Discover the capabilities of the DeepSeek-R1 condensed advanced reasoning model and observe how it operates on Intel hardware.
DeepSeek has released simplified versions of its first-generation reasoning model, Deepseek-R1. The actual excitement is in its ability to translate advanced reasoning to a small language model (SLM), even though it may appear to be just another good model that claims to surpass all the current models on tackling complex jobs.
Large language models (LLMs) can occasionally be difficult for businesses to test or implement locally because of their high processing demands, intricate infrastructure requirements, and reliance on massive hardware accelerators. Because of this, apps frequently rely on external APIs, which provide a useful substitute and accommodate a variety of use cases. In other situations, the necessity to execute models locally is driven by elements like strategic priorities, operational limitations, or particular deployment requirements. Effective solutions that maximize accessibility, scalability, and performance are needed to handle these situations.
Using more easily accessible computing resources, SLMs with advanced reasoning capabilities seem to be able to overcome these obstacles by bridging the gap between large-scale performance and local or reasonably priced deployment. These compact yet incredibly powerful models provide sophisticated reasoning while preserving effectiveness and maximizing resource usage. They can be used by businesses as efficient answers for dependable, affordable, and scalable AI applications.
How Do Reasoning Models Work?
An LLM and a reasoning model produce different results. An Large language models (LLMs) optimizes for speed and fluency by producing its first token instantly based on statistical likelihood. Advanced reasoning models, on the other hand, might put off creation in order to schedule intermediate processes, giving logical correctness precedence over quick reaction.
| Feature | LLMs | Reasoning Models |
| Speed of first token | Fast | Delayed (due to planning) |
| Mechanism | Predicts token based on statistical likelihood | Uses intermediate reasoning before token selection |
| Fluency vs Accuracy | Prioritizes fluency | Prioritizes accuracy |
| Pattern vs Logical Steps | Patterns from data | Follows logical steps |
Stronger reasoning skills in SLMs result in better problem-solving, better code generation, and more dependable AI assistants driven by strategies like retrieval-augmented generation (RAG), making this distinction relevant.
This difference is best illustrated by DeepSeek-R1’s novel Group Relative Policy Optimization (GRPO) technique, which assesses and enhances its results independently of conventional external reward models. DeepSeek-R1 also uses chain-of-thought (CoT) reasoning, which enables the model to deconstruct complicated tasks into manageable, logical steps, producing outputs that are more accurate and visible.
Advanced reasoning model
Comparing Llama 3.1 8B with DeepSeek 8B
Decided to give it a go because intel is not spectators! It selected an AI PC with an Intel Core Ultra 7 CPU, which provides great performance-per-watt AI inference, so small models may operate on laptops. To used an AI PC notebook with 32 GB of RAM.
A large, high-performing model (the “teacher,” DeepSeek-R1) imparts its knowledge to a small model (the “student,” LLaMA architecture) through knowledge distillation. This procedure makes it faster and more useful for local use by minimizing size and computational demands while maintaining reasoning ability to the greatest extent feasible. It used a logic-based inquiry to compare its performance with that of Llama 3.1-8B, a top general-purpose LLM in the same parameter size class that requires comparable deployment resources.
Llama 3.1 8B made a logical error that resulted in the incorrect conclusion, but DeepSeek-R1 used careful thinking to obtain the solution correctly. The response time for an answer may be impacted by DeepSeek-R1‘s lengthier time to reach a decision. For enterprise use cases, this will be important since the model comprehends the prompt with context, responds promptly with accurate and contextually aware solutions, and integrates easily into production settings without sacrificing efficiency or scalability.
Why Use a Small Language Model?
Despite the strong reasoning outcomes, it goes beyond simply having a model to tackle “logic problems.” A model that strikes a balance between speed, accuracy, and reasoning power is the best option for businesses. It guarantees that it satisfies application requirements while optimizing the vast array of computing resources that are generally accessible throughout an organisation, ranging from CPUs to specialized accelerators.
Organizations can easily deploy, optimize, and scale AI models across a variety of environments, including cloud and edge devices, as well as on-premises infrastructure, even when it comes to application deployment, where open source frameworks like Open Platform for Enterprise AI (OPEA) are necessary.
There are two important lessons for businesses to remember:
- Accuracy/Reasoning: In certain use circumstances, an SLM that performs exceptionally well on reasoning tasks might take the role of much bigger models (such as 70B Parameters), increasing the effectiveness of AI systems.
- Hardware requirements: SLMs can operate on standard PCs and are known to be lightweight. This opens up a variety of deployment options, such as more effective external APIs and local deployments, in addition to allowing developers to test their AI apps on their laptops.
You must take into account the scalability of your model while implementing it for your AI application. That is, how many requests will it process and how many users will engage with it?
Your AI applications will require increasingly powerful hardware as their utilization levels increase. SLM inference can be performed using Intel Xeon Scalable processors, which are widely accessible and can be used in cloud, on-site datacenter, or external APIs. You can add specialized AI accelerators, like Intel Gaudi, for more capacity.
The ability to create and test extremely powerful AI applications using SLMs on local AI PCs and have the option to deploy to easily accessible datacenter or PC-based compute resources is quite revolutionary.
In conclusion
Its performance varied throughout it testing, though, indicating areas that could use more improvement. In its work, DeepSeek-R1: Incentivizing Reasoning Capability in LLMs by Reinforcement Learning, DeepSeek recognizes these limitations. In spite of this, DeepSeek-R1 has made significant strides. It brings developers closer to cutting-edge technology and is a step towards developing AI that is more affordable.
Have You Tried It? Experience Intel Hardware’s DeepSeek
Explore DeepSeek for yourself on these Intel platforms, which are based on the assumption that the optimal solution strikes a balance between speed, accuracy, and reasoning power to fulfil enterprise expectations while minimizing processing time and resource usage:
AI PC: Intel Tiber AI Cloud makes AI-powered PC capabilities accessible, facilitating effective on-device AI workloads with enhanced responsiveness and performance.
Intel Xeon Scalable Processors: Several cloud service providers provide Xeon processors, which are built for high-performance computing. Additionally, for improved scalability and workload optimization, Intel Tiber AI Cloud provides access to state-of-the-art Xeon-based AI infrastructure.
Accelerators for Intel Gaudi 2 AI: Gaudi 2 is designed to enhance the price-performance ratio of deep learning and is best suited for both training and inference in large-scale artificial intelligence models.
