Groq

Company Overview

Groq builds custom Language Processing Units (LPUs) — purpose-built silicon designed from the ground up for AI inference.^[1] Unlike GPU-based platforms that repurpose graphics hardware for AI, Groq's LPU architecture is optimized for the sequential nature of language model inference, delivering dramatically faster token generation.

Founded by Jonathan Ross (who invented Google's TPU), Groq has become synonymous with "fast inference" in the developer community, often serving as the speed benchmark others measure against.

What It Does

• LLM inference API — OpenAI-compatible endpoints for popular open-source models^[2]
• Deterministic latency — Consistent response times without batching delays
• Free tier — Generous free usage for development and prototyping
• Production API — Pay-per-token for production workloads

How It Works

The LPU (Language Processing Unit) differs fundamentally from GPUs:

• No batching — Each request gets dedicated compute; no waiting for batch formation
• Deterministic — Same input produces same latency every time
• Sequential optimization — Architecture designed for autoregressive token generation
• SRAM-based — On-chip memory eliminates external memory bottlenecks

The result: token generation speeds that are often 10x+ faster than GPU-based platforms, with consistent low latency.

Pricing

• Free tier — Generous rate limits for development
• Pay-per-token — Production pricing by model
• No GPU management — Fully managed API

Groq's per-token pricing is competitive with GPU-based alternatives despite the speed advantage, making it attractive for latency-sensitive applications.

Strengths

• Fastest inference — Consistently benchmarks as the fastest LLM API
• Deterministic latency — No variance from batching or contention
• Generous free tier — Low barrier to adoption
• OpenAI-compatible — Drop-in replacement for existing code
• Developer beloved — Strong community adoption and mindshare
• TPU creator pedigree — Founder designed Google's TPU

Weaknesses / Risks

• Limited model selection — Only supported open-source models; no custom deployment
• No fine-tuning — Can't train custom models on Groq hardware
• Hardware supply — Custom silicon means limited scale vs commodity GPUs
• Model lag — New models take time to be optimized for LPU architecture
• No image/video — Focused on text/language models
• Single vendor risk — Entirely dependent on Groq's custom hardware roadmap

Competitive Landscape

vs. Cerebras: Both use custom silicon. Cerebras focuses on the largest single-chip approach; Groq on deterministic latency at scale.

vs. Together AI/Fireworks: GPU-based platforms offer more models and fine-tuning. Groq wins purely on speed.

vs. DeepInfra: DeepInfra may be cheaper per-token. Groq is significantly faster.

vs. SambaNova: Both custom silicon. SambaNova targets enterprise on-prem; Groq targets cloud API developers.

Ideal User

• Applications where latency is the #1 priority (real-time chat, voice, gaming)
• Developers prototyping with the free tier
• Teams wanting the fastest possible open-source model inference
• Products where consistent response time matters more than model customization

Bottom Line

Groq is the speed king of AI inference. Custom LPU hardware delivers a genuine architectural advantage that GPU-based platforms can't easily replicate. The trade-off is flexibility: you're limited to supported models with no custom deployment or fine-tuning. For latency-sensitive applications using popular open-source models, Groq is hard to beat. For everything else, GPU platforms offer more versatility.

---