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ffs non stop stuttering is back anyone had this issue? games unplayable for me right before IEM sydney😪 CPU AMD ryzen 9 5900x and GPU NVIDIA geforce RTX 2080 Ti. Stutters on main menu also

Simon

4,366 subscribers

118,591 Aufrufe • vor 2 Jahren •via X (Twitter)

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11 Kommentare

Profilbild von Oli Tierney

Oli Tierneyvor 2 Jahren

Just wanted to let you know I've never had this issue and I dont know how to help you. Cheers mate, good luck.

Profilbild von Simon

Simonvor 2 Jahren

Hahaha u prick

Profilbild von ropz

ropzvor 2 Jahren

ripbozo

Profilbild von ESL Counter-Strike

ESL Counter-Strikevor 2 Jahren

We spoke to our chief of stuttering science counter-strike viceroy and there is no fix. jks, can someone pls help Simon? @fREQUENCYCS @ThourCS

Profilbild von Merks

Merksvor 2 Jahren

Hey I'd be happy to take a look if you'd like

Profilbild von ESL Counter-Strike

ESL Counter-Strikevor 2 Jahren

@sico_csgo vouch

Profilbild von TjP

TjPvor 2 Jahren

Unfortunate go next :)

Profilbild von MacMate 🇪🇺

MacMate 🇪🇺vor 2 Jahren

This video from @NartOutHere might be able to help you brother. It fixed a bunch of my issues.

Profilbild von Chris JJ Magnum

Chris JJ Magnumvor 2 Jahren

try this video bro

Profilbild von Skirmy 😼

Skirmy 😼vor 2 Jahren

possible GPU issue? Im rocking a 3060 Ti with 5900X and have no stuttering issues at all

Profilbild von Simon

Simonvor 2 Jahren

yeah 2080ti could be doggy for cs2, guess it's pretty old now?

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AMD CEO LISA SU HELD A MINI PC ON STAGE THAT RUNS A 235B MODEL AND REPLACES YOUR $440/MONTH AI STACK amd's ryzen ai max+ 395 is the first x86 chip that runs a 200 billion parameter model on one piece of silicon. cpu and gpu share 128gb of unified memory, no separate graphics card needed the gmktec evo-x2 runs qwen3 235b fully, deepseek v3 comfortably and llama 3.3 70b with headroom. on linux you get 110gb of usable vram out of 128gb amd claimed the chip beat an nvidia rtx 5080 by more than 3x on deepseek r1 inference. a lunchbox sized pc outrunning a $1,000 discrete gpu on a real ai workload a heavy ai user pays $200 for claude code max, $200 for chatgpt pro, $20 for cursor and $20 for gemini. that's $5,280 a year and the box pays itself off in 9 to 10 months install ollama, pull the model, point claude code at localhost. same interface, nothing leaves the machine, nothing costs per request bookmark this and read the article below

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A lower price strategy helps AMD achieve this by increasing adoption among consumers and enterprises. ~Gaming GPUs: By focusing on mainstream GPUs with competitive pricing (e.g., targeting an 80% addressable market rather than the high-end 10%), AMD aims to build a larger user base. This scale encourages developers to optimize games for AMD’s technologies, such as FSR 3 (FidelityFX Super Resolution) and Anti-Lag 2, improving the ecosystem and competitiveness against Nvidia’s CUDA platform. ~Open Ecosystem in AI: AMD’s open-source ROCm platform contrasts with Nvidia’s proprietary CUDA, appealing to developers who prefer flexibility. Lower-priced hardware makes it easier for developers to adopt AMD’s solutions, fostering a broader AI software ecosystem. 4. Historical Context and Brand Positioning Since its founding in 1969, AMD has positioned itself as a challenger brand, often acting as a “second source” supplier to Intel. 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38,006 Aufrufe • vor 10 Monaten

