SambaNova vs Cerebras vs Groq: AI Chip Startups Challenging NVIDIA

Guide 2025-08-27

7 min

Updated: 2026-09-09

SambaNova vs Cerebras vs Groq: AI Chip Startups Challenging NVIDIA

How SambaNova, Cerebras, and Groq each rethink chip design for AI inference and training—dataflow (RDU), wafer-scale, and LPU architectures—and why they offer alternatives to NVIDIA GPUs.

AI Chips SambaNova Cerebras Groq AI Inference AI Hardware

Update, September 9, 2026: The comparison below reflects August 2025. Over the following year the standing of all three companies changed substantially. Groq continues to exist as an independent company, but NVIDIA acquired Groq’s assets under what Groq characterizes as a non-exclusive license, and the result has been commercialized as NVIDIA Groq 3 LPX, whose full production NVIDIA announced on August 24, 2026. Groq itself went on to announce a $350 million Series A on August 17, 2026, with the proceeds earmarked for NVIDIA clusters. Cerebras listed on Nasdaq in May 2026 (ticker CBRS, IPO price $185), opening at roughly twice the offer price on day one²⁰. It also supplies the “Ultrafast” tier of the OpenAI API. SambaNova raised $1 billion in July 2026 at an $11 billion valuation, with JPMorgan Chase adopting it for on-premises inference. The “which one should you pick” guidance below therefore cannot be used as-is for a decision today.

Ever felt like ChatGPT takes forever to respond? The truth is, much of that wait time comes down to chip design. Running AI on a GPU—a graphics card built for gaming—is like driving a Formula 1 car to the grocery store: fast, but not built for the job, so a lot of that power goes to waste.

The three companies profiled here are tackling this problem at the root by building machines designed purely for AI. Put simply, SambaNova is a “transforming robot,” Cerebras is a “brute-force giant,” and Groq is a “zero-waste sprinter”—each reinventing AI processing in its own way¹²³.

SambaNova: The AI Chip That Bends Like a Transforming Robot

Why does chip “shape-shifting” matter?

“Run an image-generation model in the morning, then switch to a text model in the afternoon.” That’s the kind of flexibility SambaNova was built for. Founded in 2017 by Rodrigo Liang with Stanford professors Kunle Olukotun and Chris Ré, the company’s RDU (Reconfigurable Dataflow Unit) changes its internal structure on the fly depending on the workload, much like a transformer toy reshaping itself for a new purpose.

How does it “transform”?

Think of SambaNova’s chip as a box of LEGO bricks: the same pieces can be reassembled into a car one moment and an airplane the next.

The numbers:

Memory capacity: up to 1.5TB (roughly the storage of 100 iPhone Pro Max phones)⁴
Speed gain: 6.6x faster than GPUs
Footprint: 19x smaller⁵

In other words, an AI system that once needed an entire office floor can now run in a single conference room.

What can it actually do?

Remarkable multitasking:

Holds hundreds of AI models in memory simultaneously
Switches between models in 0.000001 seconds (microseconds)
100x faster model switching than GPUs⁶

A concrete example: text generation like ChatGPT

Typical ChatGPT: about 15 characters per second
With SambaNova: more than 200 characters per second⁷

That’s fast enough to finish writing a report in the time it takes to order a coffee.

Cerebras: The World’s Largest Chip Solves Problems Through Brute Force

Why go giant?

The team of former AMD engineers who founded Cerebras in 2015 ran into a problem: “traffic jams” of data²¹. On a conventional chip, moving data between chips takes time—like cars backing up at a highway tollbooth.

Cerebras’s answer: “What if we put everything on one giant chip and eliminate the tollbooth entirely?”

Just how big is it?

Size comparison:

Cerebras WSE-3: 21.5cm x 21.5cm (about the size of a tablet)
A typical chip: 2cm x 2cm (about the size of a thumbnail)
56x larger than an NVIDIA H100, as reported by ServeTheHome⁹

What’s inside:

Transistors: 4 trillion (40x the number of neurons in the human brain)⁸
AI-dedicated cores: 900,000 (roughly the population of a small city)
Memory bandwidth: 21 petabytes per second¹⁰

That’s fast enough to download every video on Netflix in a single second.

What can it actually do?

Blazing processing speed:

ChatGPT-style text generation: 1,800 characters per second
20x faster than NVIDIA GPUs¹¹¹²
Power draw: 23kW (about as much as 8 average households)

Real-world deployments:

Mayo Clinic: developing AI models for cancer research and drug discovery
AlphaSense: accelerating financial-analysis AI
Condor Galaxy 3: a supercomputer built from 64 linked units¹³

It’s especially well suited to workloads that need to crunch massive volumes of data at once.

Groq: The Zero-Waste Sprinter Built for Pure Speed

Why obsess over speed?

In 2016, Jonathan Ross—who had previously worked on Google’s TPU—grew frustrated with how much AI made users wait. “Why does it take a computer so long to think?”

His answer: build a 100-meter sprinter with zero wasted motion. The result was the LPU (Language Processing Unit).

How did Groq cut out the waste?

Think of it as a factory conveyor belt.

A typical GPU works like a restaurant kitchen, cooking each dish as orders come in. Groq’s LPU works like an assembly line, where every step runs in a fixed, predetermined order.

The results:

Response time: under 0.001 seconds (faster than a blink)¹⁴
Power efficiency: 10x better than GPUs¹⁵¹⁶¹⁷
Roughly a tenth of the electricity cost

What can it actually do?

Stunning speed: 13x faster than ChatGPT

Typical ChatGPT: about 10 seconds to respond
With Groq: responds in 0.7 seconds¹⁹

Practical benefits:

Customer support: instant replies
Live chat: response speed indistinguishable from a human
Code completion: suggestions appear as you type

Companies using Groq: Dropbox, Vercel, Volkswagen, Canva, and others¹⁸

(At the time, a plan was described to expand to roughly 136x that capacity — 108,000 LPUs — by early 2025. The LPU technology has since also been productized on NVIDIA’s side, and progress against that plan has not been tracked here.)

Which One Should You Choose? A Simple Guide

If each company were a mode of transportation…

Company	Analogy	Strength	Weakness	Best fit
SambaNova	Transforming robot / bus	Flexibility, multitasking	Higher upfront cost	Companies running multiple AI models
Cerebras	Giant cargo ship	Massive data processing	Physically huge	Research institutions, large enterprises
Groq	Formula 1 car	Unmatched speed	Not built for training	Real-time services

Who should pick which?

Choose SambaNova if you:

Offer multiple AI services
Need to switch between image generation in the morning and text generation in the afternoon
Want to stay flexible for whatever AI models come next

Choose Cerebras if you: