#developercloud

[govindhtech]

kAI: A Mexican AI Startup, Improves The Everyday Activities

Mexican AI

kAI, a Mexican AI startup, simplifies and improves the convenience of managing daily tasks.

kAI Meaning

“Künstliche Intelligenz” (German for “Artificial Intelligence”) refers to AI technology, techniques, and systems. The word “kAI” may refer to AI-based solutions that use machine learning, data analysis, and other AI methods to improve or automate activities.

AI startup business kAI is based in the technological center of Mexico and is creating an AI-powered organizing software called kAI Tasks. With the help of this software, users can easily arrange their personal days and focus their efforts on the things that really important. With kAI, creating an agenda takes less than a minute because of artificial intelligence’s intuitive capabilities. WatchOS-based smartwatches, tablets, and smartphones running Android and Apple can all use kAI Tasks.

The Problem

In an environment where there are always fresh assignments and meetings, being productive is crucial. Regrettably, rather of increasing user productivity, existing to-do apps actually decrease it. Either important functionality is missing, the user experience is not straightforward enough, or the system does not support the users’ regular daily chores.

The Resolution

The mobile task management software from kAI makes it simple for end users to plan, schedule, and arrange their workdays. Compared to conventional to-do management apps and tools, this can be completed in a fraction of the time because of artificial intelligence.

Block planning appears on one screen daily when using kAI Tasks.Image Credit To Intel

The following are a few of the benefits and features that make the tool so alluring:

Intelligent task management: kAI provides tailored recommendations and reminders to help you stay on track by learning from end users’ behaviors and preferences.

Easy event planning: Arrange agendas and schedules with ease, freeing you time to concentrate on the important things.

Constant adaptation: The more you use the tool, the more it learns about your requirements and adjusts accordingly, personalizing your everyday experience.

AI Tasks may be tailored to the requirements of the final user

To optimize everyday objectives, kAI Tasks may be used in conjunction with a smartphone or wristwatch. The end user may easily control his or her productivity and maintain organization with this configuration.

By the end of September 2024, kAI hopes to provide additional features including wearables and the creation of a bot for Telegram and WhatsApp, among other things. With the aid of these connections, the business will be able to expand its user base and make everyday job organization easier without requiring the usage of another software.

“The foundation of an excellent lifestyle is personal organization. They are redefining time and task management at kAI. Its modern equipment boosts productivity, well-being, and stress reduction. You may easily accomplish your business and personal objectives with kAI while maintaining the ideal balance in your life. According to Kelvin Perea, CEO of kAI, “All of us can even do more in less time because their company is a part of the Intel Liftoff Program.”

kAI chores, which is compatible with almost all smart devices, makes it simple to arrange daily chores. Task management is made more simpler and more straightforward with the aid and assistance of AI, as the software gradually learns the end user’s behavior.

Are you prepared to further innovate and grow your startup? Enroll in the Intel Liftoff program right now to become a part of a community that is committed to fostering your ideas and promoting your development.

Intel Liftoff

Liftoff for Startups using Intel

Take Down Code Barriers, Release Performance, and Turn Your Startup Into a Scalable, AI Company that Defines the Industry.

Early-stage AI and machine learning businesses are eligible to apply for Intel Liftoff for startups. No matter where you are in your entrepreneurial career, this free virtual curriculum supports you in innovating and scaling.

Benefits of the Program for AI Startups

Startups may get the processing power they need to address their most pressing technological problems with Intel Liftoff. The initiative also acts as a launchpad for collaborations, allowing entrepreneurs to improve customer service and strengthen one other’s offers.

Superior Technical Knowledge and Instruction

Availability of the program’s Slack channel

Free online seminars and courses

Engineering advice and assistance

Reduced prices for certification and training

Invitations to forums and activities with experts

Advanced Technology and Research Resources

Offers for Intel Developer Cloud free cloud credits

Cloud service provider credits

Availability of Intel developer tools, which provide several technological advantages

Use the Intel software library to access devices with next-generation artificial intelligence

Opportunities for Networking and Comarketing

Boost consumer awareness using Intel’s marketing channels.

Venture exhibitions at trade shows

Introductions at Intel around the ecosystem

Establish a connection with Intel Capital and the worldwide venture capital (VC) network

Developer Cloud Intel Tiber

Take down the obstacles to hardware access, quicken development times, and increase your AI and HPC processes’ return on investment (ROI).