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Once Human

1,832,730 Aufrufe • vor 2 Jahren

Jensen Huang just identified the next $200 billion market (Save this). The shift starts with a observation about agentic AI that changes everything about infrastructure. In the era of training and inference, the GPU was everything while CPU was a traffic cop, scheduling work, managing memory, dispatching tasks while the GPU did the heavy lifting. Agentic AI breaks that model entirely. An AI agent does not just run a single inference pass but rather it plans, calls tools, executes code in sandboxes, retrieves data from multiple sources and loops through complex multi-step reasoning sequences often thousands of times per second at scale. Every one of those operations runs through the CPU and the GPU sits idle waiting for the CPU to prepare the next task, supply the right context and execute the retrieval and tool calling logic fast enough to keep the accelerators fed. The CPU is now the conductor and the GPU is the orchestra and the bottleneck is the conductor falling behind. This is showing up in production AI factory utilization right now, which is exactly why Jensen built Vera from scratch rather than licensing x86. Vera achieves 40% lower peak memory latency than x86, 50% faster core to core communication, and 1.8 times the agentic sandbox performance of current x86 processors on a purpose-built architecture designed around the agentic loop. Now here is where the investment thesis gets interesting. The obvious beneficiary is Nvidia itself, and that thesis is real. Nvidia's CFO has guided for nearly $20 billion in Vera CPU revenue this fiscal year alone, a market Nvidia had zero presence in just three years ago. Intel held 60% of server CPU market share as recently as Q4 2025 and that transition is now happening at a pace Intel structurally cannot respond to. But the deeper question is, what architecture is Vera actually built on? Vera's Olympus cores are ARM compatible and every single Vera CPU deployed in every Vera Rubin rack in every data center in the world runs on ARM architecture. And ARM Holdings collects a royalty on every one of them. ARM does not make chips but rather licenses the instruction set architecture and CPU core designs that others build on top of. Every time Nvidia ships a Vera CPU, every time a hyperscaler deploys a Vera Rubin rack, every time an enterprise qualifies Vera for their AI factory, ARM earns a royalty. The secular tailwind here is almost perfectly constructed for ARM's business model. Amazon's Graviton, Microsoft's Cobalt, Google's Axion, Apple's silicon stack, and Qualcomm's data center push all run on ARM. And now Nvidia's Vera, which is projected to displace Intel as the largest server CPU supplier by revenue in a single fiscal year, is ARM. ARM's royalty rate on high end server chips is estimated at roughly 1 to 2% of chip selling price. At $5,000 per Vera CPU and 4 million units projected for FY2027, that is a royalty line growing from near zero to potentially $400 million to $800 million annually from Nvidia's data center CPU business alone before counting Amazon, Microsoft, Google, Apple, and Qualcomm. The total ARM addressable royalty base across all the silicon it already licenses is compounding at a rate that the current $130 billion market cap does not fully reflect. Jensen's CPU thesis is the most underappreciated catalyst in ARM's fundamental story, and the royalty compounding has barely started. Come join Milk Road Pro and get our full ARM royalty model and our entire AI trade thesis. Link below!

Jensen Huang just identified the next $200 billion market (Save this). The shift starts with a observation about agentic AI that changes everything about infrastructure. In the era of training and inference, the GPU was everything while CPU was a traffic cop, scheduling work, managing memory, dispatching tasks while the GPU did the heavy lifting. Agentic AI breaks that model entirely. An AI agent does not just run a single inference pass but rather it plans, calls tools, executes code in sandboxes, retrieves data from multiple sources and loops through complex multi-step reasoning sequences often thousands of times per second at scale. Every one of those operations runs through the CPU and the GPU sits idle waiting for the CPU to prepare the next task, supply the right context and execute the retrieval and tool calling logic fast enough to keep the accelerators fed. The CPU is now the conductor and the GPU is the orchestra and the bottleneck is the conductor falling behind. This is showing up in production AI factory utilization right now, which is exactly why Jensen built Vera from scratch rather than licensing x86. Vera achieves 40% lower peak memory latency than x86, 50% faster core to core communication, and 1.8 times the agentic sandbox performance of current x86 processors on a purpose-built architecture designed around the agentic loop. Now here is where the investment thesis gets interesting. The obvious beneficiary is Nvidia itself, and that thesis is real. Nvidia's CFO has guided for nearly $20 billion in Vera CPU revenue this fiscal year alone, a market Nvidia had zero presence in just three years ago. Intel held 60% of server CPU market share as recently as Q4 2025 and that transition is now happening at a pace Intel structurally cannot respond to. But the deeper question is, what architecture is Vera actually built on? Vera's Olympus cores are ARM compatible and every single Vera CPU deployed in every Vera Rubin rack in every data center in the world runs on ARM architecture. And ARM Holdings collects a royalty on every one of them. ARM does not make chips but rather licenses the instruction set architecture and CPU core designs that others build on top of. Every time Nvidia ships a Vera CPU, every time a hyperscaler deploys a Vera Rubin rack, every time an enterprise qualifies Vera for their AI factory, ARM earns a royalty. The secular tailwind here is almost perfectly constructed for ARM's business model. Amazon's Graviton, Microsoft's Cobalt, Google's Axion, Apple's silicon stack, and Qualcomm's data center push all run on ARM. And now Nvidia's Vera, which is projected to displace Intel as the largest server CPU supplier by revenue in a single fiscal year, is ARM. ARM's royalty rate on high end server chips is estimated at roughly 1 to 2% of chip selling price. At $5,000 per Vera CPU and 4 million units projected for FY2027, that is a royalty line growing from near zero to potentially $400 million to $800 million annually from Nvidia's data center CPU business alone before counting Amazon, Microsoft, Google, Apple, and Qualcomm. The total ARM addressable royalty base across all the silicon it already licenses is compounding at a rate that the current $130 billion market cap does not fully reflect. Jensen's CPU thesis is the most underappreciated catalyst in ARM's fundamental story, and the royalty compounding has barely started. Come join Milk Road Pro and get our full ARM royalty model and our entire AI trade thesis. Link below!

