startup that makes custom gpus for inference and sounds like Elon’s AI
Groq; Inc
2019-03-11
<div><div>
	<div>
	<div>
	<p>
	</p><h2>Built for leaders needing the fastest time-to-value</h2>	<p></p>
	</div>
	<div>
	<p>Groq offers comprehensive end-to-end acceleration solutions from scalable, ultra low latency systems to generalized software. We improve results by orders of magnitude for our customers needing to modernize underperforming systems. Groq increases performance and enables innovation unlike any other technology provider.</p>
	</div>
	</div>
	<div>
	<p>
	</p><h2>Our First Generation Solutions</h2>	<p></p>
	</div>
	<div>
	<div>
	<div>
	</div>
	<div>
	<p>
	</p><h3>GroqCard™</h3>	<p></p>
	</div>
	<div>
	<p>A single GroqChip in a PCIe Gen4 x16 interface with 230 MB of on-die memory delivers up to 750 TOPs, 188 TFLOPs (INT8, FP16 @900 MHz).</p>
	</div>
	</div>
	<div>
	<div>
	</div>
	<div>
	<p>
	</p><h3>GroqNode™</h3>	<p></p>
	</div>
	<div>
	<p>Eight GroqCard accelerators in a rack-ready 4U server chassis supports millions of parameters and is a scalable building block for a single global hop network, delivering up to 6 POPs, 1.5 PFLOPs (INT8, FP16 @900MHz).</p>
	</div>
	</div>
	<div>
	<div>
	</div>
	<div>
	<p>
	</p><h3>GroqRack™</h3>	<p></p>
	</div>
	<div>
	<p>Eight plus one interconnected GroqNode servers delivers low system latency of ~1.6 µsec and up to 48 POPs, 12 PFLOPs (INT8, FP16 @900MHz).</p>
	</div>
	</div>
	<div>
	<div>
	</div>
	<div>
	<p>
	</p><h3>GroqCloud™</h3>	<p></p>
	</div>
	<div>
	<p>GroqCloud delivers 216 POPs, 54 PFLOPs (INT8, FP16) and is growing.</p>
	</div>
	</div>
	</div>
	<div>
	<div><p>
	Groq offers deployment optionality to meet customers where they need performance most, while our architecture and software tools improve developer velocity and deliver industry-leading results. </p><h2>Redefining the Developer Experience</h2>
<div>
<ul>
 	<li>Groq™ Compiler: Out-of-the-box applicability, serving the majority of industry standard models</li>
 	<li>Groq API: Meets tailored solution needs with fine-grained control</li>
 	<li>Productivity Tools: GroqView™ Visualization and Profiler, Performance Estimator, and GroqFlow™ Tool Chain</li>
 	<li><b>Try Out Groq: </b><a href="/cdn-cgi/l/email-protection#fc888e859b8e938dbc9b8e938dd29f9391">Contact</a> us for access to the GroqWare™ Developer Tools Package</li>
</ul>
</div>	</div>
	<div>
	<div>
	<p>
	</p><h2>Providing Value to Customers Today</h2>	<p></p>
	</div>
	<div>
	<div>
	<p>
	</p><h3>Groq Enables Drug Discovery in Minutes at Argonne National Laboratory</h3>	<p></p>
	</div>
	<div>
	<div>
	</div>
	<div>
	<div>
	<p>“Using the Groq platform at Argonne, we were able to accelerate our efforts to identify promising COVID-19 drug candidates from a vast number of small molecules. The system’s AI capabilities enabled us to achieve significantly more inferences a second,<strong> reducing the time needed for each search from days to minutes.”</strong></p>
	</div>
	<div>
	<p>—Tom Brettin, Computational Scientist at Argonne National Laboratory</p>	</div>
	</div>
	</div>
	</div>
	</div>
	</div>
	<div>
	<p>
	</p><h2>Key Advantages and Differentiators<br /></h2>	<p></p>
	</div>
	<div>
	<div>
<div><p><strong>We’re proud of our breakthrough cybersecurity anomaly detection work, recently validated by the US Army. Our algorithms need to run on GroqChip™ to achieve these results, outperforming quantum computers to realize 600-1000x faster results. Groq is the technology bridging our solution and quantum, helping us deliver on the promises of quantum today.</strong></p><p>—Jason Turner, CEO and Founder of Entanglement AI</p></div>
<div><p><strong>We look forward to working with Groq to help our government partners address their enduring need for higher performance, lower latency compute solutions to process large volumes of data faster and use less power.</strong></p><p>—Abi Sivanathan, Technology Architect, In-Q-Tel</p></div>
<div><p><strong>Groq validates that it is developing disruptive technology that can address rapidly expanding AI and ML opportunities with the ability to drive powerful use cases...</strong></p><p>—Futurum Research</p></div>
<div><p><strong>The TSP is designed to extend single core performance across multiple chips, can perform inference tasks quickly, and is deterministic—it delivers consistent, predictable, and repeatable performance with zero overhead for context switching.</strong></p><p>—Kyle Felker, Computational Scientist, Argonne National Laboratory</p></div>
<div><p><strong>Groq’s TSP stands out in peak performance and its [deep-learning] accelerator is the fastest available on the merchant market.</strong></p><p>—Linley Gwennap, Microprocessor Report - <i>Groq Rocks Neural Networks</i></p></div>
