#CUDA AI

[diagnozabam]

Revoluția AI Local: Open WebUI și Puterea GPU-urilor NVIDIA în 2025

Într-o eră dominată de inteligența artificială bazată pe cloud, asistăm la o revoluție tăcută: aducerea AI-ului înapoi pe computerele personale. Apariția Open WebUI, alături de posibilitatea de a rula modele de limbaj de mari dimensiuni (LLM) local pe GPU-urile NVIDIA, transformă modul în care utilizatorii interacționează cu inteligența artificială. Această abordare promite mai multă…

[oensible]

Crying tears out of my eyeballs the cuda have posted a horrible ai gen doll merch mockup of fenzy and it has me in shambles

[tsubakicraft]

大規模言語モデルのチューニング

2000件ほどのデータセットを使ってチューニングしたRinna社の7.5Bパラメーターの言語モデル。学習には24時間かかりました。 GPUはRTX 3060というVRAMを12GB積んだエントリーグレードのものでCPUはCore i5…

[innovatexblog]

Unlocking AI's Power: Your 2025 Guide to GPUs!

Get ready to dive deep into the hardware that's fueling the AI revolution! Our latest post is your ultimate guide to understanding the powerhouse GPUs of 2025.

We're breaking down the epic showdown between NVIDIA H100 and AMD Instinct MI300, looking at everything from raw performance to real-world applications in LLM pretraining and genomics.

Plus, we'll guide you through the nuances of precision modes (FP16, INT8, FP8), help you build an AI workstation on a budget, and navigate the world of open-source GPU libraries like CUDA and ROCm.

Whether you're an AI enthusiast, a developer, or just curious about the tech shaping our future, this is a must-read!

🔗 explore the full guide

[kamalkafir-blog]

Nvidia CEO Jensen Huang calls US ban on H20 AI chip ‘deeply painful’

[ASIA] Nvidia CEO Jensen Huang said Washington’s plan to stymie China’s artificial intelligence (AI) capabilities by restricting access to its H20 graphics processing units (GPUs) was “deeply uninformed”, as the US semiconductor giant continues to navigate through a deepening tech rivalry between the world’s two largest economies. In an interview with tech site Stratechery following his keynote…

[govindhtech]

Intel Data Center GPU SqueezeLLM Inference With SYCLomatic

Turn on SqueezeLLM for Efficient LLM Inference on Intel Data Center GPU Max Series utilizing SYCLomatic for Converting CUDA to SYCL.

In brief

Researchers at the University of California, Berkeley, have devised a revolutionary quantization technique called SqueezeLLM, which enables accurate and efficient generative LLM inference. Cross-platform compatibility, however, requires unique kernel implementations and hence more implementation work.

Using the SYCLomatic tool from the Intel oneAPI Base Toolkit to take advantage of CUDA-to-SYCL migration, they were able to immediately achieve a 2.0x speedup on Intel Data Center GPUs with 4-bit quantization without the need for manual tweaking. Because of this, cross-platform compatibility may be provided with little extra technical effort needed to adapt the kernel implementations to various hardware back ends.

SqueezeLLM: Precise and Effective Low-Precision Quantization for Optimal LLM Interpretation

Because LLM inference allows for so many applications, it is becoming a common task. But LLM inference uses a lot of resources; it needs powerful computers to function. Furthermore, since generative LLM inference requires the sequential generation of output tokens, it suffers from minimal data reuse, while previous machine learning workloads have mostly been compute-bound. Low-precision quantization is one way to cut down on latency and memory use, but it may be difficult to quantize LLMs to low precision (less than 4 bits, for example) without causing an unacceptable loss of accuracy.

SqueezeLLM is a tool that UC Berkeley researchers have created to facilitate precise and efficient low-precision quantization. Two important advances are included into SqueezeLLM to overcome shortcomings in previous approaches. It employs sensitivity-weighted non-uniform quantization, which uses sensitivity to determine the optimal allocation for quantization codebook values, thereby maintaining model accuracy.