Milk Road AI

11,819 Aufrufe • vor 1 Monat

So, a lot of you have been talking about Wuthering Waves' optimization. Let me explain. 5700G (16 MB L3 Cache) + RX 9060 XT (FSR Quality) vs 5800X3D (96 MB 3D V-Cache) + RTX 5060 Ti (DLSS Quality) Note: I even increased the graphics settings on the 5060 Ti to show the difference. Explanation: Both GPUs deliver similar performance overall, but you'll notice higher FPS on the right side. Why? The Ryzen 7 5800X3D's 96 MB 3D V-Cache significantly improves performance in Wuthering Waves, especially 1% lows and overall frame consistency. In my testing, the performance improvement was around 40% compared to the 5700G. One reason is that Wuthering Waves is very CPU-sensitive, particularly in open-world areas. There are also optimization issues that Kuro Games still needs to address. I'm not trying to downplay anyone's PC. I'm simply pointing out that Wuthering Waves benefits a lot from CPUs with larger L3 cache, similar to games like Crimson Desert, STALKER 2, and Black Myth: Wukong. PS5 Performance Kuro Games still needs to improve the PS5 and PS5 Pro versions. Even some demanding games perform better on those consoles, so there's definitely room for optimization. I hope this helps! If you have any questions or want the best settings for your PC, feel free to ask. #WutheringWaves #Gacha

So, a lot of you have been talking about Wuthering Waves' optimization. Let me explain. 5700G (16 MB L3 Cache) + RX 9060 XT (FSR Quality) vs 5800X3D (96 MB 3D V-Cache) + RTX 5060 Ti (DLSS Quality) Note: I even increased the graphics settings on the 5060 Ti to show the difference. Explanation: Both GPUs deliver similar performance overall, but you'll notice higher FPS on the right side. Why? The Ryzen 7 5800X3D's 96 MB 3D V-Cache significantly improves performance in Wuthering Waves, especially 1% lows and overall frame consistency. In my testing, the performance improvement was around 40% compared to the 5700G. One reason is that Wuthering Waves is very CPU-sensitive, particularly in open-world areas. There are also optimization issues that Kuro Games still needs to address. I'm not trying to downplay anyone's PC. I'm simply pointing out that Wuthering Waves benefits a lot from CPUs with larger L3 cache, similar to games like Crimson Desert, STALKER 2, and Black Myth: Wukong. PS5 Performance Kuro Games still needs to improve the PS5 and PS5 Pro versions. Even some demanding games perform better on those consoles, so there's definitely room for optimization. I hope this helps! If you have any questions or want the best settings for your PC, feel free to ask. #WutheringWaves #Gacha

