vLLM vs Groq
Both are free/open-source alternatives to OpenAI API (ChatGPT). Here's how they stack up — verified facts, no spin.
vLLM
High-throughput serving for production-grade local inference.
vLLM is a fast inference and serving engine built for throughput, using paged attention to serve many concurrent requests efficiently. It is the choice when a team needs to self-host models at real scale.
Groq
The speed king — open models at 500+ tokens/second on custom LPU chips.
Groq runs open models (Llama and friends) on its custom LPU hardware and is, as of mid-2026, the fastest mainstream inference API available — 500+ tokens per second, at prices mostly under $1 per million tokens (Llama 3.3 70B at $0.59/$0.79). If your product's bottleneck is latency — voice agents, live UX, rapid tool loops — Groq is the honest answer. It's a proprietary hosted platform, but like Together, the models themselves are open, so you're renting speed, not locking in your stack.
Side by side
| vLLM | Groq | |
|---|---|---|
| Sovereignty Score | 88 | 42 |
| Open source | Yes | No |
| Self-hostable | Yes | No |
| Local-first | Yes | No |
| License | Apache-2.0 | Proprietary (platform); serves open-weight models |
| Pricing | Free / self-host | Most models under $1 per 1M tokens; Llama 3.3 70B $0.59/$0.79; batch −50% |
vLLM edges it on the Sovereignty Score, but the right pick depends on the trade-offs below.
vLLM
Strengths
- +Excellent throughput under concurrency
- +OpenAI-compatible server mode
- +Backed by a large community
Trade-offs
- −Aimed at capable GPUs, not laptops
- −Steeper operational learning curve
Groq
Strengths
- +Fastest inference on the market (500+ tok/s)
- +Very low prices on open models
- +OpenAI-compatible API — near drop-in
Trade-offs
- −Hosted-only; custom hardware means no self-host path for the speed
- −Model catalog is narrower than Together's
More OpenAI API (ChatGPT) head-to-heads
Facts verified 2026-07-04. Licenses and pricing change — spotted something out of date? That's a correction we want.