<div><p><strong>Groq is one of the leaders, if not the leader, in machine learning...</strong></p><p>—Patrick Moorhead, Founder, CEO, and Chief Analyst, Moor Insights &amp; Strategy</p></div>
</div>
	</div>
	</div><p>This site uses cookies to operate our website and analyze website traffic. <a href="https://groq.com/privacy-policy/">Read More</a><a>Accept</a><a>Reject</a> </p><div>
	<div>
	<p>
	</p><h2>GroqCloud™</h2>	<p></p>
	</div>
	<div>
	<p>GroqCloud can be used today, from anywhere, to take advantage of our architectural innovation providing synchronous, deterministic, ultra-low latency compute solutions, with the same quality of our on-premise products. The advantages of the Tensor Streaming Processor (TSP) architecture include predictability that allows customers a more finite control over usage/inference and cost. The developer can reduce time-to-production using our generalized compiler and software solutions that exist as part of our cloud service without the need to stand-up their own racks or systems. Access via GroqCloud means scalability as the workload or model demands it. Groq is focused on TCO, developer velocity, ease of use, and time-to-results, which are best facilitated via our cloud and co-cloud options.</p>
	</div>
	</div><div>
	<div>
	<p>
	</p><h2>Predictability</h2>	<p></p>
	</div>
	<div>
	<div>
	<p>Your inferences - exactly on time, every time.</p>	</div>
	<div>
	<p>Determinism means predictable and repeatable performance, while eliminating tail latencies and correction of all single-bit errors in hardware, preventing uncorrectable multi-bit errors. The total execution time is known at compile time. This means knowing a workload’s exact runtime and order of operations—with zero variance. Regardless of whether it’s one GroqChip™ or a network of chips, workloads run identically the first time and the millionth time—with no performance variation. Better for planning, better for safety, better for budgeting—and better for your developers.</p><p>Determinism plus low latency equals cutting-edge predictability, enabling true QoS (Quality of Service) with Groq.</p>	</div>
	</div>
	<div>
	<p>
	Consistent &amp; 
	predictable 
	execution time
	Per Pass (execution time)
	Groq
	Variable execution times
	Per Pass (execution time)
	Others
	</p>
	</div>
	<div>
	<div>
	<p>	</p>
	</div>
	<div>
	<p>Developers know total execution time at compile time, with no need for error checking.</p>	</div>
	</div>
	<div>
	<div>
	<p>	</p>
	</div>
	<div>
	<p>Tail latencies and performance variation are a thing of the past.</p>	</div>
	</div>
	<div>
	<div>
	<p>	</p>
	</div>
	<div>
	<p>Better for planning, safety, and budgeting.</p>	</div>
	</div>
	</div><div>
	<div>
	<p>
	</p><h2>Low Latency</h2>	<p></p>
	</div>
	<div>
	<div>
	<p>All your inferences - right now.</p>	</div>
	<div>
	<p>Groq offers the lowest latency machine learning architecture on the market. This means ~18,800 IPS on ResNet 50 (at batch-1) with a latency of ~0.05ms. And unlike others we don’t compromise latency for throughput, we maintain high throughput in batch 1. Our batch 1 performance works very differently compared to high batch performance GPUs. We don’t wait to collect data and then send it to a processor. Give us one piece of data, we will give you the result. The response? Lower latency, more accuracy, and less energy consumed. Image identification, segmentation, natural language processing, and machine vision rely upon low latency, predictability, and accuracy so identification and decisions are timely enough to matter. For your real-time, mission critical applications lower latency is the difference maker.</p>
	</div>
	</div>
	<div>
	<p>
	Low batch size advantage
	Groq
	Others
	</p>
	</div>
	<div>
	<div>
	<p>	</p>
	</div>
	<div>
	<p>The lowest latency machine learning architecture on the market, enabling more complex algorithms.</p>	</div>
	</div>
	<div>
	<div>
	<p>	</p>
	</div>
	<div>
	<p>No compromising latency for throughput.</p>	</div>
	</div>
	<div>
	<div>
	<p>	</p>
	</div>
	<div>
	<p>Peak performance at batch 1, ideal for low latency, mission critical applications.</p>	</div>
	</div>
	</div><div>
	<div>
	<p>
	</p><h2>Velocity</h2>	<p></p>
	</div>
	<div>
	<div>
	<p>Insights to models - fast.</p>	</div>
	<div>
