Provider Spotlight: Groq

Groq is offering unprecedented speeds for LLM inference. This spotlight post explores Groq's unique new chip technology and how transitioning to open-source models can unlock its potential to dramatically improve the user experience of your applications LLM tasks.

Who are Groq?

Groq is an LLM API provider and hardware company based in Mountain View that has developed a novel AI chip designed specifically for ML inference called the Language Processing Unit™ (LPU™). Their hardware and software is specifically designed to deliver exceptional compute speed for ML inference.

Groq was founded by Jonathan Ross, who was heavily involved in the development of the Tensor Processing Unit (TPU) at Google. The TPU was a custom-built AI chip that was designed to significantly accelerate machine learning training and inference to help support the development of new neural network projects on the TensorFlow framework. Realising the importance of this technology, he left in 2016 to found Groq, a company with the mission of designing and manufacturing new hardware specifically to power ML workloads.

Key features of Groq's technology include:

1. LPU™ Inference Engine: A new class of processor optimized for sequential workloads like language processing.
2. GroqCloud™: Their main offering is a cloud LLM API service powered by a network of LPUs, offering access to popular open-source LLMs.
3. GroqRack™ and GroqNode™: In the past, Groq had also explored directly selling hardware solutions for large-scale, low-latency deployments which were to be employed by enterprise companies looking to control their data while speeding up their applications.

The Speed Advantage

Groq claims to offer significantly faster inference speeds compared to traditional GPU-based solutions, and this has been well demonstrated through their public GroqCloud demo's and deployments in live applications today.

• Up to 18x faster performance for models like Meta AI's Llama 2 70B compared to other leading providers.
  • You can try their demo on GroqCloud today and find a 300 token per second response on the 70b models, more than 3x faster than a standard GPT-4o response.
• Exceptionally low end-to-end latency of just 1.6μs within a GroqRack.
• Near-linear scalability across multiple servers and racks without the need for external switches.

Transitioning from Closed to Open-Source Models

To leverage Groq's speed advantages, organizations need to use open-weights models that are available to be run on Groq's infrastructure. Here's how transitioning from closed-source models like GPT or Claude to open-source alternatives can enable the use of Groq:

1. Identify Open-Source Alternatives:
   • For GPT 4 or Claude Sonnet 3.5 like tasks, consider models like Meta's Llama 3.1 70b or 405b.
   • For GPT 3.5 level tasks you can consider the Mistral 8x7b model, or a Llama 8b model.
2. Evaluate, optimize, and fine-tune:
   • Test these open-source models on your specific use cases.
   • Start improving prompts to cover new failure cases when moving down in model. strength
   • Provide better live context by augmenting your system through search and RAG systems
   • Fine-tune the models to match or exceed the performance of closed-source alternatives.
3. Migrate to Groq:
   • Once you've successfully transitioned to an open-source model, you can deploy it on Groq's infrastructure in a 1 line code change using their SDK or API which matches the OpenAI interface.

Impact on User Experience

The speed improvements offered by Groq can significantly enhance user experience:

1. Near-Instantaneous Responses: With latencies as low as 1.6μs, users can experience almost real-time interactions with AI models.
2. Increased Throughput: Faster inference means handling more requests in less time, reducing wait times during peak usage.
3. More Complex Interactions: The speed boost allows for more back-and-forth exchanges within the same time frame, enabling more sophisticated AI interactions.
4. Improved Accessibility: Lower latency can make AI-powered tools more accessible to users with slower internet connections.
5. Enhanced Mobile Experience: Faster responses are particularly beneficial for mobile users, where every millisecond counts.

Real-World Example

Let's consider a scenario where a you transition from GPT-4o to Llama 3 70B on Groq:

• Original setup: GPT-4o with an average response time of 2.4s at 80 tokens per second
• New setup: Llama 3 70B on Groq with a response time of 650ms at over 300 tokens per second

In this scenario, the user experience transforms from a noticeable half-second delay to an almost instantaneous response, making the AI interaction feel much more natural and fluid.

Conclusion

Groq's innovative LPU technology offers a compelling option for organizations looking to dramatically improve the speed and efficiency of their LLM deployments. By transitioning from closed-source models to open-source alternatives, companies can tap into Groq's infrastructure and potentially achieve significant improvements in user experience.

However, it's important to note that such a transition requires careful evaluation, testing, and potentially fine-tuning to ensure that the new setup meets or exceeds the performance of the original system in all critical aspects, not just speed.

As the AI landscape continues to evolve, solutions like Groq highlight the importance of staying adaptable and open to new technologies that can provide competitive advantages in delivering AI-powered services.

Stay tuned for more updates in our Provider Spotlight series to understand the background, mission, and capabilities of each provider in the space.