This approach addresses the inefficient representation of the underlying parameter distribution caused by the limitations of uniform quantization. Furthermore, SqueezeLLM provides dense-and-sparse quantization, which allows quantization of the remaining parameters to low precision by addressing extremely high outliers in LLM parameters by preserving outlier values in a compact sparse format.

Non-uniform quantization is used by SqueezeLLM to best represent the LLM weights with less accuracy. When generating the non-uniform codebooks, the non-uniform quantization technique takes into consideration not only the magnitude of values but also the sensitivity of parameters to mistake, offering excellent accuracy for low-precision quantization.

Dense-and-sparse quantization, which SqueezeLLM employs, allows for the greater accuracy storage of a tiny portion of outlier values. This enables precise low-precision quantization for the dense matrix by lowering the needed range that must be represented by the remaining dense component.

The difficulty is in offering cross-platform assistance for low-precision LLM quantization

The method in SqueezeLLM provides for considerable latency reduction in comparison to baseline FP16 inference, as well as efficient and accurate low-precision LLM quantization to minimize memory usage during LLM inference. Their goal was to allow cross-platform availability of these methods for improving LLM inference on systems like Intel Data Center GPUs, by enabling cross-platform support.

SqueezeLLM, on the other hand, depends on handcrafted custom kernel implementations that use dense-and-sparse quantization to tackle the outlier problem with LLM inference and non-uniform quantization to offer correct representation with extremely few bits per parameter.

Even though these kernel implementations are rather simple, it is still not ideal to manually convert and optimize them for various target hardware architectures. They predicted a large overhead while converting their SqueezeLLM kernels to operate on Intel Data Center GPUs since they first created the kernels using CUDA and it took weeks to construct, profile, and optimize these kernels.

Therefore, in order to target Intel Data Center GPUs, they needed a way to rapidly and simply migrate their own CUDA kernels to SYCL. To prevent interfering with the remainder of the inference pipeline, this calls for the ability to convert the kernels with little human labor and the ability to more easily modify the Python-level code to use the custom kernels. They also wanted the ported kernels to be as efficient as possible so that Intel customers could benefit fully from SqueezeLLM‘s efficiency.

SYCLomatic

SYCLomatic offers a way to provide cross-platform compatibility without requiring extra technical work. The effective kernel techniques may be separated from the target deployment platform by using SYCLomatic’s CUDA-to-SYCL code conversion. This allows for inference on several target architectures with little extra engineering work.

Their performance investigation shows that the SYCLomatic-ported kernels achieve a 2.0x speedup on Intel Data Center GPUs running the Llama 7B model, and instantly improve efficiency without the need for human tweaking.

CUDA to SYCL

Solution: A SYCLomatic-Powered CUDA-to-SYCL Migration for Quantized LLMs on Multiple Platforms.

First Conversion

SYCLomatic conversion was carried out in a development environment that included the Intel oneAPI Base Toolkit. Using the SYCLomatic conversion command dpct quant_cuda_kernel.cu, the kernel was moved to SYCL. They are happy to inform that the conversion script changed the kernel implementations as needed and automatically produced accurate kernel definitions. The following examples demonstrate how SYCL-compatible code was added to the kernel implementation and invocations without

Change Python Bindings to Allow Custom Kernel Calling

The bindings were modified to utilize the PyTorch XPU CPP extension (DPCPPExtension) in order to call the kernel from Python code. This enabled the migrating kernels to be deployed using a setup in the deployment environment. Python script:

Initial Bindings Installation CUDA Kernel Installation in the Setup Script

1. setup( name="quant_cuda", 2 .ext_modules=[ 3. cpp_extension.CUDAExtension( 4. "quant_cuda", 5. ["quant_cuda.cpp", "quant_cuda_kernel.cu"] 6. ) 7. ], 8. cmdclass={"build_ext": cpp_extension.BuildExtension}, 9. )