	<p>At Groq, we’re fundamentally changing the developer’s human experience. High performance, low power, low latency are all wonderful technology features, but we’re removing what limits how humans innovate—which happens to be the biggest bottleneck in the industry.</p><p>Our chips, nodes, and networks are deterministic. Here, determinism equals industry-best predictable performance—and that’s what speeds up developers. Within seconds of a developer hitting compile on their code, Groq’s static profiling provides an ultra-detailed performance report and visualization of the entire chip’s compute and memory usage for the whole program. The developer has an immediate and accurate summary of performance metrics like latency, utilization and bandwidth, power, memory and FLOP occupancy. Suddenly, the slow, painful dynamic profiling process is gone. As for competitor configuration changes, noisy neighbors require continuous error removal. Groq’s TSP architecture means workloads are executed identically every time so you never have to debug the same program twice.</p>	</div>
	</div>
	<div>
	<p>
	velocity
	developer 
	enabled 
	Determinism- 
	Groq
	process
	dynamic profiling 
	Slow, painful 
	Others
	</p>
	</div>
	<div>
	<div>
	<p>	</p>
	</div>
	<div>
	<p>Groq enables developer velocity with determinism, delivering insights to models faster than ever.</p>	</div>
	</div>
	<div>
	<div>
	<p>	</p>
	</div>
	<div>
	<p>GroqView™ profiler provides performance reports and visualizations of the entire chip’s compute and memory usage for a program at compile time - say goodbye to the slow dynamic profiling process.</p>	</div>
	</div>
	<div>
	<div>
	<p>	</p>
	</div>
	<div>
	<p>Determinism equals predictive execution - no debugging the same program twice, ever.</p>	</div>
	</div>
	</div><div>
	<div>
	<p>
	</p><h2>Accuracy</h2>	<p></p>
	</div>
	<div>
	<div>
	<p>True numerical reality - consistently.</p>	</div>
	<div>
	<p>Uniquely, the GroqChip™ accelerator contains both floating point (FP) and integer, so you can train and deploy inference with the same device. Groq TruePoint™ technology doesn’t sacrifice between accuracy or performance across artificial intelligence (AI), machine learning (ML), deep learning (DL), and linear algebra computing by working on floating point calculations. Most solutions train on one device and infer on another using distinct hardware types with dissimilar numerics, resulting in unforeseen compatibility issues and increased TCO. Groq’s Tensor Streaming Processor (TSP) architecture is more versatile, employing a single source of numerics. TruePoint enables accuracy at lower input bit sizes which results in better performance, lower power, and less memory usage for your workloads. The benefit for large complex iterative algorithms is less accumulated error, for example, a training algorithm can see faster convergence. A world with TruePoint means performance and accuracy, rather than the constant decision between the two.</p>
	</div>
	</div>
	<div>
	<div>
	<p>	</p>
	</div>
	<div>
	<p>TruePoint™ technology implements a single source of numerics. works on floating point calculations.</p>	</div>
	</div>
	<div>
	<div>
	<p>	</p>
	</div>
	<div>
	<p>Training and inference deployment on the same device.</p>	</div>
	</div>
	<div>
	<div>
	<p>	</p>
	</div>
	<div>
	<p>Delivers reproducible high accuracy and precision without sacrificing performance.</p>	</div>
	</div>
	</div><div>
	<div>
	<p>
	</p><h2>Scalability</h2>	<p></p>
	</div>
	<div>
	<div>
	<p>Inference to training - one device, scaling near-linearly.</p>	</div>
	<div>
	<p>The biggest advantage of Groq’s deterministic architecture is that it scales. Our RealScale™ chip-to-chip network contains no switches or CPUs— GroqChip™ and GroqCard™ are all direct connections, orchestrated by GroqWare™ suite (API &amp; compiler). Networking is built into the chip, so racks are as efficient as a card across both inference and training. This in turn eliminates the need to move data with extra silicon, saving power and improving on overall TCO. From HPC to ML, RealScale offers near-linear scaling from just one to thousands of connected devices.</p>
	</div>
	</div>
	<div>
	<p>
	GroqRack
	™ 
	64 Chips
	GroqNode
	™ 
	8 Chips
	GroqCard
	™ 
	2 Chips
	GroqChip
	™ 
	1 Chip
	50,000
	45,000
	40,000
	35,000
	30,000
	25,000
	15,000
	10,000
	5,000
	0
	TOps
	</p>
	</div>
	<div>
	<div>
	<p>	</p>
	</div>
	<div>
	<p>RealScale™ chip-to-chip network contains no switches or CPUs - networking is built into GroqChip.</p>	</div>
	</div>
	<div>
	<div>
	<p>	</p>
	</div>
	<div>
	<p>Groq hardware scales near linearly from one to thousands of devices, from inference to training, with no efficiency trade-offs.</p>	</div>
	</div>
	<div>
	<div>
	<p>	</p>
	</div>
	<div>
	<p>Less data movement means more power savings and improved TCO.</p>	</div>
	</div>
	</div></div>||||I|||| * Products
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 Posts

