Quicker, smarter, extra responsive AI functions – that’s what your customers count on. However when giant language fashions (LLMs) are gradual to reply, consumer expertise suffers. Each millisecond counts.
With Cerebras’ high-speed inference endpoints, you possibly can cut back latency, velocity up mannequin responses, and keep high quality at scale with fashions like Llama 3.1-70B. By following a number of easy steps, you’ll have the ability to customise and deploy your individual LLMs, supplying you with the management to optimize for each velocity and high quality.
On this weblog, we’ll stroll you thru you the right way to:
- Arrange Llama 3.1-70B within the DataRobot LLM Playground.
- Generate and apply an API key to leverage Cerebras for inference.
- Customise and deploy smarter, quicker functions.
By the top, you’ll be able to deploy LLMs that ship velocity, precision, and real-time responsiveness.
Prototype, customise, and take a look at LLMs in a single place
Prototyping and testing generative AI fashions usually require a patchwork of disconnected instruments. However with a unified, built-in surroundings for LLMs, retrieval strategies, and analysis metrics, you possibly can transfer from thought to working prototype quicker and with fewer roadblocks.
This streamlined course of means you possibly can deal with constructing efficient, high-impact AI functions with out the effort of piecing collectively instruments from completely different platforms.
Let’s stroll via a use case to see how one can leverage these capabilities to develop smarter, quicker AI functions.
Use case: Dashing up LLM interference with out sacrificing high quality
Low latency is important for constructing quick, responsive AI functions. However accelerated responses don’t have to come back at the price of high quality.
The velocity of Cerebras Inference outperforms different platforms, enabling builders to construct functions that really feel clean, responsive, and clever.
When mixed with an intuitive growth expertise, you possibly can:
- Cut back LLM latency for quicker consumer interactions.
- Experiment extra effectively with new fashions and workflows.
- Deploy functions that reply immediately to consumer actions.
The diagrams under present Cerebras’ efficiency on Llama 3.1-70B, illustrating quicker response occasions and decrease latency than different platforms. This permits speedy iteration throughout growth and real-time efficiency in manufacturing.
How mannequin measurement impacts LLM velocity and efficiency
As LLMs develop bigger and extra complicated, their outputs turn into extra related and complete — however this comes at a price: elevated latency. Cerebras tackles this problem with optimized computations, streamlined information switch, and clever decoding designed for velocity.
These velocity enhancements are already reworking AI functions in industries like prescribed drugs and voice AI. For instance:
- GlaxoSmithKline (GSK) makes use of Cerebras Inference to speed up drug discovery, driving increased productiveness.
- LiveKit has boosted the efficiency of ChatGPT’s voice mode pipeline, attaining quicker response occasions than conventional inference options.
The outcomes are measurable. On Llama 3.1-70B, Cerebras delivers 70x quicker inference than vanilla GPUs, enabling smoother, real-time interactions and quicker experimentation cycles.
This efficiency is powered by Cerebras’ third-generation Wafer-Scale Engine (WSE-3), a customized processor designed to optimize the tensor-based, sparse linear algebra operations that drive LLM inference.
By prioritizing efficiency, effectivity, and suppleness, the WSE-3 ensures quicker, extra constant outcomes throughout mannequin efficiency.
Cerebras Inference’s velocity reduces the latency of AI functions powered by their fashions, enabling deeper reasoning and extra responsive consumer experiences. Accessing these optimized fashions is straightforward — they’re hosted on Cerebras and accessible through a single endpoint, so you can begin leveraging them with minimal setup.
Step-by-step: Methods to customise and deploy Llama 3.1-70B for low-latency AI
Integrating LLMs like Llama 3.1-70B from Cerebras into DataRobot means that you can customise, take a look at, and deploy AI fashions in only a few steps. This course of helps quicker growth, interactive testing, and higher management over LLM customization.
1. Generate an API key for Llama 3.1-70B within the Cerebras platform.
2. In DataRobot, create a customized mannequin within the Mannequin Workshop that calls out to the Cerebras endpoint the place Llama 3.1 70B is hosted.
3. Inside the customized mannequin, place the Cerebras API key throughout the customized.py file.
4. Deploy the customized mannequin to an endpoint within the DataRobot Console, enabling LLM blueprints to leverage it for inference.
5. Add your deployed Cerebras LLM to the LLM blueprint within the DataRobot LLM Playground to begin chatting with Llama 3.1 -70B.
6. As soon as the LLM is added to the blueprint, take a look at responses by adjusting prompting and retrieval parameters, and examine outputs with different LLMs immediately within the DataRobot GUI.
Broaden the bounds of LLM inference to your AI functions
Deploying LLMs like Llama 3.1-70B with low latency and real-time responsiveness is not any small job. However with the appropriate instruments and workflows, you possibly can obtain each.
By integrating LLMs into DataRobot’s LLM Playground and leveraging Cerebras’ optimized inference, you possibly can simplify customization, velocity up testing, and cut back complexity – all whereas sustaining the efficiency your customers count on.
As LLMs develop bigger and extra highly effective, having a streamlined course of for testing, customization, and integration, will likely be important for groups trying to keep forward.
Discover it your self. Entry Cerebras Inference, generate your API key, and begin constructing AI functions in DataRobot.
Concerning the writer
Kumar Venkateswar is VP of Product, Platform and Ecosystem at DataRobot. He leads product administration for DataRobot’s foundational companies and ecosystem partnerships, bridging the gaps between environment friendly infrastructure and integrations that maximize AI outcomes. Previous to DataRobot, Kumar labored at Amazon and Microsoft, together with main product administration groups for Amazon SageMaker and Amazon Q Enterprise.
Nathaniel Daly is a Senior Product Supervisor at DataRobot specializing in AutoML and time sequence merchandise. He’s centered on bringing advances in information science to customers such that they’ll leverage this worth to resolve actual world enterprise issues. He holds a level in Arithmetic from College of California, Berkeley.
