Fluents.ai | Fluents + Groq: Fastest LLM Inference for Real-Time Voice AI

When sub-100ms LLM response is the goal, Groq's dedicated inference hardware makes it possible.

Voice AI is a real-time medium. Every millisecond of LLM inference time is a millisecond of silence between what the caller says and what the agent responds. Groq's Language Processing Units (LPUs) deliver LLM inference 10-50x faster than GPU-based systems — making it the fastest available conversation engine for Fluents deployments where response latency is the primary optimization target.

For high-frequency trading firms, emergency services applications, or any use case where conversational flow must feel instant, Groq is the choice.

Groq's LPU hardware delivers LLM inference at speeds 10-50x faster than GPU-based systems — reducing the pause between caller and agent to near-imperceptible levels

Consistent low latency at scale — Groq's hardware architecture avoids the variable latency spikes common with GPU inference under load

Available as the conversation engine for Fluents deployments where conversational flow quality is the top optimization target

![](/images/68c1a5e95a5a8f4282be60bc-n How LPUs Change the Latency Equation

Traditional GPU-based LLM inference involves significant overhead from memory bandwidth constraints and batch scheduling. Groq's LPU architecture is purpose-built for sequential token generation — the exact workload of a real-time conversation. The result is consistent sub-100ms inference for many model sizes, compared to 300-800ms typical for GPU inference.

High-Volume Call Centers: Naturalness at Scale

A contact center running thousands of simultaneous Fluents calls needs each call to feel like a natural conversation. With GPU inference, latency can spike when infrastructure is under load. Groq's hardware delivers consistent sub-100ms response regardless of concurrent call volume — so the 1,000th simultaneous call feels as natural as the first.

Urgent Use Cases: Medical Triage, Emergency Dispatch

Not all AI calls are routine. Medical triage lines, emergency callback systems, and crisis intervention applications require agents that respond instantly. A noticeable pause before the agent speaks is unacceptable when a patient is describing chest pain. Groq's latency profile makes Fluents suitable for time-critical communication contexts that other LLM configurations can't meet.

Calls That Just Work

No per-minute taxes. No brittle workflows. Just enterprise-grade reliability with API-level flexibility.

![Fluents.ai AI platform dashboard interface screenshot](/images/68c1a5e95a5a8f4282be60bc-nCustomer Support

Fluents + Keragon 

Automate Patient Communication with Fluents Voice AI The Fluents connector for Keragon bridges the gap between your healthcare data and action. By integrating Fluents' powerful Voice AI directly into your Keragon workflows, you can automatically trigger outbound phone calls to patients or staff based on real-time events.

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MailerLite

Third-party

Fluents + MailerLite empowers real-time voice integration into your email campaigns, enhancing orchestration and maintaining compliance across channels.

BotPenguin

Third-party

Fluents + BotPenguin empower real campaigns with seamless integration, compliance assurance, and enhanced communication orchestration.

“Fluents made it incredibly fast to get our AI agent live. It replaced an answering service that cost 5x more - and performed better. Trusted partner, excellent quality, zero hassle.”

![Business professional photo](/images/68c1a5e95a5a8f4282be60bc-nAlvin Ramin

Premier AI Advisors, Partner

![Premier AI Advisors logo](/images/68c1a5e95a5a8f4282be60bc-n FAQs

Questions about using Groq in Fluents.

What models does Groq support for Fluents?

Groq runs fast inference on open-weight models including Llama and Mixtral. The available model selection via Groq is narrower than full frontier models like Gemini or GPT-4, but the inference speed is significantly faster. The team can advise on which Groq-hosted model performs best for your specific Fluents use case.

Does Groq's speed come at a quality trade-off?

Groq runs existing open-weight models faster — it doesn't change the models themselves. So the quality ceiling is the quality of the best open-weight models available on Groq (currently Llama 3.1 and Mixtral). For most structured voice AI tasks — intake, qualification, reminders — these models perform at or near frontier quality. For highly complex reasoning tasks, Gemini remains the better choice.

How do I enable Groq as my Fluents conversation engine?

Groq configuration is an enterprise feature. Contact the Fluents team to discuss whether Groq is the right fit for your latency requirements and use case.