Changed Setup Script Kernel Installation to Bindings

1. setup( 2. name='quant_sycl', 3. ext_modules=[ 4. DPCPPExtension( 5. 'quant_sycl', 6. ['quant_cuda.cpp', 'quant_cuda_kernel.dp.cpp',] 7. ) 8. ], 9. cmdclass={ 10. 'build_ext': DpcppBuildExtension 11. } 12. )

The converted SYCL kernels could be called from PyTorch code when the kernel bindings were installed, allowing end-to-end inference to be conducted with the converted kernels. This made it easier to convert the current SqueezeLLM Python code to support the SYCL code, requiring just small changes to call the migrated kernel bindings.

Analysis of Converted Kernels’ Performance

The ported kernel implementations were tested and benchmarked by the SqueezeLLM team using Intel Data Center GPUs made accessible via the Intel Tiber Developer Cloud. As described earlier, SYCLomatic was used to convert the inference kernels, and after that, adjustments were made to enable calling the SYCL code from the SqueezeLLM Python code.

Benchmarking the 4-bit kernels on the Intel Data Center GPU Max Series allowed us to evaluate the performance gains resulting from low-precision quantization. In order to really enable efficient inference on many platforms, this evaluated if the conversion procedure might provide efficient inference kernels.

Table 1 shows the speedup and average latency for matrix-vector multiplications while using the Llama 7B model to generate 128 tokens. These findings show that substantial speedups may be achieved with the ported kernels without the need for human tweaking.

In order to evaluate the latency advantages of low-precision quantization that are achievable across various hardware back ends without requiring changes to the SYCL code, the 4-bit kernels were benchmarked on the Intel Data Center GPU. Running the Llama 7B model without any human adjustment allows SqueezeLLM to achieve a 2.0x speedup on Intel Data Center GPUs compared to baseline FP16 inference, as Table 1 illustrates.KernelLatency (in seconds)Baseline: fp16 Matrix-Vector Multiplication2.584SqueezeLLM: 4-bit (0% sparsity)1.296Speedup2.0x

When this speedup is contrasted with the 4-bit inference results achieved on the NVIDIA A100 hardware platform, which achieved 1.7x speedups above baseline FP16 inference, it can be shown that the ported kernels outperform the handwritten CUDA kernels designed for NVIDIA GPU systems. These findings demonstrate that equivalent speedups on various architectures may be achieved via CUDA-to-SYCL migration utilizing SYCLomatic, all without requiring extra engineering work or manual kernel tweaking after conversion.

In summary

For new applications, LLM inference is a fundamental task, and low-precision quantization is a crucial way to increase inference productivity. SqueezeLLM allows for low-precision quantization to provide accurate and efficient generative LLM inference. However, cross-platform deployment becomes more difficult due to the need for bespoke kernel implementations. The kernel implementation may be easily converted to other hardware architectures with the help of the SYCLomatic migration tool.

For instance, SYCLomatic-migrated 4-bit SqueezeLLM kernels show a 2.0x speedup on Intel Data Center GPUs without the need for human tweaking. Thus, SYCL conversion democratizes effective LLM implementation by enabling support for many hardware platforms with no additional technical complexity.

Red more on Govindhtech.com

[avandelay20]

Summarized by Bing Chat:

Eric Schmidt’s talk on “The Age of AI” at Stanford ECON295/CS323.

Introduction

Eric Schmidt, former CEO of Google and founder of Schmidt Futures, begins his talk by discussing the rapid advancements in artificial intelligence (AI) and its profound implications for the future. He emphasizes the importance of staying updated on AI developments due to the fast-paced nature of the field. Schmidt’s extensive experience in the tech industry provides a unique perspective on the transformative potential of AI.

Short-Term AI Developments

In the short term, Schmidt highlights the concept of a “million-token context window.” This refers to the ability of AI models to process and understand vast amounts of information simultaneously. This advancement is expected to significantly enhance AI capabilities within the next one to two years. Schmidt explains that this development will enable AI systems to handle more complex tasks and provide more accurate and contextually relevant responses.

