Groq vs Cerebras
Custom silicon inference for real-time voice AI.
Every number cited. Every source linked. No affiliation with any provider.
Quick Verdict
Cerebras achieves higher peak throughput (4,000tok/s with speculative decoding vs Groq's 1,200 tok/s). Groq offers more consistent Sub-100ms TTFT. Both make the LLM step a non-bottleneck in voice pipelines.
Groq (Llama 4 Maverick)
LPU inference
Cerebras (WSE-3)
Speculative decoding
Groq Voice Cost
Est. voice conversation
Cerebras Voice Cost
Est. voice conversation
Head-to-Head Comparison
Both Groq and Cerebras have built purpose-designed silicon for LLM inference, rejecting the GPU paradigm in favor of architectures optimized for sequential token generation and memory bandwidth.
Based on publicly available data as of March 2026. Actual performance may vary.
Groq's LPU delivers 1,200 tokens per second with Sub-100ms time to first token, fast enough that the LLM step matches human reaction speed.
Cerebras's WSE-3 chip contains 4 trillion transistors and 900,000 cores. With speculative decoding, it achieves up to 4,000 tokens per second using a 3B-parameter draft model verified against a 70B-parameter model, giving users the speed of the smaller model with the quality of the larger one.
Architecture Deep Dive
Groq LPU
The Language Processing Unit is a custom ASIC designed for deterministic, high-throughput AI inference. Unlike GPUs, which are optimized for parallel matrix operations, the LPU is architected for the sequential nature of autoregressive token generation.
- Fastest production inference (1,200 tok/s)
- Sub-100ms TTFT matches human reaction speed
- Custom LPU hardware, not GPU-based
Cerebras WSE-3
The Wafer Scale Engine 3 is the largest chip ever built: a single wafer-scale processor with 4 trillion transistors and 900,000 AI-optimized cores. It eliminates the memory bandwidth bottleneck that limits conventional GPU inference.
- 4 trillion transistor chip, 900K cores
- Speculative decoding: speed of 3B + quality of 70B
- 80–150ms voice translation latency
Both architectures solve the same fundamental problem: the memory bandwidth wall that prevents GPUs from delivering consistent low-latency inference for single requests. GPUs excel at batching many requests together for throughput. Custom silicon excels at minimizing latency for individual requests, which is exactly what real-time voice applications require.
Voice AI Implications
With Sub-100ms TTFT from Groq and 80–150ms from Cerebras, the LLM is no longer the dominant latency contributor in a voice pipeline. The bottleneck has shifted to TTS time-to-first-byte, which ranges from 40-200ms depending on the provider. In a well-optimized cascaded pipeline, the LLM step now contributes less than 100ms to total end-to-end latency.
Cerebras reports 80-150ms voice translation latency in their real-time voice agent benchmarks. The target for natural-feeling conversation is under 300ms total pipeline response time, a threshold that becomes achievable when the LLM step takes under 100ms.
Customer Impact (Groq)
Published case studies from Groq customers demonstrate measurable latency improvements in production deployments.
| Customer | Impact | Source |
|---|---|---|
| Willow | 300-500ms faster response times | Groq Case Study |
| Tenali | 25x latency reduction, 10x cost reduction | Groq |
| Mem0 | Nearly 5x latency improvement | Groq |
Customer impact data from Groq and Groq customer stories. These are provider-reported figures.
When to Choose Which
Choose Groq when:
- Consistent sub-100ms TTFT is more important than peak throughput
- You need the lowest voice AI cost per minute ($0.002/min)
- Deterministic latency behavior matters for your SLAs
- You are building on Llama-family models
Choose Cerebras when:
- Peak throughput matters more than cost (4,000 tok/s)
- You want 70B-quality responses at 3B-level speed via speculative decoding
- Voice translation or real-time multilingual processing is a use case
- You need the absolute highest tokens-per-second available
For most voice AI applications, either provider makes the LLM step fast enough that it is no longer the bottleneck. The practical difference between Sub-100ms TTFT and 80–150ms TTFT is measurable but unlikely to be perceptible to end users. The choice more often comes down to model availability, pricing, and API maturity.
Both providers are in an early growth phase. Groq has a more established developer ecosystem and published customer case studies. Cerebras has a partnership with OpenAI for 750MW of wafer-scale AI systems (2026-2028 deployment), which signals long-term infrastructure commitment.
Sources
- GroqVerified Mar 4, 2026
- CerebrasVerified Mar 4, 2026
- Groq - Willow Case StudyVerified Mar 4, 2026
- Cerebras - Realtime Voice TranslationVerified Mar 4, 2026
Disclaimer: STT WER, latency, noise robustness, and multi-language data are independently tested by Speko using automated benchmarks. Pricing reflects publicly available rates. TTS, LLM, S2S, and platform data sourced from official documentation. We are not affiliated with any provider listed.
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