Groq LPU for Voice Agents: 330ms End-to-End Pipeline (2026)

TL;DR — Groq's LPU has become the default LLM step for low-latency voice agents in 2026. Independent benchmarks: 80ms TTFT vs 200–500ms for GPU; Llama-3 70B at 800 tok/s vs 55 tok/s on A100; Whisper Large V3 at 189× real-time (1 minute of audio in 0.3s). Groq 3 LPU (announced GTC 2026): 1.2 PFLOPS, 500MB SRAM at 150TB/s, 1500 tok/s target. End-to-end voice pipeline: 330ms.

The voice latency math

Pipeline = STT (80ms) + LLM TTFT (80ms) + TTS TTFB (130ms) + jitter buffer (40ms) ≈ 330ms voice-to-voice. Same pipeline on H100: 880ms — users describe that as "laggy." Groq makes the LLM step structurally invisible.

Architecture

flowchart LR
 CALLER[SIP / Browser] --> SFU[WebRTC SFU]
 SFU -->|PCM| STT[Groq Whisper-V3 189x RT]
 STT -->|text| LLM[Groq Llama 3.3 70B 800 tok/s]
 LLM -->|stream| TTS[Cartesia / Aura-1]
 TTS -->|frames| SFU

CallSphere stack on Groq

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Build steps

1. pip install groq and export GROQ_API_KEY=....
2. Use the OpenAI-compatible chat-completions endpoint with model="llama-3.3-70b-versatile" and stream=True.
3. Stream output deltas straight into a TTS streaming endpoint (Cartesia Sonic, Aura-1, ElevenLabs Flash).
4. For STT: model="whisper-large-v3" via the audio transcriptions endpoint — chunk audio at 5s.
5. Set tool-calling on; Groq's structured-output throughput keeps function calls under 100ms.
6. Add a fallback to Cerebras or Together when Groq queue depth spikes (rare in 2026 but plan for it).

Pitfalls

• Context windows shorter than peers. 70B versatile = 32K context; trim history aggressively.
• Rate limits. Free tier hard-caps; production needs paid TPM allowance.
• Tool calling determinism. Llama 3.3 + Groq is fast but you must validate JSON; pass response_format={"type":"json_object"}.
• No native audio output. Groq does LLM and STT; TTS is on you.

FAQ

Q: Groq vs Cerebras for voice? A: For most voice apps either makes the LLM invisible. Groq has wider model availability (Llama, Kimi, DeepSeek); Cerebras has higher peak (2,500 tok/s on Maverick).

Q: Groq Cloud vs on-prem? A: Cloud is cheap and fast. On-prem LPUs are sold to enterprise; not worth it under 50M tok/day.

Q: HIPAA? A: Groq Enterprise BAAs available; route through /industries/healthcare workflows.

Q: Cost? A: Llama 3.3 70B on Groq ≈ $0.59/M input, $0.79/M output — competitive. CallSphere /pricing bundles.

Q: How fast is Groq 3? A: 1.2 PFLOPS INT8, 500MB SRAM, 150TB/s bandwidth — targeting 1500 tok/s in production for agent workloads.

Sources

• Groq LPU benchmarks 2026 (TokenMix)
• Groq AI inference speed vs GPU 2026
• ArtificialAnalysis benchmark — Groq leads
• Groq LPU architecture
• Groq DeepSeek 2026 latency

Groq LPU for Voice Agents: 330ms End-to-End Pipeline (2026): production view

Groq LPU for Voice Agents: 330ms End-to-End Pipeline (2026) ultimately resolves into one engineering question: when do you use the OpenAI Realtime API versus an async pipeline? Realtime wins on latency for live calls. Async wins on cost, retries, and structured tool reliability for callbacks and SMS flows. Most teams need both, and the routing layer between them becomes the most load-bearing piece of the stack.

Shipping the agent to production

Production AI agents live or die on three loops: evals, retries, and handoff state. CallSphere runs 37 agents across 6 verticals, each with its own eval suite — synthetic call transcripts replayed nightly with assertion checks on extracted entities (date, time, party size, insurance, address). Without that loop, prompt regressions ship silently and you only find out when bookings drop.

Structured tools beat free-form text every time. Our 90+ function tools all enforce JSON schemas validated server-side; if the model hallucinates an integer where a string is required, we retry with a corrective system message before falling back to a deterministic path. For long-running flows, we treat agent handoffs as a state machine — booking → confirmation → SMS — so context survives turn boundaries.

The Realtime API vs. async decision usually comes down to "is the user holding the phone right now?" If yes, Realtime; if no (callback queue, after-hours voicemail), async wins on cost-per-conversation, which we track per agent in 115+ database tables spanning all 6 verticals.

FAQ

Is this realistic for a small business, or is it enterprise-only? 57+ languages are supported out of the box, and the platform is HIPAA aligned, which removes most of the procurement friction in regulated verticals. For a topic like "Groq LPU for Voice Agents: 330ms End-to-End Pipeline (2026)", that means you're not starting from scratch — you're configuring an agent template that's already been hardened across thousands of conversations.

Which integrations have to be in place before launch? Day one is integration mapping (scheduler, CRM, messaging) and prompt tuning against your top 20 real call transcripts. Day two through five is shadow-mode running, where the agent transcribes and recommends but a human still answers, so you can compare side-by-side. Go-live is the moment your eval pass-rate clears your internal bar.

How do we measure whether it's actually working? The honest answer: it scales until your tool catalog gets stale. The agent is only as good as the integrations it can actually call, so the operational discipline is keeping schemas, webhooks, and fallback paths green. The platform handles the rest — observability, retries, multi-region routing — without your team owning the GPU layer.

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