  Groq Inc @groqinc ·
  7 Feb

  What does driving the cost of compute mean? @GroqInc CEO and founder @JonathanRoss321 explains to @danielnewmanUV, the key lies in our performance. “When we’re delivering 200X, 600X, 1000X the performance, we’re giving customers 200, 600, 1000 times the performance per dollar.”

 The Futurum Group @TheFuturumGroup

 On this episode [email protected] , @DanielNewmanUV is joined by @GroqInc CEO, Jonathan Ross, for a conversation about the future of #AI accelerators and how this #technology can help organizations reduce uncertainty.
 https://buff.ly/40sqPk1

 @FuturumResearch

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  Groq Inc @groqinc ·
  6 Feb

  We’re looking for creative problem solvers to help us build the next generation of #MachineLearning Systems! Apply here https://groq.com/careers/?gh_jid=5090081003

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 Insights

 Automated Discovery Meets Automated Compilation: Groq Runs LLaMA, Meta’s Newest LLM

 March 9, 2023

 Meet Groq™ Compiler Solutions for a symbiotic software-hardware ecosystem

 March 1, 2023

 Groq Launches GroqFlow™: Radically Simplifying Model Building for GroqChip™

 September 6, 2022
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 Menu
 * Products
 * Verticals
 * Resources
 + Videos
 + Docs
 + Blog
 + Press
 + Events
 * About
 * Careers
 * Contact Us
 Announcements

 News

 Read:
 Automated Discovery Meets Automated Compilation: Groq Runs LLaMA, Meta’s Newest LLM
 Read: AI Accelerator Groq™ Adapts and Runs LLaMA, the Meta™ Chatbot Model and Competitor to ChatGPT, for Its Systems
 Read: Automated Discovery Meets Automated Compilation: Groq Runs LLaMA, Meta’s Newest LLM
 Read: Meet Groq™ Compiler Solutions for a symbiotic software-hardware ecosystem
 Explore: Groq at SC22
 Latest Benchmarks: Groq and STAC Release Financial Services Industry Benchmarks
 In the News: Forbes, Cybersecurity Is Entering The High-Tech Era

 Posts

  Groq Inc @groqinc ·
  7 Feb

  What does driving the cost of compute mean? @GroqInc CEO and founder @JonathanRoss321 explains to @danielnewmanUV, the key lies in our performance. “When we’re delivering 200X, 600X, 1000X the performance, we’re giving customers 200, 600, 1000 times the performance per dollar.”

 The Futurum Group @TheFuturumGroup

 On this episode [email protected] , @DanielNewmanUV is joined by @GroqInc CEO, Jonathan Ross, for a conversation about the future of #AI accelerators and how this #technology can help organizations reduce uncertainty.
 https://buff.ly/40sqPk1

 @FuturumResearch

  Reply on Twitter 1623014315174539264 Retweet on Twitter 1623014315174539264 1 Like on Twitter 1623014315174539264 6 Twitter 1623014315174539264
  Groq Inc @groqinc ·
  6 Feb

  We’re looking for creative problem solvers to help us build the next generation of #MachineLearning Systems! Apply here https://groq.com/careers/?gh_jid=5090081003

  Reply on Twitter 1622628258553663488 Retweet on Twitter 1622628258553663488 Like on Twitter 1622628258553663488 2 Twitter 1622628258553663488

 Insights

 Automated Discovery Meets Automated Compilation: Groq Runs LLaMA, Meta’s Newest LLM

 March 9, 2023

 Meet Groq™ Compiler Solutions for a symbiotic software-hardware ecosystem

 March 1, 2023

 Groq Launches GroqFlow™: Radically Simplifying Model Building for GroqChip™

 September 6, 2022

 Accelerating Systems with Real-time AI Solutions

 Built for leaders needing the fastest time-to-value

 Groq offers comprehensive end-to-end acceleration solutions from scalable, ultra low latency systems to generalized software. We improve results by orders of magnitude for our customers needing to modernize underperforming systems. Groq increases performance and enables innovation unlike any other technology provider.

 Our First Generation Solutions

 GroqCard™

 A single GroqChip in a PCIe Gen4 x16 interface with 230 MB of on-die memory delivers up to 750 TOPs, 188 TFLOPs (INT8, FP16 @900 MHz).

 Learn more

 GroqNode™

 Eight GroqCard accelerators in a rack-ready 4U server chassis supports millions of parameters and is a scalable building block for a single global hop network, delivering up to 6 POPs, 1.5 PFLOPs (INT8, FP16 @900MHz).

 Learn more

 GroqRack™

 Eight plus one interconnected GroqNode servers delivers low system latency of ~1.6 µsec and up to 48 POPs, 12 PFLOPs (INT8, FP16 @900MHz).

 Learn more

 GroqCloud™

 GroqCloud delivers 216 POPs, 54 PFLOPs (INT8, FP16) and is growing.

 Learn more
 Groq offers deployment optionality to meet customers where they need performance most, while our architecture and software tools improve developer velocity and deliver industry-leading results .

