NextComputing

08/18/2026

What is AMD Ryzen Threadripper?

Built for demanding workloads like 3D rendering, video encoding, and AI tasks, AMD Ryzen Threadripper CPUs deliver high core counts and massive processing power for high-performance workstations.

🔗 Read the full overview & benchmarks:

AMD Ryzen Threadripper is a high-end desktop (HEDT) processor line designed primarily for professionals and enthusiasts who require extreme multi-threaded performance for tasks like high-volume data throughput, running simulations, video editing, and 3D rendering. Is it for gaming? If you’ve ever ...

07/31/2026

Ready to level up your live streams? High-quality broadcasts require more than just hitting “Go Live.”

In their latest guide, NextComputing breaks down the essential gear needed to offer seamless, professional streaming.

• Core Processing: Smooth 1080p/4K streaming relies on hardware encoding (GPUs) or a dedicated streaming PC to prevent lagging, dropped frames, and system crashes during heavy multitasking.

• Video Sources: While webcams are fine for at-home streaming, professional users who do on-site streaming for events need professional hardware like a DSLR/mirrorless camera and a DeckLink capture card for smooth visuals.

• Audio Quality: Audio makes or breaks a stream! Use proper mic placement, pop filters, and consider XLR microphones paired with an interface/mixer for precise gain control and lower noise.

• Lighting & Backdrop: Softboxes or ring lights positioned slightly above eye level reduce harsh shadows, while acoustic treatment and clean backdrops (or green screens) give your studio a polished look.

• Test Before You Broadcast: Always run private test streams to verify audio levels (-12 dB to -6 dB), frame rate stability, audio/video sync, and upload speeds (aim for at least 10+ Mbps on Ethernet).

• Choose the Right Platform: Match your goals to the platform—Twitch for real-time interactive gaming, YouTube Live for long-term search discoverability, or Restream to simulcast everywhere at once.

Read the full guide here: https://solutions.nextcomputing.com/combined-solution/what-equipment-is-needed-for-live-streaming-essential-gear-and-setup-guide/

07/15/2026

Is your infrastructure ready for the edge?

Deploying AI locally means lower latency, increased security, and faster decision-making. We've compiled the must-have hardware and software specs to get you started on the right foot.

Learn more:

Deploying AI at the edge requires compatible hardware such as low-power microcontrollers or edge TPUs capable of local data processing, optimized AI models tailored for constrained environments using techniques like quantization and pruning, data sources like cameras or medical devices, and software...

06/22/2026

How do you scale AI across thousands of physical stores without breaking the bank—or the network?

Modern retail edge AI infrastructure is shifting the heavy lifting away from distant cloud servers and placing it right on the shop floor. By deploying localized hyperconverged infrastructure (HCI), rugged edge nodes, and specialized accelerators, retailers are achieving ultra-low latency, bandwidth savings, and offline resilience.

NextComputing knows that to run these intensive AI models and handle massive datasets efficiently—especially out in the field or at the network edge—choosing the right high-performance hardware architecture is just as critical as choosing your software tools.

Retail edge AI infrastructure typically consists of localized computing hardware such as hyperconverged edge platforms, integrated with AI-capable devices like IoT sensors, cameras, and specialized low-power AI processors, along with orchestration and containerization, storage, and networking. (Lear...

06/18/2026

Guesswork or gut feelings? There’s a smarter way to make critical business decisions.

A/B testing is the gold standard for data-driven choices, but pairing it with AI takes your optimization to the next level. From automating hypotheses to predicting high-impact wins and dynamically managing traffic with Multi-Armed Bandits, AI makes testing faster, sharper, and more precise.

Stop losing conversions while you wait for statistical significance. Let data—and intelligent automation—speak clearly.

Read our latest guide on how to integrate AI into your A/B testing framework:

What if you made all your critical business decisions based on guesswork or gut feelings, how often would you get it right? In data science, A/B testing offers a smarter way: testing two versions of something to see which one works better. But what if this process could be faster, sharper, and smart...

06/04/2026

As cyber threats grow more sophisticated, modern banks are moving past traditional security methods and turning to AI as an always-on sentinel to protect customer data and assets. By using machine learning models, financial institutions can detect anomalies, block fraud, and neutralize zero-day threats like deepfakes and advanced phishing in real time. AI also helps automate incident response to isolate breaches instantly, monitors for insider risks through behavioral analytics, and simplifies regulatory compliance—giving banks a proactive, fast-evolving shield against cybercriminals.

Read more at https://solutions.nextcomputing.com/ai/how-is-ai-used-in-cybersecurity-for-banks/

05/18/2026

Integrating AI into data science boosts efficiency and accuracy by automating routine tasks like data cleaning, predictive modeling, and NLP. By utilizing powerful platforms like PyTorch, Scikit-learn, and Tableau, data scientists can shift their focus from tedious manual ex*****on to high-value strategy and advanced visualization.

To use AI for data science, start by leveraging AI-powered tools to automate boilerplate coding, data cleaning, and exploratory analysis tasks, which accelerates your workflow. Take advantage of AI’s NLP (Natural Language Processing) and data visualization and exploration to glean fresh insights. ...

04/23/2026

What are AI workloads? Specialized computing tasks designed to develop, train, and deploy artificial intelligence models, ranging from lightweight CPU-based tasks to large-scale data processing and high-performance computing. An AI workload is any job, big or small, that a system must execute to build, run, or maintain an AI model.

AI workloads are specialized computing tasks designed to develop, train, and deploy artificial intelligence models, ranging from lightweight CPU-based tasks to large-scale data processing and high-performance computing. An AI workload is any job, big or small, that a system must execute to build, ru...