Register to get instant access to the newest Intel software and hardware innovations, enabling you to write, test, and optimize code more quickly, cheaply, and effectively.

AI Pioneers Who Discovered Intel Liftoff for Startups as Their Launchpad

Their companies are breaking new ground in a variety of AI-related fields. Here’s how they sum up their time in the program and the benefits they’ve received in terms of improved performance.

Enabling businesses to develop and implement vision AI solutions more quickly and consistently

By processing crucial machine learning tasks with AI Tools, the Hasty end-to-end vision AI platform opens up new AI use cases and makes application development more approachable.

“Using Intel OneAPI to unlock computationally demanding vision AI tasks will be a stepwise shift for critical industries like disaster recovery, logistics, agriculture, and medical.”

Use particle-based simulation tools to assist engineers in creating amazing things

Using the Intel HPC Toolkit and the Intel Developer Cloud, Dive Solutions improves their cloud-native computational fluid dynamics simulation software for state-of-the-art hardware.

“It’s used parts from the Intel HPC Toolkit to optimize their solver performance on Intel Xeon processors in an economical manner. The workloads are currently being prepared to execute on both CPU and GPU architectures.

Using a hyperconverged, real-time analytics platform to address the difficulties posed by big data

Using oneAPI, the Isima low-code framework optimizes for cost and performance in the cloud while enabling real-time use cases that drastically shorten time-to-value.

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About - DigitalOcean – The developer cloud

About – DigitalOcean – The developer cloud

About – DigitalOcean – The developer cloud Website hostingEasily and reliably host a website for your business while keeping complete control of the underlying infrastructure. StreamingWe provide the combination of flexible compute layers with low-bandwidth pricing that make building a streaming service easy for your developers – and cost-efficient for your business. About your developers –…

[pty-ltd]

Natural Language Processing

Natural-language processing (NLP) is an area of computer science and artificial intelligence concerned with the interactions between computers and human (natural) languages, in particular how to program computers to fruitfully process large amounts of natural language data. https://en.wikipedia.org/wiki/Natural-language_processing

Considering that my INPUT is a large corpus of text, it would be interesting to perform some kind of NLP on it and see what kind of results that would output. This is what a computer saw when I plugged in a few of my messages.

Breaking down Parts of Speech is a common use for NLP. Another common use is sentiment analysis, which is a way to assess how “negative” or “positive” a statement may be.

There are many online services and packages that will perform this sort of analysis

SpaCy (Python)

Google NLP (web api)

Microsoft Azure (web api)

IBM Watson (web api)

Indico (web api)

RiTa (java and javascript)

They all mostly do much the same thing, whith the exception of Indico and RiTa. Indico offered a few other quirky endpoints for analysis, such as the political leanings of a statement (conservative or liberal). RiTa on the other hand doesn't analyse anything but is instead a generative text library.

[everywareproject]

Using IBM Watson

Speech to text:

Watson speech to text service is built to work with other services such as the Tone analyser.

The speech to text service can recognise and tell apart different speakers, which is useful when multiple wearers are together. The service by default includes keyword spotting and profanity filtering, which can be disabled if required. The speech to text service can be customized (beta acoustic model customisation).

The service takes input from either pre-recorded or live inputs; so you can speak to it much like you would speak to Google now or Siri.

The text is outputted as a text transcript in a JSON file, with highlighted recognised words if required.

The service is limited to 100 minutes per month on a student account.

 Source: https://www.ibm.com/watson/developercloud/speech-to-text/api/v1/

Tone Analysis:

The tone analyser service is built to work with the speech to text service as well as direct text inputs.

Unfortunately the tone analyser can’t be edited like the image recognition service, but this could be worked around.

The service takes input as a text file, which can be full sentences or not, and outputs both an ID for the tone and a percentage score. Both this ID and the percentage could be used to affect the colours used in the T-shirt’s response.

The service is limited to 2,500 api calls per month on a student account.

Source: https://www.ibm.com/watson/developercloud/tone-analyzer/api/v3/?python#post-tone

Watson general purpose tones:

The Watson tone analyser has a default output, this consists of both Emotional tone & Language tone outputs:

Anger (emotional tone)

Fear (emotional tone)

joy (emotional tone)

Analytical (Language Tone)

sadness (emotional tone)

confident (language tone)

tentative (Language tone)

[jtenney4-blog]

Data Mining Tools SWOT Analysis

SAS        IBM SPSS Modeler        Knime       Revolution Analytics (Microsoft)

Both SAS (Fact Sheet, 2016) and SPSS (What’s New, 2015) have advanced data preparation tools that find missing values, filter outliers, and segment in a high-level manner.  Both have industry specific predictive analytics solutions. However, IBM has deep industry consulting experience to add to their industry package options which their customers cite as a main feature of interest.  SAS is remaining an industry leader, while IBM is widely recognized for its innovation their sales have decreased (Gartner, 2016).

