#GPU Acceleration

[electronicsbuzz]

https://electronicsbuzz.in/highpoint-pcie-gen5-x16-nvme-switch-adapters-revolutionize-ai-gpu-storage/

[genericpuff]

the /r/clipstudio mods removed this so that's how you know it deserves to be said

[tamara-kama]

Look at what I have leftover from the 90's.. I believe this is my original 3Dfx Voodoo 1 graphics board from Diamond Multimedia!

They called it the Diamond Monster 3D!

Some time I'd like to mount it in a special shadow box with maybe the best ad for it as the background and lighting as a display.

TBH the 4MB of VRAM it has would be considered a miniscule amount these days now that we have GPUs with multiple GBs of VRAM.. haha

[deeliteyears]

Of course I'm AGP

[guiltiest-gear]

girl. discord somehow started eating up 50% of my cpu. what the fuck

Average proprietary software moment

[semimediapress]

AMD acquires engineering team from Untether AI to strengthen AI chip capabilities

June 9, 2025 /SemiMedia/ — AMD has acquired the engineering team of Untether AI, a Toronto-based startup known for developing power-efficient AI inference accelerators for edge and data center applications. The deal marks another strategic step by AMD to expand its AI computing capabilities and challenge NVIDIA’s dominance in the field. In a statement, AMD said the newly integrated team will…

[qyriaha]

sure no this project worth 35% of this 30 credit module is due at midnight and i've finished 1/4 of the code & haven't started the report yet. no i'm fine wdym.

[clown-machine]

According to most of the notes it seems like whatever the capture blocking protocol is is dealt with using hardware acceleration in most browsers, so turning off hardware acceleration in the general/performance settings will let you capture again.

hbo max blocks screenshots even when I use the snipping tool AND firefox AND ublock which is a fucking first. i will never understand streaming services blocking the ability to take screenshots thats literally free advertising for your show right there. HOW THE HELL IS SOMEBODY GONNA PIRATE YOUR SHOW THROUGH SCREENSHOTS. JACKASS

[hadoriel]

Agh Discord updated and I hate it and also my screen keeps blacking out and glitching while trying to watch Youtube this morning :C

[buysellram]

BuySellRam.com is expanding its focus on AI hardware to meet the growing demands of the industry. Specializing in high-performance GPUs, SSDs, and AI accelerators like Nvidia and AMD models, BuySellRam.com offers businesses reliable access to advanced technology while promoting sustainability through the recycling of IT equipment. Read more about how we're supporting AI innovation and reducing e-waste in our latest announcement:

[electronicsbuzz]

https://electronicsbuzz.in/hardware-acceleration-enhancing-performance-in-industrial-computing/

[govindhtech]

Tech Breakdown: What Is a SuperNIC? Get the Inside Scoop!

The most recent development in the rapidly evolving digital realm is generative AI. A relatively new phrase, SuperNIC, is one of the revolutionary inventions that makes it feasible.

Describe a SuperNIC

On order to accelerate hyperscale AI workloads on Ethernet-based clouds, a new family of network accelerators called SuperNIC was created. With remote direct memory access (RDMA) over converged Ethernet (RoCE) technology, it offers extremely rapid network connectivity for GPU-to-GPU communication, with throughputs of up to 400Gb/s.

SuperNICs incorporate the following special qualities:

Ensuring that data packets are received and processed in the same sequence as they were originally delivered through high-speed packet reordering. This keeps the data flow’s sequential integrity intact.

In order to regulate and prevent congestion in AI networks, advanced congestion management uses network-aware algorithms and real-time telemetry data.

In AI cloud data centers, programmable computation on the input/output (I/O) channel facilitates network architecture adaptation and extension.

Low-profile, power-efficient architecture that effectively handles AI workloads under power-constrained budgets.

Optimization for full-stack AI, encompassing system software, communication libraries, application frameworks, networking, computing, and storage.

Recently, NVIDIA revealed the first SuperNIC in the world designed specifically for AI computing, built on the BlueField-3 networking architecture. It is a component of the NVIDIA Spectrum-X platform, which allows for smooth integration with the Ethernet switch system Spectrum-4.

The NVIDIA Spectrum-4 switch system and BlueField-3 SuperNIC work together to provide an accelerated computing fabric that is optimized for AI applications. Spectrum-X outperforms conventional Ethernet settings by continuously delivering high levels of network efficiency.

Yael Shenhav, vice president of DPU and NIC products at NVIDIA, stated, “In a world where AI is driving the next wave of technological innovation, the BlueField-3 SuperNIC is a vital cog in the machinery.” “SuperNICs are essential components for enabling the future of AI computing because they guarantee that your AI workloads are executed with efficiency and speed.”

The Changing Environment of Networking and AI

Large language models and generative AI are causing a seismic change in the area of artificial intelligence. These potent technologies have opened up new avenues and made it possible for computers to perform new functions.

GPU-accelerated computing plays a critical role in the development of AI by processing massive amounts of data, training huge AI models, and enabling real-time inference. While this increased computing capacity has created opportunities, Ethernet cloud networks have also been put to the test.

The internet’s foundational technology, traditional Ethernet, was designed to link loosely connected applications and provide wide compatibility. The complex computational requirements of contemporary AI workloads, which include quickly transferring large amounts of data, closely linked parallel processing, and unusual communication patterns all of which call for optimal network connectivity were not intended for it.

Basic network interface cards (NICs) were created with interoperability, universal data transfer, and general-purpose computing in mind. They were never intended to handle the special difficulties brought on by the high processing demands of AI applications.

The necessary characteristics and capabilities for effective data transmission, low latency, and the predictable performance required for AI activities are absent from standard NICs. In contrast, SuperNICs are designed specifically for contemporary AI workloads.