 Redefining the Developer Experience

 * Groq™ Compiler: Out-of-the-box applicability, serving the majority of industry standard models
 * Groq API: Meets tailored solution needs with fine-grained control
 * Productivity Tools: GroqView™ Visualization and Profiler, Performance Estimator, and GroqFlow™ Tool Chain
 * Try Out Groq: Contact us for access to the GroqWare™ Developer Tools Package

 Providing Value to Customers Today

  Groq Enables Drug Discovery in Minutes at Argonne National Laboratory

  “Using the Groq platform at Argonne, we were able to accelerate our efforts to identify promising COVID-19 drug candidates from a vast number of small molecules. The system’s AI capabilities enabled us to achieve significantly more inferences a second, reducing the time needed for each search from days to minutes.”

  —Tom Brettin, Computational Scientist at Argonne National Laboratory
  Read the full story

 Key Advantages and Differentiators

 Predictability
 Low Latency
 Velocity
 Accuracy
 Scalability

 We’re proud of our breakthrough cybersecurity anomaly detection work, recently validated by the US Army. Our algorithms need to run on GroqChip™ to achieve these results, outperforming quantum computers to realize 600-1000x faster results. Groq is the technology bridging our solution and quantum, helping us deliver on the promises of quantum today.
 —Jason Turner, CEO and Founder of Entanglement AI

 We look forward to working with Groq to help our government partners address their enduring need for higher performance, lower latency compute solutions to process large volumes of data faster and use less power.

 —Abi Sivanathan, Technology Architect, In-Q-Tel

 Groq validates that it is developing disruptive technology that can address rapidly expanding AI and ML opportunities with the ability to drive powerful use cases...

 —Futurum Research

 The TSP is designed to extend single core performance across multiple chips, can perform inference tasks quickly, and is deterministic—it delivers consistent, predictable, and repeatable performance with zero overhead for context switching.

 —Kyle Felker, Computational Scientist, Argonne National Laboratory

 Groq’s TSP stands out in peak performance and its [deep-learning] accelerator is the fastest available on the merchant market.

 —Linley Gwennap, Microprocessor Report - Groq Rocks Neural Networks

 Groq is one of the leaders, if not the leader, in machine learning...

 —Patrick Moorhead, Founder, CEO, and Chief Analyst, Moor Insights & Strategy
 © 2023 Groq, Inc.
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 SAVE & ACCEPT

 GroqCloud™

 GroqCloud can be used today, from anywhere, to take advantage of our architectural innovation providing synchronous, deterministic, ultra-low latency compute solutions, with the same quality of our on-premise products. The advantages of the Tensor Streaming Processor (TSP) architecture include predictability that allows customers a more finite control over usage/inference and cost. The developer can reduce time-to-production using our generalized compiler and software solutions that exist as part of our cloud service without the need to stand-up their own racks or systems. Access via GroqCloud means scalability as the workload or model demands it. Groq is focused on TCO, developer velocity, ease of use, and time-to-results, which are best facilitated via our cloud and co-cloud options.

 Predictability

 Your inferences - exactly on time, every time.

 Determinism means predictable and repeatable performance, while eliminating tail latencies and correction of all single-bit errors in hardware, preventing uncorrectable multi-bit errors. The total execution time is known at compile time. This means knowing a workload’s exact runtime and order of operations—with zero variance. Regardless of whether it’s one GroqChip™ or a network of chips, workloads run identically the first time and the millionth time—with no performance variation. Better for planning, better for safety, better for budgeting—and better for your developers.

 Determinism plus low latency equals cutting-edge predictability, enabling true QoS (Quality of Service) with Groq.

 learn more
 Consistent & predictable execution time Per Pass (execution time) Groq Variable execution times Per Pass (execution time) Others
 Developers know total execution time at compile time, with no need for error checking.
 Tail latencies and performance variation are a thing of the past.
 Better for planning, safety, and budgeting.

 Low Latency

 All your inferences - right now.

 Groq offers the lowest latency machine learning architecture on the market. This means ~18,800 IPS on ResNet 50 (at batch-1) with a latency of ~0.05ms. And unlike others we don’t compromise latency for throughput, we maintain high throughput in batch 1. Our batch 1 performance works very differently compared to high batch performance GPUs. We don’t wait to collect data and then send it to a processor. Give us one piece of data, we will give you the result. The response? Lower latency, more accuracy, and less energy consumed. Image identification, segmentation, natural language processing, and machine vision rely upon low latency, predictability, and accuracy so identification and decisions are timely enough to matter. For your real-time, mission critical applications lower latency is the difference maker.

 Learn More
 Low batch size advantage Groq Others
 The lowest latency machine learning architecture on the market, enabling more complex algorithms.
 No compromising latency for throughput.
 Peak performance at batch 1, ideal for low latency, mission critical applications.

 Velocity

 Insights to models - fast.

 At Groq, we’re fundamentally changing the developer’s human experience. High performance, low power, low latency are all wonderful technology features, but we’re removing what limits how humans innovate—which happens to be the biggest bottleneck in the industry.