AI Agents and Text-to-Action

Schmidt delves into the technical definitions of AI agents and the concept of text-to-action. AI agents are specialized programs designed to perform specific tasks autonomously. Text-to-action involves converting text inputs into actionable commands, such as programming in Python. Schmidt illustrates this concept with examples, demonstrating how AI can streamline various processes and improve efficiency in different domains.

The Dominance of Python and New Programming Languages

Python has long been the dominant programming language in the AI community due to its simplicity and versatility. Schmidt introduces a new language called Mojo, which aims to address some of the challenges associated with AI programming. While he acknowledges the potential of Mojo, Schmidt expresses skepticism about whether it will surpass Python’s dominance. He emphasizes the importance of continuous innovation in programming languages to keep pace with AI advancements.

Economic Implications of AI

The economic impact of AI is a significant focus of Schmidt’s talk. He discusses the reasons behind NVIDIA’s success in the AI market, attributing the company’s $2 trillion valuation to its CUDA optimizations. These optimizations are crucial for running AI code efficiently, making NVIDIA a key player in the AI hardware industry. Schmidt also explores the broader economic implications of AI, including its potential to disrupt traditional industries and create new opportunities for growth.

AI in Business and Society

Schmidt concludes his talk by discussing the broader implications of AI for businesses and society. He emphasizes the need for organizations and individuals to adapt to the rapidly changing AI landscape. Schmidt highlights the importance of ethical considerations in AI development and deployment, stressing the need for responsible AI practices to ensure positive outcomes for society.

Conclusion

In summary, Eric Schmidt’s talk on “The Age of AI” provides valuable insights into the current state and future potential of artificial intelligence. He covers a wide range of topics, from technical advancements and programming languages to economic implications and ethical considerations. Schmidt’s expertise and experience offer a comprehensive overview of the transformative power of AI and its impact on various aspects of our lives.

[ourwitching]

CUDA Graphs can provide a significant performance increase, as the driver is able to optimize exe...

[ketbra]

Cpu: 5 minutes and 30 seconds

Cuda : 4 minutes and 15 seconds

Am I joke to you??? 😭😭😭😭😭

[phonemantra-blog]