Benefits of SuperNICs in AI Computing Environments

Data processing units (DPUs) are capable of high throughput, low latency network connectivity, and many other sophisticated characteristics. DPUs have become more and more common in the field of cloud computing since its launch in 2020, mostly because of their ability to separate, speed up, and offload computation from data center hardware.

SuperNICs and DPUs both have many characteristics and functions in common, however SuperNICs are specially designed to speed up networks for artificial intelligence.

The performance of distributed AI training and inference communication flows is highly dependent on the availability of network capacity. Known for their elegant designs, SuperNICs scale better than DPUs and may provide an astounding 400Gb/s of network bandwidth per GPU.

When GPUs and SuperNICs are matched 1:1 in a system, AI workload efficiency may be greatly increased, resulting in higher productivity and better business outcomes.

SuperNICs are only intended to speed up networking for cloud computing with artificial intelligence. As a result, it uses less processing power than a DPU, which needs a lot of processing power to offload programs from a host CPU.

Less power usage results from the decreased computation needs, which is especially important in systems with up to eight SuperNICs.

One of the SuperNIC’s other unique selling points is its specialized AI networking capabilities. It provides optimal congestion control, adaptive routing, and out-of-order packet handling when tightly connected with an AI-optimized NVIDIA Spectrum-4 switch. Ethernet AI cloud settings are accelerated by these cutting-edge technologies.

Transforming cloud computing with AI

The NVIDIA BlueField-3 SuperNIC is essential for AI-ready infrastructure because of its many advantages.

Maximum efficiency for AI workloads: The BlueField-3 SuperNIC is perfect for AI workloads since it was designed specifically for network-intensive, massively parallel computing. It guarantees bottleneck-free, efficient operation of AI activities.

Performance that is consistent and predictable: The BlueField-3 SuperNIC makes sure that each job and tenant in multi-tenant data centers, where many jobs are executed concurrently, is isolated, predictable, and unaffected by other network operations.

Secure multi-tenant cloud infrastructure: Data centers that handle sensitive data place a high premium on security. High security levels are maintained by the BlueField-3 SuperNIC, allowing different tenants to cohabit with separate data and processing.

Broad network infrastructure: The BlueField-3 SuperNIC is very versatile and can be easily adjusted to meet a wide range of different network infrastructure requirements.

Wide compatibility with server manufacturers: The BlueField-3 SuperNIC integrates easily with the majority of enterprise-class servers without using an excessive amount of power in data centers.

[updatedideas]

Amazons GPT55X Unveiled

Hey there, tech enthusiast! 🚀 Grab your coffee because we’re about to dive into one of the most exciting innovations in the world of AI: Amazon’s GPT55X. Picture this: you’re chatting with a friend, and they casually mention this groundbreaking piece of tech. Confused? Don’t fret. We’re here to break it down for you, friend-to-friend. Introducing the Rockstar: Amazons GPT55X Ever watched a movie…

[verilog-official]

it's a 15 year old mid-ish low-ish range laptop. it should be perfectly playable but it's the GPU that makes it run poorly, integrated graphics in 2010 was certainly not suitable for gaming at the time.

but it's kinda funny because although it runs at like 20fps the CPU usage is less than 15%, just because it's so severely bottlenecked by the GPU.

in fact, in half life 1 I get around 40fps using the GPU. by using software rendering, I get 30fps. software rendering, mind you, essentially just makes the CPU *act* as a GPU, and is insanely slow.

20 fps in half life 2 with lowest settings let's goo

[s-lycopersicum]

cool party trick where you give me a laptop with linux and after half an hour i give it back with a stable, fully working, gpu-accelerated installation of photoshop cs6

[jcmarchi]

End GPU underutilization: Achieve peak efficiency

New Post has been published on https://thedigitalinsider.com/end-gpu-underutilization-achieve-peak-efficiency/

End GPU underutilization: Achieve peak efficiency

AI and deep learning inference demand powerful AI accelerators, but are you truly maximizing yours?

GPUs often operate at a mere 30-40% utilization, squandering valuable silicon, budget, and energy.

In this live session, NeuReality’s Field CTO, Iddo Kadim, tackles the critical challenge of maximizing AI accelerator capability. Whether you build, borrow, or buy AI acceleration – this is a must-attend.

Date: Thursday, December 5 Time: 10 AM PST | 5 PM GMT Location: Online

Iddo will reveal a multi-faceted approach encompassing intelligent software, optimized APIs, and efficient AI inference instructions to unlock benchmark-shattering performance for ANY AI accelerator.

The result?

You’ll get more from the GPUs buy, rather than buying more GPUs to make up for the limitations of today’s CPU and NIC-reliant inference architectures. And, you’ll likely achieve superior system performance within your current energy and cost constraints. 

Your key takeaways:

The urgency of GPU optimization: Is mediocre utilization hindering your AI initiatives? Discover new approaches to achieve 100% utilization with superior performance per dollar and per watt leading to greater energy efficiency.

Factors impacting utilization: Master the key metrics that influence GPU utilization: compute usage, memory usage, and memory bandwidth.

Beyond hardware: Harness the power of intelligent software and APIs. Optimize AI data pre-processing, compute graphs, and workload routing to maximize your AI accelerator (XPU, ASIC, FPGA) investments.

Smart options to explore: Uncover the root causes of underutilized AI accelerators and explore modern solutions to remedy them. You’ll get a summary of recent LLM real-world performance results – made possible by pairing NeuReality’s NR1 server-on-a-chip with any GPU or AI accelerator.

You spent a fortune on your GPUs – don’t let them sit idle for any amount of time.