North Carolina based industry leader, SAS (2016) is claimed to have the strongest UI, the broadest range of analytic solutions, and the widest customer base through vast market permeation. While SAS is revered the ‘gold-standard’ due to its functionality, supported technologies, and continued innovation, customers also find the need to switch between so many programs with overlapping technologies confusing.  This lack of transparency and high costs are a couple of reasons organizations and scientist look to alternative solutions (Gartner, 2016). The Excel add-on is a popular feature among users and developers and is a testament to the strength of a familiar interface.  The neural networks of this platform are impressive and it seems the SAS portfolio may be larger than IBMs in that it is able to retain its own history and metadata that is incorporated into algorithms.  Training and support within and outside the platform appear to be a major strength for this option (Getting Started, 2015).  

IBM, a New York based company, has leverage behind its Watson Conversation technology (Gartner, 2016) that uses cognitive technologies to simulate conversation by bot (Watson, 2017). Many customers state this technology confuses the tech giants place in the analytics field, many of the individual pieces do not fit together, and sales personnel are not properly trained to assist with the feature.  This lack of support and training may correlate with the overall in commercial licenses in the IBM analytics portfolio as a whole.  Fortunately, IBM is implementing customer participation techniques to improve in this realm (Gartner, 2016).  

The Swiss company, Knime, holds expertise in the life sciences, government, and service industries. Nonetheless, this open source, cloud optional solution is deployable across enterprises in all industries. Knime has held the crown for customer satisfaction due to its flexibility, openness, and ease of integration.  User interface for this platform is highly outdated and users must go outside of the platform for decent visualizations (Gartner, 2016).  Although this end-to-end analytics tool is useful for its diversity in functionality and integration (Knime, 2016), the supported techniques within the platform leave much room for improvement.

Microsoft recently acquired Revolution Analytics which was combined with Azure Machine Learning and the Cortana Analytics Suite to form the most comprehensive SQL Server Analysis Services (SSAS), despite still being considered a visionary company (Gartner, 2016). Cortana not only remains the strongest cloud analytics solution, it also has a partner ecosystem exceeding others in the industry.  Customers rave over Microsoft’s support and integration of open-source technologies to offer any functionality or technique required.  However, many customers remain using simply SSAS without AML or RA while citing limitations.  Being a strict cloud offering is a major limitation for the Cortana solution though (Gartner, 2016).  R technologies are used for SQL Server, which is optional but not required of other solutions. Data movement is extremely limited on this platform which differs from others in a positive sense.  Proactive alerting and prescriptive recommendations are a huge strength of the Cortana solution. Similar to IBM’s Watson technologies, Cortana allows for building of bots and intelligent agents for an interactive experience and even handling communications and social networks.  Vision, speech, and text recommendations as well as face and emotion detection to improve business can also extraordinarily be built in (Cortana, 2017).  

All of the aforementioned cover each portion of the data mining process.  Some are stronger in certain steps than others and some have to integrate other solutions to complete all steps.  It is good to have a working knowledge of the entire ‘toolbox’ available for different business applications.  The business requirements and end user goals will truly determine the tools and solutions.  

References

Cortana Intelligence Suite. (2017). Microsoft. Retrieved from https://www.microsoft.com/en-us/cloud-platform/why-cortana-intelligence-suite

Getting Started with SAS Enterprise Miner: Setting up an Enterprise. (2015). YouTube. Retrieved from https://youtu.be/489wJm2X0TY

Knime Analytics Platform. (2016). Knime. Retrieved from https://www.knime.org/knime-analytics-platform

Magic Quadrant for Advanced Analytics Platforms. (2016). Gartner. Retrieved from https://www.gartner.com/doc/reprints?id=1-2YEIILW&ct=160210&st=sb

SAS Enterprise Miner. (2016). Data Mining Software, Model Development and Deployment. Retrieved from http://www.sas.com/en_us/software/analytics/enterprise-miner.html#m=demo1