The Ultimate Guide to RTX A5000: Unleashing the Power of Next-Generation Graphics Understanding RTX A5000 What is RTX A5000? The RTX A5000 is a powerful graphics card developed by NVIDIA. It is the latest addition to the RTX series and is designed to deliver exceptional performance and cutting-edge technology for graphics-intensive tasks and applications. [caption id="attachment_62082" align="aligncenter" width="800"] RTX a5000[/caption] Evolution of RTX A5000 The RTX A5000 represents the culmination of NVIDIA's continuous innovation in the field of graphics cards. It builds upon the advancements made in previous RTX models, incorporating new features and improvements to deliver even better performance and visual quality. Benefits of RTX A5000 The RTX A5000 offers numerous benefits to professionals and enthusiasts alike. Its powerful GPU architecture, high CUDA core count, and ample memory capacity enable faster rendering, real-time visualization, and complex modeling. This graphics card is a game-changer for industries such as graphic design, data science, video editing, and gaming. Technical Specifications The RTX A5000 is built on NVIDIA's latest GPU architecture and features 8192 CUDA cores. It boasts 24 GB of GDDR6 memory, a memory bandwidth of 768 GB/s, and a maximum power consumption of 230W. With a base clock speed of 1410 MHz and a boost clock speed of 1935 MHz, the RTX A5000 delivers exceptional performance and responsiveness. Applications and Use Cases Professional Graphics Design RTX A5000 empowers graphic designers, architects, and artists with its exceptional capabilities. With real-time rendering, complex modeling, and visualization capabilities, professionals can bring their creative visions to life with unparalleled speed and accuracy. The A5000 allows for seamless workflow integration and enhances productivity for professionals in the field of graphic design. Data Science and AI RTX A5000 is a game-changer for data scientists and AI researchers. Its high-performance GPU enables accelerated machine learning and data analysis, allowing for faster training and inference times. With its advanced GPU architecture and large memory capacity, the A5000 is capable of handling complex deep-learning models, neural networks, and simulations, making it an ideal choice for professionals in the field. Video Editing and Production For video editors and content creators, the RTX A5000 offers unparalleled rendering capabilities. With real-time editing, color grading, and special effects, professionals can achieve their creative visions with ease. The A5000's powerful GPU and ample memory capacity enable smooth playback and editing of high-resolution footage, providing a seamless editing experience. Installation and Optimization System Requirements Before installing the RTX A5000, it is important to ensure that your system meets the minimum requirements. These include a compatible motherboard with an available PCIe slot, a sufficient power supply, and the necessary cables for connectivity. It is recommended to check the official documentation provided by NVIDIA for the specific system requirements. Installing the RTX A5000 Installing the RTX A5000 is a straightforward process. Begin by shutting down your computer and disconnecting the power cable. Open the computer case and locate an available PCIe slot. Carefully insert the A5000 into the slot, ensuring it is aligned properly. Secure the graphics card using the screws provided. Finally, reconnect the power cable and any necessary cables for display connectivity. Driver Installation and Updates Once the RTX A5000 is physically installed, it is crucial to install the appropriate drivers to ensure optimal performance. Visit the official NVIDIA website and navigate to the drivers section. Use the search filters to find the drivers specifically designed for the RTX A5000 and your operating system. Download the latest driver version and follow the on-screen instructions to install it on your system. Performance Optimization To maximize the performance of your RTX A5000, consider the following optimization techniques: Overclocking: If you are comfortable with advanced settings, you can overclock the GPU to achieve higher clock speeds and performance. However, be cautious and ensure proper cooling to prevent overheating. Cooling: Proper cooling is essential to maintain optimal performance and prevent thermal throttling. Make sure your system has adequate airflow, clean any dust from fans and heatsinks, and consider using additional cooling solutions if necessary. Monitoring: Utilize monitoring tools to keep track of GPU temperature, clock speeds, and usage. This will help you identify any potential performance issues and take appropriate action. Frequently Asked Questions What is the difference between RTX A5000 and previous RTX models? The RTX A5000 brings several advancements over previous RTX models, including increased CUDA core count, higher memory capacity, and improved performance. These enhancements result in better rendering speeds, real-time visualization, and overall graphics processing capabilities. Can I use multiple RTX A5000 cards in parallel? Yes, the RTX A5000 supports multi-GPU configurations through NVIDIA's SLI technology. By installing multiple A5000 cards in your system and enabling SLI, you can harness the combined power of the GPUs for even greater performance in supported applications. Is the RTX A5000 compatible with my existing PC? Before purchasing the RTX A5000, ensure that your PC meets the requirements. Check for compatibility with your motherboard, power supply, and available PCIe slots. Additionally, verify that your system has the appropriate power connectors and sufficient power capacity to support the A5000. Does the RTX A5000 support ray tracing and DLSS? Yes, the RTX A5000 fully supports ray tracing and DLSS (Deep Learning Super Sampling). Ray tracing enables realistic lighting and reflections in real time, while DLSS enhances performance by using AI to upscale lower-resolution images to higher resolutions with minimal loss in quality. What software applications are optimized for RTX A5000? The RTX A5000 is optimized for various software applications used in graphic design, data science, video editing, and gaming. Some popular examples include Adobe Creative Suite, Autodesk Maya, Blender, TensorFlow, and Unreal Engine. NVIDIA regularly collaborates with software developers to optimize their applications for the RTX series. Can the RTX A5000 be used for cryptocurrency mining? While the RTX A5000 can technically be used for cryptocurrency mining, it is not the most efficient choice due to its higher price point and power consumption. GPUs specifically designed for mining, such as NVIDIA's CMP series, offer better performance and cost-effectiveness for mining purposes. How does the RTX A5000 compare to AMD's competing graphics cards? The RTX A5000 competes with AMD's professional graphics cards, such as the Radeon Pro series. While the specific comparison may vary depending on the models, the RTX A5000 generally offers superior performance, advanced ray tracing capabilities, and broader software optimization. However, it is recommended to compare specific models and their features to make an informed decision. What kind of warranty and support is provided with the RTX A5000? The RTX A5000 typically comes with a standard warranty provided by NVIDIA or the manufacturer. The duration and terms of the warranty may vary, so it is advisable to check the warranty information provided by the seller or NVIDIA's official website. What future developments can we expect from NVIDIA in the graphics card industry? NVIDIA is continuously pushing the boundaries of graphics card technology. In the future, we can expect further advancements in GPU architecture, increased performance, improved power efficiency, and enhanced features. NVIDIA is also likely to continue investing in technologies such as ray tracing, DLSS, and AI to provide even more realistic and immersive graphics experiences. Conclusion: The RTX A5000 is a powerful graphics card that offers exceptional performance and cutting-edge technology. With its advanced GPU architecture, high CUDA core count, and ample memory capacity, the A5000 revolutionizes industries such as graphic design, data science, video editing, and gaming. By understanding its features, applications, and proper installation and optimization techniques, users can unleash the full potential of the RTX A5000 and experience the next generation of graphics.

