ConclusiveEXP-04iones labUpdated on 20 September 2026
Groq versus GPU runtimes: when speed really changes the product
The questionDoes Groq’s inference speed justify integrating it into our products, and for which uses?
Verdict
Yes for anything interactive and short: classification, autocompletion, input assistants. No for long generation and reasoning, where the quality of the available models matters more than speed.
DecisionRetained for real-time uses. GPU runtime kept for complex generation.
- Status
- Conclusive
- Duration
- 2 weeks
- Team
- 1 developer, 1 architect
- Stack
- Groq
- Llama 3.3 70B
- vLLM
- Claude
- k6
- Grafana
0.21 stime to first tokenagainst 0.9 s on a GPU runtime
×5.6throughput in tokens per second480 against 85
−4 ptsquality on reasoning taskson our grid of 120 cases
2,000requests replayedfrom anonymised production logs
01
Context
On several client projects, the most requested AI feature is not the smartest one, it is the most responsive one: suggesting a category while the user types, rephrasing a title, completing a field. A response in two seconds breaks the flow; a response in 200 milliseconds extends it. Groq claims inference throughput far above GPU runtimes. We wanted to know whether that holds in real conditions, and what it cost in quality.
02
What we tested
We replayed 2,000 requests taken from anonymised logs of three production features (request classification, record summary, writing assistant), on three targets:
- Groq with Llama 3.3 70B.
- The same Llama 3.3 70B model served by vLLM on a dedicated GPU, our reference configuration.
- A top-tier proprietary model through an API, as a benchmark for maximum quality.
For each request, we measured time to first token, throughput and cost, and had two developers score response quality blind on a grid of 120 representative cases. The load was ramped up gradually with k6 to 40 simultaneous requests.
03
Results
- 0.21 s
- first token on Groq
- 0.9 s
- first token on vLLM GPU
- 480
- tokens / s on Groq
- 85
- tokens / s on vLLM GPU
The speed is real and stable. Time to first token stays under 300 ms up to 40 simultaneous requests, whereas our GPU runtime exceeds 2 s from 15 onwards. For classification, the complete response arrives in under 400 ms: users perceive it as instantaneous.
Quality is identical on short tasks and degrades on long ones. Same model, same answers on classification and summarisation (difference not significant). On the writing assistant and on multi-step reasoning cases, the proprietary benchmark keeps a 4-point lead on our grid, and speed will not make up for that.
- Groq’s model catalogue is limited: no top-tier model for long reasoning at the time of the test.
- Cost per classification request: about three times lower than the dedicated GPU once the infrastructure is amortised, at equivalent volume.
- Streaming output becomes pointless on Groq for short responses: the full text arrives before the animation is noticeable.
- Two rate-limiting periods observed over two weeks, which makes a fallback mechanism necessary.
04
What we conclude
Speed is not a comfort improvement, it is a new category of uses. At 200 ms, you can put a model in a typing loop, in a search filter, in a form, without the user being aware of waiting. At 2 s, you cannot. These are features we would not have offered before.
On the other hand, for anything that demands depth (long-form writing, analysis, code), model quality remains the criterion, and a user will accept waiting a few seconds for a correct answer. The right design is not “a single model”, it is to route each use to the appropriate runtime.
05
What’s next
- A routing layer has been added to our AI foundation: classification, suggestion and autocompletion to Groq, long generation to the GPU runtime or the proprietary API.
- Automatic fallback to the GPU in case of rate limiting, tested in real conditions.
- Next measurement: the real impact on the usage rate of a suggestion feature that went from 1.8 s to 0.3 s, at a volunteer client.