 Our chips, nodes, and networks are deterministic. Here, determinism equals industry-best predictable performance—and that’s what speeds up developers. Within seconds of a developer hitting compile on their code, Groq’s static profiling provides an ultra-detailed performance report and visualization of the entire chip’s compute and memory usage for the whole program. The developer has an immediate and accurate summary of performance metrics like latency, utilization and bandwidth, power, memory and FLOP occupancy. Suddenly, the slow, painful dynamic profiling process is gone. As for competitor configuration changes, noisy neighbors require continuous error removal. Groq’s TSP architecture means workloads are executed identically every time so you never have to debug the same program twice.

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 velocity developer enabled Determinism- Groq process dynamic profiling Slow, painful Others
 Groq enables developer velocity with determinism, delivering insights to models faster than ever.
 GroqView™ profiler provides performance reports and visualizations of the entire chip’s compute and memory usage for a program at compile time - say goodbye to the slow dynamic profiling process.
 Determinism equals predictive execution - no debugging the same program twice, ever.

 Accuracy

 True numerical reality - consistently.

 Uniquely, the GroqChip™ accelerator contains both floating point (FP) and integer, so you can train and deploy inference with the same device. Groq TruePoint™ technology doesn’t sacrifice between accuracy or performance across artificial intelligence (AI), machine learning (ML), deep learning (DL), and linear algebra computing by working on floating point calculations. Most solutions train on one device and infer on another using distinct hardware types with dissimilar numerics, resulting in unforeseen compatibility issues and increased TCO. Groq’s Tensor Streaming Processor (TSP) architecture is more versatile, employing a single source of numerics. TruePoint enables accuracy at lower input bit sizes which results in better performance, lower power, and less memory usage for your workloads. The benefit for large complex iterative algorithms is less accumulated error, for example, a training algorithm can see faster convergence. A world with TruePoint means performance and accuracy, rather than the constant decision between the two.

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 TruePoint™ technology implements a single source of numerics. works on floating point calculations.
 Training and inference deployment on the same device.
 Delivers reproducible high accuracy and precision without sacrificing performance.

 Scalability

 Inference to training - one device, scaling near-linearly.

 The biggest advantage of Groq’s deterministic architecture is that it scales. Our RealScale™ chip-to-chip network contains no switches or CPUs— GroqChip™ and GroqCard™ are all direct connections, orchestrated by GroqWare™ suite (API & compiler). Networking is built into the chip, so racks are as efficient as a card across both inference and training. This in turn eliminates the need to move data with extra silicon, saving power and improving on overall TCO. From HPC to ML, RealScale offers near-linear scaling from just one to thousands of connected devices.

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 GroqRack ™ 64 Chips GroqNode ™ 8 Chips GroqCard ™ 2 Chips GroqChip ™ 1 Chip 50,000 45,000 40,000 35,000 30,000 25,000 15,000 10,000 5,000 0 TOps
 RealScale™ chip-to-chip network contains no switches or CPUs - networking is built into GroqChip.
 Groq hardware scales near linearly from one to thousands of devices, from inference to training, with no efficiency trade-offs.
 Less data movement means more power savings and improved TCO.
Accelerating Systems with Real-time AI Solutions - Groq
https://groq.com/
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2024-06-24T00:00:00Z
<div><div><section><header></header><div><p></p></div></section><div><div><p></p><h2><span>_</span>Transformers etched into silicon</h2><p></p></div><p>By burning the transformer architecture into our chips, we can run AI models an order of magnitude faster and cheaper than GPUs.</p></div><div><div><p></p></div><div><p></p><h2><span>_</span>Build products that are impossible with GPUs</h2><p></p></div></div><div><div><p></p><h2>Real-time voice agents</h2><p></p><p>Ingest thousands of words in milliseconds</p></div><div><p></p><h2>Better coding with tree search</h2><p></p><p>Compare hundreds of responses in parallel</p></div><div><p></p><h2>Multicast speculative decoding</h2><p></p><p>Generate new content in real-time</p></div></div><section><div><div><p></p><h2><span>_</span>Run tomorrow's trillion parameter models</h2><p></p></div><div><div><p>Fully open-source software stack</p></div><div><div><p>Expansible to 100T param models<br/></p></div><div><p>Beam search and MCTS decoding</p></div></div><div><p>MoE and transformer variants</p></div></div></div><div><p></p></div></section></div></div>||||I||||      Etched raises $120M to build Sohu – Learn more
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              Meet Sohu: the world's first transformer ASIC

              _ Transformers etched into silicon

            By burning the transformer architecture into our chips, we can run AI models an order of magnitude faster and cheaper than GPUs.