[utopicwork]

So how did DeepSeek do it besides what's already been discussed? It seems like we know now:

"DeepSeek's AI breakthrough bypasses Nvidia's industry-standard CUDA, uses assembly-like PTX programming instead"

[tsubakicraft]

小さい大規模言語モデルの学習

小さい大規模って・・・ Rinna社さんがオープンソースで公開している言語モデルの学習がローカル環境でもやれるというのでやってみています。 Core i5 13400とRTX…

[kamalkafir-blog]

Tech war: US curbs on global use of Huawei chips add uncertainty to China’s AI investment

[ASIA] New US guidelines on the use of Huawei Technologies’ Ascend chips have introduced fresh uncertainty into China’s investment spree in artificial intelligence (AI) infrastructure, according to analysts and industry insiders. This puts Chinese companies investing heavily in computing infrastructure in a difficult position. On one hand, they have been denied access to cutting-edge AI…

[budgetgameruae]

Best PC for Data Science & AI with 12GB GPU at Budget Gamer UAE

Are you looking for a powerful yet affordable PC for Data Science, AI, and Deep Learning? Budget Gamer UAE brings you the best PC for Data Science with 12GB GPU that handles complex computations, neural networks, and big data processing without breaking the bank!

Why Do You Need a 12GB GPU for Data Science & AI?

Before diving into the build, let’s understand why a 12GB GPU is essential:

✅ Handles Large Datasets – More VRAM means smoother processing of big data. ✅ Faster Deep Learning – Train AI models efficiently with CUDA cores. ✅ Multi-Tasking – Run multiple virtual machines and experiments simultaneously. ✅ Future-Proofing – Avoid frequent upgrades with a high-capacity GPU.

Best Budget Data Science PC Build – UAE Edition

Here’s a cost-effective yet high-performance PC build tailored for AI, Machine Learning, and Data Science in the UAE.

1. Processor (CPU): AMD Ryzen 7 5800X

8 Cores / 16 Threads – Perfect for parallel processing.

3.8GHz Base Clock (4.7GHz Boost) – Speeds up data computations.

PCIe 4.0 Support – Faster data transfer for AI workloads.

2. Graphics Card (GPU): NVIDIA RTX 3060 12GB

12GB GDDR6 VRAM – Ideal for deep learning frameworks (TensorFlow, PyTorch).

CUDA Cores & RT Cores – Accelerates AI model training.

DLSS Support – Boosts performance in AI-based rendering.

3. RAM: 32GB DDR4 (3200MHz)

Smooth Multitasking – Run Jupyter Notebooks, IDEs, and virtual machines effortlessly.

