: AI inference platform with public, private, and co-cloud options
- 3Deterministic execution via a custom compiler and static scheduling
- 4On-chip SRAM for primary weight storage and fast tensor parallelism
- 5On-prem option (GroqRack) for regulated environments; data center deployments; SOC 2, GDPR, HIPAA compliance
Target Customer Industries
Decision Makers
- 1CTO
- 2CEO
- 3VP of Engineering
- 4Senior AI/ML Architect
- 5Head of Data Science
Client Results
- 1Chat speed surged 7.41x with costs falling by 89% after adopting GroqCloud (PGA of America case context)
- 2Groq has enabled substantial savings and significantly reduced overhead for offerings in some customer deployments
- 3Towards lower and more predictable costs with linear pricing and no hidden charges
Competitive Advantages
- 1Deterministic, predictable performance at scale due to single-core, on-chip SRAM architecture and a purpose-built compiler
- 2Lower, predictable costs with linear pricing and no idle infrastructure
- 3OpenAI compatibility and broad model support (LLMs, STT, TTS, image-to-text) via GroqCloud; plus on-prem and cloud deployment options
Key Benefits
Case Studies & Success Stories
McLaren F1 Team selects Groq for inference
McLaren F1 Team relies on Groq for real-time decision-making, analysis, development, and insights generated by AI inference.
PGA of America uses GroqCloud to boost performance and cut costs
CTO Kevin Scott notes GroqCloud enabled breakthroughs in chat speed while reducing costs by a large margin, illustrating significant efficiency gains in their infrastructure.
Opennote and Fintool cite cost savings and efficiency gains
Abhigyan Arya (Opennote, CTO) and Nicolas Bustamante (Fintool, CEO) describe immense savings, reduced overhead, and lower costs enabling more accessible offerings for users, including students.
Additional Product Information
Product Description
Groq LPU is a purpose-built inference processor designed for fast, low-cost AI inference, complemented by GroqCloud, an inference platform that delivers scalable, predictable performance and model support.
Target Market
Developers and organizations deploying large-scale AI/inference workloads across data centers and regulatory environments; teams seeking predictable cost and latency.
Unique Value Proposition
Only custom-built inference chip designed for inference with deterministic, predictable performance, combined with a scalable cloud/on-prem platform and linear, transparent pricing.
Technical Requirements
{"On-prem deployment option (GroqRack) for regulated environments","Data center deployments in multiple global regions","Air-cooled hardware design; minimal cooling/infrastructure requirements","Support for industry-standard frameworks and integrations"}
Pricing Model
{"GroqCloud pricing with Free, Developer, and Enterprise plans","On-demand, token-based pricing for core models (e.g., GPT OSS, Kimi K2, Qwen3 32B, Llama variants, etc.) with per-token costs for input/output tokens","No hidden costs; linear, predictable pricing with billed usage","Batch processing available for asynchronous workloads; enterprise and on-prem options available"}
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