                Tokens
                per Second
                NVIDIA
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                Etched
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            _ Build products that are impossible with GPUs

                Real-time voice agents

              Ingest thousands of words in milliseconds

                Better coding with tree search

              Compare hundreds of responses in parallel

              Multicast speculative decoding

            Generate new content in real-time

              _ Run tomorrow's trillion parameter models

                Only one core
                Fully open-source software stack
                Expansible to 100T param models
                Beam search and MCTS decoding
                144 GB HBM3E per chip
                MoE and transformer variants

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Etched | The World's First Transformer ASIC
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2024-02-14T00:00:00Z
<div><div>
    <div>
              <p>
          </p><h2>
            Purpose-built for tensor contraction
          </h2>
        <p></p>
                    <p>AI models structure data in tensors of various shapes. The RNGD chip fully exploits parallelism and data reuse by flexibly adapting to each tensor contraction with software-defined tactics and supporting model-wise operator fusion.</p>
          </div>

          
    
          <p>
          Uniquely designed for AI inference deployment, Furiosa TCP unlocks superior utilization, performance and energy efficiency.
        </p>
    
          <p>AI models structure data in tensors of various shapes. The RNGD chip fully exploits parallelism and data reuse by flexibly adapting to each tensor contraction with software-defined tactics and supporting model-wise operator fusion.</p>
    
    
      </div><div>
    <div>
              <p>
          </p><h2>
            ENTERPRISE-READY &amp; CLOUD-READY STACK
          </h2>
        <p></p>
                </div>

          
    
    
    
          <div>
                  <div>
                                  <h3>
                    Workflow Integration
                  </h3>
                                
  
  <p>
    <span>Our platform integrates seamlessly with your existing workflow, offering easy-to-use APIs, support for popular AI frameworks like PyTorch, ONNX, TensorFlow Lite, and compatibility with NumPy data structures.</span>
  </p>
              </div>
                  <div>
                                  <h3>
                    Model Optimization
                  </h3>
                                
  
  <p>
    <span>Leveraging our advanced compiler, we ensure your models achieve peak performance per watt. With our profiling tools identifying performance bottlenecks, we can further enhance model performance and efficiency.</span>
  </p>
              </div>
                  <div>
                                  <h3>
                    Flexible Deployment
                  </h3>
                                
  
  <p>
    <span>Furiosa hardware provides the flexibility to choose multiple processing elements (PEs) depending on your workloads. It’s designed for data center adaptability, incorporating containerization and Kubernetes, to facilitate the swift scaling of AI projects.</span>
  </p>
              </div>
              </div>
    
          <p>
        
                  
                              
      <a href="https://furiosa.ai/software">
        
          
          <span>Learn more about Furiosa SW</span>
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        Meet RNGD - 2nd-gen AI accelerator

            The most efficient data center accelerator for high-performance LLM and multimodal deployment

            512 TFLOPS 64 TFLOPS (FP8) x 8 Processing Elements 48 GB HBM3 Memory Capacity 1.5 TB/s Memory Bandwidth 150 W Thermal Design Power
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          512 TFLOPS 64 TFLOPS (FP8) x 8 Processing Elements 48 GB HBM3 Memory Capacity 1.5 TB/s Memory Bandwidth 150 W Thermal Design Power

          Performance results

            Llama 2 7B

            Energy Efficiency

              Perf/Watt (tokens/s/W)

              *Higher value is better

              Batch=32, Input Length=2K, Output Length=2K

              Batch=16, Input Length=2K, Output Length=2K

            Latency

              (ms)

              *Lower value is better

              Batch=1, Sequence Length=128

              Batch=1, Sequence Length=128

            Throughput

              (tokens/s)

              *Higher value is better

              Batch=16, Input Length=2K, Output Length=2K

              Batch=32, Input Length=2K, Output Length=2K

                                             RNGD      H100      L40S
                Technology               TSMC 5nm  TSMC 4nm  TSMC 5nm
                BF16/FP8 (TFLOPS)         256/512  989/1979   362/733
                INT8/INT4 (TFLOPS)       512/1024    1979/-   733/733
                Memory Capacity (GB)           48        80        48
                Memory Bandwidth (TB/s)       1.5      3.35      0.86
                Host I/F                 Gen5 x16  Gen5 x16  Gen4 x16
                TDP (W)                       150       700       350
                

              Disclaimer: Measurements by FuriosaAI internally on current specifications and/or internal engineering calculations. Nvidia results were retrieved from Nvidia website, https://developer.nvidia.com/d... , on February 14, 2024.

              Purpose-built for tensor contraction

              AI models structure data in tensors of various shapes. The RNGD chip fully exploits parallelism and data reuse by flexibly adapting to each tensor contraction with software-defined tactics and supporting model-wise operator fusion.

            Uniquely designed for AI inference deployment, Furiosa TCP unlocks superior utilization, performance and energy efficiency.

            AI models structure data in tensors of various shapes. The RNGD chip fully exploits parallelism and data reuse by flexibly adapting to each tensor contraction with software-defined tactics and supporting model-wise operator fusion.

            Series RNGD

                RNGD-S 2025

                RNGD-S 2025

                Leadership performance for creatives, media and entertainment, and video AI

                RNGD Q3 2024

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                Versatile cloud and on-prem LLM and Multimodal deployment

                512 TFLOPS 48GB HBM3 Memory Capacity 1.5TB/s Memory Bandwidth 150W TDP

                RNGD-Max 2025

                RNGD-Max 2025

                Powerful cloud and on-prem LLM and Multimodal deployment

            Learn more about RNGD

              ENTERPRISE-READY & CLOUD-READY STACK

                Workflow Integration

                Our platform integrates seamlessly with your existing workflow, offering easy-to-use APIs, support for popular AI frameworks like PyTorch, ONNX, TensorFlow Lite, and compatibility with NumPy data structures.