SAS Enterprise Miner: Fact Sheet. (2016). SAS. Retrieved from http://www.sas.com/content/dam/SAS/en_us/doc/factsheet/sas-enterprise-miner-101369.pdf

SQL Server. (2017). Microsoft. Retrieved from https://www.microsoft.com/en-us/sql-server/

Watson Developer Cloud. (2017). IBM. Retrieved from https://www.ibm.com/watson/developercloud/conversation.html

What’s New in IBM SPSS Modeler. (2015). YouTube. Retrieved from https://youtu.be/otkLJ5xE1cw

[govindhtech]

Intel oneAPI DPC++/C++ Compiler JPEG Image Compression

Discrete Cosine Transform DCT

Discrete Cosine Transform (DCT) with SYCL for GPU-Based JPEG Image Compression. Use the  Intel oneAPI DPC++/C++ Compiler to accelerate concurrent picture compression.

Image compression reduces digital image files without compromising quality. Eliminating superfluous and duplicated data simplifies image storage and transmission over the internet or other networks.

The oneAPI GitHub repository has a code sample for the Discrete Cosine Transform, which is discussed in this blog. It shows how to use SYCL and the Intel oneAPI DPC++/C++ Compiler to create the Discrete Cosine Transform (DCT), an irreversible picture compression method for JPEG images.

Discrete cosine transform for image compression

Let’s expand on their discussion of picture compression before getting into the specifics of the code sample.

Applications of image compression in the real world include:

Digital photography to share and store high-resolution photos taken using cameras in an effective manner

Consumer electronics to reduce data usage and storage capacity on mobile devices, such as tablets and smartphones.

Medical imaging to transfer and store medical images efficiently while maintaining image quality for accurate diagnosis.

Video surveillance to effectively store and transfer photos taken by surveillance systems by compressing them using cloud services.

Web development to enhance user experience and save bandwidth consumption by enabling quicker image loading times on websites.

Discrete Cosine Transform Example

Two categories of image compression methods exist

Lossless compression: This method ensures image quality and accurate image reconstruction from compressed data. PNG, GIF, and TIFF are prominent lossless image formats.

Lossy compression: This method permanently destroys image data, making image reconstruction impossible. JPEG and WebP are popular lossy compression formats.

Lossy compression algorithms often translate the image into a frequency domain before quantizing the frequency components using mathematical approaches like the DCT.

Advantages of Discrete Cosine Transform

Because it tends to concentrate the image signal information in a few low-frequency components, the DCT image compression approach is advantageous. This facilitates the attainment of large compression ratios without sacrificing visual quality.

The loss of image quality resulting from the DCT compression process can be rendered undetectable to the human eye while achieving a large reduction in file size through the careful application of quantization.

Now let’s talk about the Discrete Cosine Transform code sample and how SYCL-based GPU offload can be used to accelerate compression utilising the Intel oneAPI DPC++/C++ Compiler.

Overview of the Intel oneAPI DPC++/C++ Compiler

A high-performance, LLVM-based compiler that complies with industry standards, the  Intel oneAPI DPC++/C++ Compiler aids in the compilation of ISO C/C++ and SYCL applications on a variety of architectures. It is the first compiler in the world to support the most recent version of the SYCL 2020 specification. It supports OpenMP and OpenCL in addition to SYCL and other accelerated parallel computing frameworks.

It is intended to work in harmony with oneAPI libraries, such oneDPL and oneTBB, and to take advantage of them for offloading computation acceleration and optimized parallel execution. Code reusability across heterogeneous hardware platforms, such as  CPUs, GPUs, and FPGAs, is made possible by these design qualities.

Discrete Cosine Transform Applications

Concerning The Sample Discrete Cosine Transform Code

The input image is first quantized and the Discrete Cosine Transform (DCT) is applied by the code sample. The resulting intermediate image is then subjected to inverse DCT and de-quantization to yield an output BMP image. This image will be utilized to evaluate the amount of image information lost as a result of the DCT compression method.

DCT Phase

Each pixel’s colour value is stored in the image’s pixel representation. A sum of several cosine functions is used to depict the colour pattern of image subsets. Eight by eight subsections, or “blocks” in the code example, are used to process the image. Only 8 discrete cosine functions can be used to depict an 8×8 image. All that is needed to reconstruct the image from the cosine representation are the coefficients connected to each cosine function. The DCT procedure converts the input image’s 8×8 pixel matrix into an equivalent 8×8 matrix of coefficients.