Future-Expandable – Upgrade to 64GB if needed.

4. Storage: 1TB NVMe SSD + 2TB HDD

Ultra-Fast Boot & Load Times – NVMe SSD for OS and datasets.

Extra HDD Storage – Store large datasets and backups.

5. Motherboard: B550 Chipset

PCIe 4.0 Support – Maximizes GPU and SSD performance.

Great VRM Cooling – Ensures stability during long AI training sessions.

6. Power Supply (PSU): 650W 80+ Gold

Reliable & Efficient – Handles high GPU/CPU loads.

Future-Proof – Supports upgrades to more powerful GPUs.

7. Cooling: Air or Liquid Cooling

AMD Wraith Cooler (Included) – Good for moderate workloads.

Optional AIO Liquid Cooler – Better for overclocking and heavy tasks.

8. Case: Mid-Tower with Good Airflow

Multiple Fan Mounts – Keeps components cool during extended AI training.

Cable Management – Neat and efficient build.

Why Choose Budget Gamer UAE for Your Data Science PC?

✔ Custom-Built for AI & Data Science – No pre-built compromises. ✔ Competitive UAE Pricing – Best deals on high-performance parts. ✔ Expert Advice – Get guidance on the perfect build for your needs. ✔ Warranty & Support – Reliable after-sales service.

Performance Benchmarks – How Does This PC Handle AI Workloads?

TaskPerformanceTensorFlow Training2x Faster than 8GB GPUsPython Data AnalysisSmooth with 32GB RAMNeural Network TrainingHandles large models efficientlyBig Data ProcessingNVMe SSD reduces load times

FAQs – Data Science PC Build in UAE

1. Is a 12GB GPU necessary for Machine Learning?

Yes! More VRAM allows training larger models without memory errors.

2. Can I use this PC for gaming too?

Absolutely! The RTX 3060 12GB crushes 1080p/1440p gaming.

3. Should I go for Intel or AMD for Data Science?

AMD Ryzen offers better multi-core performance at a lower price.

4. How much does this PC cost in the UAE?

Approx. AED 4,500 – AED 5,500 (depends on deals & upgrades).

5. Where can I buy this PC in the UAE?

Check Budget Gamer UAE for the best custom builds!

Final Verdict – Best Budget Data Science PC in UAE

If you're into best PC for Data Science with 12GB GPU PC build from Budget Gamer UAE is the perfect balance of power and affordability. With a Ryzen 7 CPU, RTX 3060, 32GB RAM, and ultra-fast storage, it handles heavy workloads like a champ.

[quotejungle]

AIが登場する以前、NVIDIAはGPU、フレームレート、グラフィックスといったゲーミング企業でした。今でも多くの人がNVIDIAをそう捉えています。しかし、そのようなNVIDIAはもはや存在していません。 GPT-4、Claude、Geminiといった主要モデルのほぼすべてがNVIDIAハードウェア上で学習・提供されています。モデル学習の90%以上がNVIDIAのチップ上で実行されています。推論（レスポンスを生成するプロセス）は、依然として70～80%がNVIDIAの技術によって実現されています。 OpenAIはAzure内のNVIDIAクラスター上で稼働しています。MicrosoftはGPU供給の確保に躍起になっています。AWSはカスタムチップを搭載しているにもかかわらず、主要なワークロードでは依然としてNVIDIAに依存しています。NVIDIAなしではスケールアップは不可能です。 NVIDIAはチップを製造するだけではない。AIサプライチェーンをコントロールしているのだ。ハードウェアからドライバー、CUDAなどのソフトウェアフレームワーク、そしてGPUを展開可能なインフラストラクチャに変えるオーケストレーション層まで、これらは業界で最も静かで絶対的なボトルネックです。

AIスタートアップの99%は2026年までに消滅するだろう — その理由はここにある | スリニヴァス・ラオ | 2025年5月 | Medium

[ourwitching]

CUDA Graphs can provide a significant performance increase, as the driver is able to optimize exe...