                Model Optimization

                Leveraging our advanced compiler, we ensure your models achieve peak performance per watt. With our profiling tools identifying performance bottlenecks, we can further enhance model performance and efficiency.

                Flexible Deployment

                Furiosa hardware provides the flexibility to choose multiple processing elements (PEs) depending on your workloads. It’s designed for data center adaptability, incorporating containerization and Kubernetes, to facilitate the swift scaling of AI projects.

            Learn more about Furiosa SW

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<p>One-line installation and managed upgrade path for: PyTorch®, TensorFlow, CUDA, cuDNN, and NVIDIA Drivers</p><p></p>

</div></div>||||I||||                            Introducing 1-Click Clusters™, on-demand GPU clusters in the cloud for training large AI models. Learn more
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                        Introducing 1-Click Clusters

                        On-demand GPU clusters featuring NVIDIA H100 Tensor Core GPUs with Quantum-2 InfiniBand. No long-term contract required. Self-serve directly from the Lambda Cloud dashboard.

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                            NVIDIA H100 ON-DEMAND

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<div><div><div><p></p><h5>MI300A</h5></div><div><p></p><h5>Gaudi 2</h5></div><div><p></p><h5>H100</h5></div><div><p></p><h5>A100 40GB SXM</h5></div><div><p></p><h5>A100 40GB PCle</h5></div><div><p></p><h5>A100 80GB SXM</h5></div><div><p></p><h5>A100 80GB PCle</h5></div><div><p></p><h5>L40S</h5></div><div><p></p><h5>L40</h5></div><div><p></p><h5>L4</h5></div><div><p></p><h5>RTX A4000</h5></div><div><p></p><h5>RTX A5000</h5></div><div><p></p><h5>RTX A6000</h5></div><div><p></p><h5>A40 PCle</h5></div><div><p></p><h5>A30 PCle</h5></div><div><p></p><h5>A10 PCle</h5></div><div><p></p><h5>RTX 5000 ADA</h5></div><div><p></p><h5>RTX 6000 ADA</h5></div><div><p></p><h5>V100</h5></div><div><p></p><h5>T4</h5></div><div><p></p><h5>MI250</h5></div><div><p></p><h5>MI250X</h5></div><div><p></p><h5>MI300A</h5></div><div><p></p><h5>MI300X</h5></div></div><div><div><p>THE GPU CLOUD</p><h2>Benefits of training on a GPU cloud:</h2></div><div><div><p></p><div><h3>Accelerated Training Speed:</h3><p>GPU clouds facilitate rapid experimentation and iteration during the model development process. With quick access to powerful GPUs, you can experiment with different model architectures, hyperparameters, and training techniques, accelerating the discovery of optimal configurations for your AI model.</p></div></div><div><p></p><div><h3>Scalability:</h3><p>GPU cloud services offer scalability, allowing you to easily scale up or down based on the size and complexity of your dataset or model. This flexibility is particularly valuable when dealing with large datasets or when experimenting with different model architectures, as you can allocate resources according to your specific requirements.</p></div></div><div><p></p><div><h3>Model Development, Not Infrastructure Management:</h3><p>By utilizing a GPU cloud, you can offload the responsibility of managing and maintaining the underlying infrastructure to Inference.ai. This allows data scientists and developers to concentrate on model development, experimentation, and optimization without the distractions of hardware management.</p></div></div><div><p></p><div><h3>Access to Specialized GPU Hardware:</h3><p>GPU cloud providers often offer access to the latest and most powerful GPU hardware, including specialized GPUs designed for machine learning workloads. This ensures that your AI model benefits from state-of-the-art hardware capabilities, ultimately improving performance and efficiency.</p></div></div></div></div></div>||||I||||      HomeProductsInference VenturesNewsCareersContact Us
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        THE GPU CLOUD

      Benefits of training on a GPU cloud:

            Accelerated Training Speed:

            GPU clouds facilitate rapid experimentation and iteration during the model development process. With quick access to powerful GPUs, you can experiment with different model architectures, hyperparameters, and training techniques, accelerating the discovery of optimal configurations for your AI model.

            Scalability:

            GPU cloud services offer scalability, allowing you to easily scale up or down based on the size and complexity of your dataset or model. This flexibility is particularly valuable when dealing with large datasets or when experimenting with different model architectures, as you can allocate resources according to your specific requirements.

            Model Development, Not Infrastructure Management:

            By utilizing a GPU cloud, you can offload the responsibility of managing and maintaining the underlying infrastructure to Inference.ai. This allows data scientists and developers to concentrate on model development, experimentation, and optimization without the distractions of hardware management.

            Access to Specialized GPU Hardware:

            GPU cloud providers often offer access to the latest and most powerful GPU hardware, including specialized GPUs designed for machine learning workloads. This ensures that your AI model benefits from state-of-the-art hardware capabilities, ultimately improving performance and efficiency.

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