Consider Alternate Approaches to Language AI Development and Deployment
Enterprises, ISVs, and other technology organizations are seeking ways to make AI innovative and achievable. Language AI model development and deployment have traditionally relied on large language models (LLMs) supported by servers and workstations with discrete GPUs or other specialized hardware. However, the effort and infrastructure required to enable these types of solutions often prove prohibitive for many organizations.
As a result, pragmatic innovators are opting for SLM-based solutions. SLMs are lightweight and focused models that can enable domain-specific, language-based applications like chatbots more efficiently. To achieve even more cost-effectiveness, these SLM innovators are exploring how they can run SLM workloads on CPU-only architectures—whether deployed in the cloud, in an on-premises data center, or at the edge.
To help you better understand how to enable domain-specific language AI more efficiently, let’s examine what makes the combination of SLMs and AI-ready CPUs such as Intel® Xeon® processors so powerful.
Simplify Language AI Solutions with SLMs
For businesses prioritizing efficiency, privacy, and cost-effectiveness, SLMs provide an excellent route to AI capabilities. In contrast to LLMs, which are sprawling and general purpose, SLMs are compact AI models designed to perform specific tasks efficiently. As a result, they require less computational power and data during each stage of the AI pipeline. Examples of popular SLMs include Mistral 7B and the Llama 3.2 collection.
Efficiency and Cost Benefits
Typically, SLMs are derived from LLMs through techniques such as distillation and pruning. Because SLMs involve less data, they can be trained and retrained frequently without incurring significant electricity or cloud resource costs. This flexibility can help you fine-tune and refine your model’s performance without consuming too much budget or schedule.
Security and Privacy Benefits
Additionally, SLMs offer privacy and security benefits. Because of their smaller training data needs and less widespread use, SLMs are less likely to ingest and retain sensitive information. The smaller dataset and simpler architecture make it easier to explain results and identify biases or hallucinations. Since they require fewer resources, SLMs also present a smaller attack surface area for cybersecurity threats.
Domain-Specific AI Benefits
Since SLMs are built on smaller, more focused datasets, they’re well suited for use in domain-specific applications. Training on a dataset that’s built for a specific industry, field, or company helps SLMs develop a deep and nuanced understanding that can lower the risk of erroneous outputs. The tighter focus also facilitates optimizations for metrics such as task completion rate and accuracy. Plus, lower data and training requirements for SLMs can translate to fast turnaround times and expedited ROI.
Maximize Efficiency with SLMs on CPUs
SLMs and AI-ready CPUs can be used together to provide a lightweight, cost-efficient solution for real-world language AI implementation without sacrificing performance. Using CPUs rather than GPUs or other specialized hardware for small language models can minimize costs, complexity, and resource consumption.
For example, servers based on the latest Intel® Xeon® processors, 4th generation and newer, allow users to run SLMs on a CPU-only architecture affordably and privately with low latency. Because of their flexibility and performance, using these processors for small language models provides a particularly attractive route for enabling SLM applications in an on-premises deployment, which can be preferred when facing especially strict data security needs.
Integrated Accelerators in Intel® Xeon® Processors
Intel® Xeon® 4, 5, and 6 processors also offer the integrated Intel® Advanced Matrix Extensions (Intel® AMX) accelerator, which combines with increased memory bandwidth to enhance computational efficiency for SLMs. A smaller model size also means full applications can run on a single Intel® Xeon® processor-based node, significantly reducing costs and providing excellent latency and throughput.
Intel® AMX improves the performance of deep learning (DL) training and inference, making it ideal for workloads like natural language processing. You can code AI functionality to take advantage of the Intel® AMX instruction set or code non-AI functionality to use the processor instruction set architecture.
It’s also important to note that the latest Intel® Xeon® processors provide a range of built-in optimizations and acceleration engines beyond Intel® AMX, supporting several use cases such as security and networking.
- Read more about Intel® Advanced Matrix Extensions (AMX).
- Learn about integrated Intel® Accelerator Engines.
Llama 3.2 3B on Intel® Xeon® Processors
Benchmarking results demonstrate that running Llama 3.2 3B with an input of 1,024 tokens and an output of 128 tokens on 5th Gen Intel® Xeon® processors and Intel® Xeon® 6 P-core processors can achieve remarkable throughputs while maintaining a next-token latency of under 50ms (P99).1
Microsoft Phi-3 on Intel® Xeon® Processors
The Phi-3 family of SLMs offers capable, cost-effective options for building generative AI (GenAI) applications. Benchmarking of Phi-3-medium 4K and 128K variants shows that Intel® Xeon® processors are a performant option for LLM inference deployment.2
Evaluate Your SLM and CPU Opportunities
SLMs running on CPUs offer a viable, cost-efficient, accurate, and secure path for making language AI and domain-specific models more practical for your organization to implement.
Additionally, your path to running SLMs on a CPU architecture—including Intel® Xeon® processors— may be more direct than you expect.
Here are four steps you can take today to begin evaluating your SLM on CPU options:
- Assess your current investments with your infrastructure team. Many organizations own Intel Xeon processor-based servers, and refreshing your existing infrastructure with a migration to Intel Xeon 6 processors with Intel AMX can yield tremendous TCO benefits for SLMs.
- Consult your cloud provider. Intel® Xeon® processor-based instances with the Intel® AMX accelerator are available from any major cloud provider and ready for you to take advantage of.
- Discuss options with your technology partners. Intel® partners are ready to help you get the most out of our technologies, including Intel® Xeon® processors, for small language models from edge to cloud.
- Find out how easy it is to port existing AI applications to CPU architectures. Intel offers a range of development tools, including the OpenVINO™ toolkit, that enable you to write code once and deploy it anywhere.