Groq
LPU inference — 500+ tokens/sec with custom silicon.
About
Groq (2016, AI platform 2024) runs LLMs on custom LPU silicon: 500+ tokens/second on Llama models — an order of magnitude faster than GPUs. The free tier made it famous.
Commercial: usage-based; generous free tier. The speed demo that went viral — for real-time applications, Groq's throughput is unmatched. The LPU architecture is genuinely different (deterministic, low-latency). For bulk inference and real-time agents, Groq is the throughput king.
What it can do
- • 500+ tokens/sec on LPUs\n• Custom silicon (deterministic)\n• Generous free tier\n• OpenAI-compatible API\n• Real-time optimized\n• Low-latency inference
Setup
- Sign up at https://groq.com and get an API key.\n2. Use the OpenAI-compatible endpoint:
https://api.groq.com/openai/v1.\n3. Select LPU-optimized models.
Prerequisites
A Groq account; (free tier available).
Use cases
Works with
Trust score — 7/10
Scored 7/10 (metrics tier). Unmatched throughput (500+ tok/s) on custom silicon with a generous free tier. Docked three points: (1) limited model selection (LPU-optimized only); (2) startup risk on custom hardware; (3) less enterprise polish. For speed-critical inference.
How the trust score is built
Full methodology →- Adoption · stars/downloads, log-scaled vs peers
- 9.0/10
- Maintenance · recency, releases, issue load
- 5.0/10
- Openness · license + docs
- 4.5/10
- Manual review · sanity read + red-flag check
- 8.0/10
- · provider-tier estimate — thinner evidence (no public adoption metric)
- · 500+ tok/s on LPUs; viral speed demo
- · no release date — neutral maintenance score
- · manual review: custom silicon risk; limited models; license: proprietary — usage-based; generous free tier
Sources
Every fact above was checked against these sources. Where a claim comes from the provider and couldn't be independently verified, the text says so.