Groq vs vLLM

Side-by-side comparison of Groq and vLLM: pricing, features, API access, and community ratings.

AI comparison summary

TL;DR: Groq is a hosted inference service built on proprietary LPU hardware optimizing for raw speed and low latency, while vLLM is an open-source serving engine you deploy yourself that maximizes throughput efficiency through advanced memory management (PagedAttention) and supports virtually any hardware platform. Choose Groq for instant access to extremely fast inference without infrastructure management, or vLLM if you need self-hosted deployment, want fine-grained control over optimization techniques like quantization and distributed inference, or require flexibility across different hardware vendors beyond NVIDIA/LPU.

Groq
Groq

Fast, low-cost AI inference powered by custom LPU silicon

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vLLM
vLLM

Easy, fast, and cheap LLM serving for everyone

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Category
AI infrastructure
AI infrastructure
Pricing
Freemium
Freemium
Starting price
Free tier
Audience
All audiences
All audiences
API available
Open source
Self-hostable
Model provider
Groq (third-party models hosted on LPU infrastructure)
Founded
2016
2023
Headquarters
San Jose, USA
Berkeley, United States
Key strengths
  • ·Fast AI inference
  • ·LPU (Language Processing Unit) hardware
  • ·LPX alongside NVIDIA GPUs
  • ·Scalable inference at low cost
  • ·State-of-the-art serving throughput
  • ·PagedAttention for efficient KV memory management
  • ·Continuous batching and chunked prefill
  • ·Broad quantization support (FP8, INT8, INT4, GPTQ/AWQ, GGUF, etc.)