Groq LPU: 800 Tokens/sec and Why It Beats GPU for LLM Inference
Groq's Language Processing Unit delivers 800+ tokens/sec on Llama 3.1 70B with near-zero latency - here is the architecture reason why, and how to use it.
GPUs are designed for training - massively parallel floating-point throughput. During LLM inference, however, the bottleneck is almost never compute: it is memory bandwidth. Each forward pass reads billions of model weights from DRAM, and DRAM bandwidth tops out around 3 TB/s even on an H100. Most of that time is spent waiting for data, not computing.
Groq's Language Processing Unit (LPU) is built differently. It uses a deterministic, single-threaded compute fabric with large on-chip SRAM - no speculative execution, no cache hierarchies, no DRAM reads during the critical path. All model weights for supported model sizes fit entirely in SRAM, which provides 10 - 80x higher effective bandwidth per watt than DRAM-based GPU inference.
The result: GroqCloud delivers 800+ tokens/sec on Llama 3.1 70B, compared to 30 - 60 tokens/sec on a typical GPU-based API.
Supported Models on GroqCloud (2026)
Llama 3.1 8B Instruct
Llama 3.1 70B Instruct
Llama 3.3 70B Versatile
Gemma 2 9B IT
Mistral 8x7B (Mixtral)
Whisper Large v3 (audio)
Team workspace
Ship faster with chat, meetings, and projects in one place — Zlyqor.
from groq import Groq
client = Groq(api_key="YOUR_GROQ_API_KEY")
response = client.chat.completions.create(
model="llama-3.1-70b-versatile",
messages=[{"role": "user", "content": "Explain the LPU in plain English"}],
max_tokens=512,
)
print(response.choices[0].message.content)
Streaming Example
pip install groq
stream = client.chat.completions.create(
model="llama-3.1-8b-instant",
messages=[{"role": "user", "content": "Write a sorting algorithm in Rust"}],
stream=True,
)
for chunk in stream:
print(chunk.choices[0].delta.content or "", end="", flush=True)
Because Groq processes tokens deterministically and in-order, the time-to-first-token (TTFT) is typically under 200ms even on the 70B model - comparable to a cached local model response.
Free Tier and Cost
GroqCloud has a generous free tier: 14,400 requests/day and 500k tokens/day per model as of early 2026. Paid tiers are priced at approximately $0.05 - 0.59 per million tokens depending on model size - significantly cheaper than equivalent throughput on AWS or Azure GPU instances.
When to Use Groq vs vLLM vs Ollama
Use case
Best choice
Lowest latency API, no GPU needed
Groq
Self-hosted, high throughput, enterprise
vLLM
Local dev, privacy, offline
Ollama
Groq is ideal for user-facing applications where latency is the primary UX metric - chat interfaces, voice assistants, and real-time coding tools.
Practical deep-dives on LLMs, developer tools, and AI engineering. No filler. Unsubscribe any time.
// written byFIG. AUTH-01
530
Mahmudul Haque Qudrati
CEO & ML Engineer
CEO and ML Engineer at Pristren. Builds AI-powered software for teams and writes about machine learning, LLMs, developer tools, and practical AI applications.
Open Code Review – An AI-powered code review CLI tool: A Practical Overview
Open Code Review is an open-source CLI tool from Alibaba that uses AI to review code changes. It runs locally, supports multiple LLMs, and costs about $0.01 per review. Here's a practical breakdown.
