The battle for ultra-low latency AI inference. Compare Groq's Language Processing Units (LPUs) with Cerebras' Wafer-Scale Engine (CS-3), evaluating token costs, Time-To-First-Token, and 2,000+ tokens/second generation throughput.
Calculate monthly bills for real-time voice agents and streaming coding copilots.
Evaluating architecture specifications, benchmark rates, throughput latency, and pricing tiers.
| Dimension | Groq LPUs | Cerebras CS-3 | Key Takeaway / Advantage |
|---|---|---|---|
| Llama 3.3 70B Output Speed | 280 - 320 tok/sec | 1,800 - 2,150 tok/sec | Cerebras (6.5x Faster) |
| Llama 3.1 8B Output Speed | 800 - 1,000 tok/sec | 2,200+ tok/sec | Cerebras (2.2x Faster) |
| Time To First Token (TTFT) | 120 - 160 ms | 80 - 110 ms | Cerebras |
| 70B Input Token Rate | $0.59 / 1M | $0.60 / 1M | Groq (1.5% cheaper) |
| 70B Output Token Rate | $0.79 / 1M | $0.60 / 1M | Cerebras (24% cheaper) |
| 8B Input / Output Rate | $0.05 / $0.08 per 1M | $0.10 / $0.10 per 1M | Groq (Cheaper on 8B) |
| Silicon Architecture | Networked LPUs (Rack-scale SRAM) | Single Giant Wafer (44GB on-chip SRAM) | Different Approaches |
| Supported Models | Llama 3.3, Llama 3.1, Mixtral, Gemma 2 | Llama 3.3 70B, Llama 3.1 8B/70B | Groq (Broader catalog) |
Cerebras is the undisputed champion for raw throughput: At 1,800 to 2,100 tokens per second on Llama 3.3 70B, Cerebras operates at an entirely different magnitude of speed compared to traditional GPUs and even Groq LPUs. Reading a 2,000-word essay takes Cerebras less than 1.2 seconds, making it ideal for autonomous agents that must synthesize multi-page plans instantly.
Where Groq maintains a decisive edge: Groq offers a broader catalog of models (including Mixtral 8x7B, Gemma 2, and specialized Whisper transcription LPUs), superior developer portal tooling, and lower token costs on smaller 8B models ($0.05/M input vs Cerebras' $0.10/M).
Evaluating real monthly unit economics across practical engineering architectures.
Voice AI requiring instantaneous LLM synthesis to begin audio TTS playback before human silence becomes awkward.
An AI developer copilot generating entire 300-token function bodies speculatively on keystroke pause.
High-throughput content moderation and intent categorization requiring lowest possible unit cost.
import time
from openai import OpenAI
# Connect to Cerebras Inference Endpoint
cerebras_client = OpenAI(
base_url="https://api.cerebras.ai/v1",
api_key="YOUR_CEREBRAS_API_KEY"
)
start = time.time()
stream = cerebras_client.chat.completions.create(
model="llama3.3-70b",
messages=[{"role": "user", "content": "Write a 500-word comprehensive architectural summary of Raft consensus."}],
stream=True
)
tokens = 0
for chunk in stream:
if chunk.choices[0].delta.content:
tokens += 1
print(chunk.choices[0].delta.content, end="", flush=True)
duration = time.time() - start
print(f"\n\nGenerated {tokens} tokens in {duration:.2f}s ({tokens/duration:.1f} tokens/sec)")Real-world engineering trade-offs and hidden bill shock traps to watch out for.
Explore complementary benchmarks and financial modeling tools across the index.
Complete CS-3 token rates, throughput curves, and SLA specifications.
Complete LPU hardware benchmark telemetry, model matrix, and rate limits.
Evaluating aggregator failover routing against direct hardware LPUs.
Calculate end-to-end latency and stack economics for real-time speech agents.
On Llama 3.3 70B, Cerebras achieves between 1,800 and 2,100 tokens per second, which is approximately 6 times faster than Groq (~300 tokens/sec) and 25 times faster than standard NVIDIA H100 GPUs (~75 tokens/sec).
Groq charges $0.59/M input and $0.79/M output. Cerebras charges $0.60/M input and $0.60/M output. Cerebras is approximately 24% cheaper on generated output tokens.
Groq builds discrete chips with 230MB of ultra-fast SRAM connected across high-speed boards. Cerebras cuts an entire silicon wafer into a single massive chip with 900,000 cores and 44GB of on-chip SRAM, eliminating chip-to-chip latency bottlenecks.
Yes! Both Groq and Cerebras provide 100% OpenAI-compatible /v1 endpoints. Simply change the base_url and api_key in your OpenAI client instance.
Both deliver outstanding sub-150ms Time-To-First-Token. Cerebras finishes entire paragraphs almost instantly, reducing downstream speech synthesis buffering delay.
Cerebras is actively rolling out context caching. Groq handles repeated system prefixes efficiently across its LPU cluster topology.