Step of Quantization

The image data can be compressed thanks to the quantization procedure. The cosine functions that are most pertinent to picture data are ranked in order using a quantizing matrix. If read diagonally (as recorded in the memory), the matrix acquired after DCT is divided by the quantizing matrix, yielding a sequence of integers followed by multiple zeroes. The original image can be compressed because of the long string of zeroes.

Steps for De-quantization and Inverse DCT

The code sample then re-produces the raw image data by performing inverse DCT and de-quantization before writing the quantization output to a file. The final image will not be a reduced version of the original because to the inverse processes. It will, however, reveal the artefacts brought about by an irreversible compression technique such as DCT.

SYCL

SYCL-Based Parallel Computations

An image’s individual 8×8 blocks can be handled concurrently or individually. With a few little tweaks to the original serial approach, the code sample easily achieves SYCL parallelization.

Example of a Product

The code sample was run on a 6th generation Intel Core processor equipped with an integrated Intel Processor Graphics Gen 9 or later and an  Intel oneAPI DPC++/C++ compiler. The example output is shown below. If a compatible GPU is detected, the code will direct execution to it; otherwise, it executes on the CPU (host).Filename: willyriver.bmp W: 5184 H: 3456 Start image processing with offloading to GPU... Running on Intel(R) UHD Graphics 620 --The processing time is 6.27823 seconds DCT successfully completed on the device. The processed image has been written to willyriver_processed.bmp

What Comes Next?

See the Discrete Cosine Transform sample for an implementation of the SYCL-based parallel DCT picture compression technology.

Take a look at a few more code samples that are accessible in the oneAPI GitHub repository.

Use the  Intel oneAPI DPC++/C++ Compiler now to begin compiling C/C++ and SYCL apps across a variety of heterogeneous systems with efficiency.

Examine further  AI, HPC, and rendering solutions available in Intel’s software portfolio that is powered by oneAPI.

Obtain the Programme

Install the  Intel HPC Toolkit or Intel oneAPI Base Toolkit along with the Intel oneAPI DPC++/C++ Compiler. Additionally, you may use the Intel Tiber Developer Cloud platform to test the compiler on a variety of Intel CPUs and GPUs or download a standalone version.

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[govindhtech]

Intel Extension for Transformers & PyTorch LLM Optimisation

Enhancing deep learning model performance is essential for scalability and efficiency in the rapidly changing field of  artificial intelligence. Intel has been in the forefront of creating frameworks and tools to improve  AI models’ memory efficiency and speed of execution, especially with Intel Extension for PyTorch and Intel Extension for Transformers.

Comprehending the AI Stack

There are several layers in the  AI stack, and each is essential to optimizing LLMs. The hardware layer, which consists of Intel Xeon CPUs, Intel Data Centre GPUs, Intel Arc GPUs, and Intel Gaudi AI accelerators, is fundamental.

The acceleration libraries, such as Intel oneAPI Collective Communications Library (oneCCL) and Intel oneAPI Deep Neural Network Library (oneDNN), sit above this layer and offer optimized kernels with Intel optimized instruction sets for effective processing. The highest layer is made up of resource-efficient frameworks such as PyTorch that interface with the hardware and libraries underneath to optimize model performance.

Important Optimization Methods

Optimizing operators is essential to improving LLM performance. Using enhanced instruction sets such as Intel enhanced Vector Extensions (Intel AVX), Intel Advanced Matrix Extensions (Intel AMX), and Intel Xe Matrix Extensions (Intel XMX), Intel replaces the default operation kernels with highly-optimized Intel oneDNN kernels. The accuracy-flexible design of this optimization ensures that applications can operate at maximum speed and precision by supporting a variety of data types, from FP32 to INT4.

Graph optimizations reduce the amount of memory accesses needed during computation, which further enhances efficiency. For example, memory access times can be reduced by combining layers (e.g., Conv+ReLU+Sum) with bandwidth-limited operations (e.g., activation functions, ReLU, or Tanh).

This method works especially well for models such as ResNet-50, where a large amount of processing time is dedicated to bandwidth-constrained tasks. Specific fusion methods, including as linear post-ops fusion and multi-head attention fusion, are used in the context of LLMs with Intel Extension for PyTorch in JIT/Torch script mode to improve performance.

Memory management is essential for maximizing LLM performance because they frequently require large amounts of memory. By pre-filling key/value pairs before to the onset of autoregressive decoding and utilising pre-allocated buffers throughout the decoding stage, the Segment KV Cache approach maximizes memory use.

This technique increases efficiency by lowering the requirement for in-the-moment memory changes. Similar to this, the Indirect Access KV Cache efficiently manages memory by utilising beam index history and pre-allocated buffers, which lowers the overhead related to memory access during inference.

Model compression uses quantization algorithms, which successively decrease weight and activation precision from FP32 to lower precision forms like INT8 or INT4. This reduction minimizes the size of the model, increases inference speed, and lowers the required for memory bandwidth. Smooth Quant is a post-training quantization technique that shifts the quantization difficulty from activations to weights. This allows for the preservation of model accuracy while mitigating activation outliers and optimizing hardware utilization.

A big part of optimization is also played by custom operators. The goal of weight-only quantization is to increase input and output activation precision by quantizing the model’s weights alone. With minimal influence on accuracy, this technique maximizes computational performance by utilising weight-only quantization-optimized bespoke GEMM (General Matrix Multiply) kernels. Performance can be further optimized by using Explicit SIMD (ESIMD) extensions, which provide more precise control over hardware features.

Intel Extension for PyTorch

APIs for implementing these optimizations on CPU and GPU based training and inference are provided by the Intel Extension for PyTorch. You may make sure that your models are optimized to operate well on Intel hardware by making use of these APIs. To make it easier for developers to execute these optimizations, the extension comes with environment configurations and scripts that are intended to maximize hardware utilization.

Another essential element of Intel’s optimization approach are the Intel Gaudi  AI accelerators. Deep learning applications perform better because to the integration of PyTorch with the Intel Gaudi software suite, which effectively transfers neural network topologies onto Gaudi hardware. This integration also supports important kernel libraries and optimizations.

Intel Extension for Transformers

https://community.intel.com/t5/image/serverpage/image-id/56748i59BB048F0E369A11/image-size/large?v=v2&px=999&whitelist-exif-data=Orientation%2CResolution%2COriginalDefaultFinalSize%2CCopyright

Several plugins for widely used pipelines, like audio processing and retrieval-augmented generation (RAG), can be integrated with Neural Chat. By integrating the required optimizations straight into the pipeline setup, it makes the deployment of optimized chatbots easier.

Neural Velocity and Dispersed Interpretation

https://community.intel.com/t5/image/serverpage/image-id/56751iE6BB93D0A520220B/image-size/large?v=v2&px=999&whitelist-exif-data=Orientation%2CResolution%2COriginalDefaultFinalSize%2CCopyright

DeepSpeed

These optimizations are further expanded across numerous nodes or GPUs via Intel’s support for distributed inference via DeepSpeed. DeepSpeed now supports Intel GPUs thanks to the Intel Extension for DeepSpeed. It includes the following parts:

Implementation of the DeepSpeed Accelerator Interface

Implementation of DeepSpeed op builder for XPU

Code for DeepSpeed op builder kernel

With the help of oneCCL, this Intel-optimized extension distributes compute jobs well, lowering memory footprint and increasing throughput overall. Scaling  AI applications across heterogeneous computer systems requires this capacity.

Utilising Optimizations in Real-World Applications

It’s actually very easy to implement these optimizations using Intel’s tools, as you can use the extensions for the PyTorch and Transformers frameworks. For example, Intel Extension for Transformers improves model compression methods such as weight-only and smooth quantization right inside the well-known Transformers API. By setting the quantization parameters and using the integrated APIs, you may optimize models with ease.

In a similar vein, the Intel Extension for Transformers and PyTorch offers an adaptable framework for optimizing deep learning models other than LLMs. This update provides GPU-centric capabilities like tensor parallelism and  CPU optimizations like NUMA management and graph optimization’s to enable fine-tuning and deployment across a variety of hardware configurations.

In summary

You may significantly increase the effectiveness and performance of your AI models by utilising Intel’s extensive hardware stack, accelerated libraries, and optimized frameworks. These optimizations cut the energy and operating expenses associated with running large-scale  AI applications in addition to improving computational performance and reducing latency.

Using the getting started samples from the Intel Extension for PyTorch and Intel Extension for Transformers, you can investigate these optimizations on the Intel Tiber Developer Cloud. You can make sure your LLMs are operating at optimal performance on Intel hardware by incorporating these strategies.

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