Kaito

33,285 Aufrufe • vor 3 Tagen

I had to test it myself to believe this unreal inference speed. 3,000 tokens/s for 1 user on standard datacenter GPUs. They leveraged a hidden efficiency gap in how GPUs generate tokens. Kog just achieved 3,000 tokens/s on 8× AMD MI300X GPUs and 2,100 on 8× NVIDIA H200 (FP16, no speculative decoding). Their tech preview is on a 2B model, and they show how their techniques will scale to large frontier MoE models at similar speeds. That's a huge number because normal low-batch GPU decoding for 2B to 8B models is usually closer to 100 to 300 tokens/s per request, so Kog is claiming something like a 10X to 30X jump in the speed one user actually feels. Their trick: they are getting the speed by treating LLM decoding as a memory streaming problem, not mainly a math problem. For 1 user at batch size 1, the GPU is not doing big, efficient matrix-matrix work like in training or large-batch serving; it is repeatedly pulling the model’s active weights from high-bandwidth memory for each new token, so speed depends on how smoothly those weights keep flowing. Normal inference stacks keep breaking that flow. They run many separate GPU programs for different parts of the model, move intermediate results through memory, wait at synchronization points, talk back to the CPU for scheduling or sampling, and then repeat this token after token. Kog’s answer is to co-design 3 things that are usually tuned separately: the runtime, the low-level GPU code, and the model architecture. The biggest engineering move is the monokernel, where the whole decode pass runs as 1 persistent GPU-resident program, including sampling, so the system does not keep stopping for kernel launches, CPU scheduling, and intermediate memory round trips. They also rebuilt synchronization, because their own measurements say grid sync was eating around 35% of token-generation time; instead of making every compute unit wait at a broad barrier, each unit waits only for the exact data it needs. On AMD MI300X, they also map memory access around the chiplet layout, because memory latency changes depending on which die makes the request. Then their Laneformer model uses Delayed Tensor Parallelism, which lets cross-GPU communication happen in the background instead of blocking every layer.

I had to test it myself to believe this unreal inference speed. 3,000 tokens/s for 1 user on standard datacenter GPUs. They leveraged a hidden efficiency gap in how GPUs generate tokens. Kog just achieved 3,000 tokens/s on 8× AMD MI300X GPUs and 2,100 on 8× NVIDIA H200 (FP16, no speculative decoding). Their tech preview is on a 2B model, and they show how their techniques will scale to large frontier MoE models at similar speeds. That's a huge number because normal low-batch GPU decoding for 2B to 8B models is usually closer to 100 to 300 tokens/s per request, so Kog is claiming something like a 10X to 30X jump in the speed one user actually feels. Their trick: they are getting the speed by treating LLM decoding as a memory streaming problem, not mainly a math problem. For 1 user at batch size 1, the GPU is not doing big, efficient matrix-matrix work like in training or large-batch serving; it is repeatedly pulling the model’s active weights from high-bandwidth memory for each new token, so speed depends on how smoothly those weights keep flowing. Normal inference stacks keep breaking that flow. They run many separate GPU programs for different parts of the model, move intermediate results through memory, wait at synchronization points, talk back to the CPU for scheduling or sampling, and then repeat this token after token. Kog’s answer is to co-design 3 things that are usually tuned separately: the runtime, the low-level GPU code, and the model architecture. The biggest engineering move is the monokernel, where the whole decode pass runs as 1 persistent GPU-resident program, including sampling, so the system does not keep stopping for kernel launches, CPU scheduling, and intermediate memory round trips. They also rebuilt synchronization, because their own measurements say grid sync was eating around 35% of token-generation time; instead of making every compute unit wait at a broad barrier, each unit waits only for the exact data it needs. On AMD MI300X, they also map memory access around the chiplet layout, because memory latency changes depending on which die makes the request. Then their Laneformer model uses Delayed Tensor Parallelism, which lets cross-GPU communication happen in the background instead of blocking every layer.

Rohan Paul

13,148 Aufrufe • vor 1 Monat

🗣️“I smoked the record, and Kiss never did a song with me again... Y'all gonna have to reorder your top fives.” For anyone confused... this BagFuel interview started this Jadakiss and 38 Spesh beef, leading right back to Benny’s “Sunday School.” 🎥 Slide 1: Spesh talking spicy on BagFuel 🎧 Slide 2: Spesh’s verse 🎧 Slide 3: Kiss's verse 🎧 Slide 4: Full "Sunday School" track Spesh is like that, and so is Kiss. Is this a back-and-forth you'd wanna see? Who actually had the best verse on Sunday School? 👇

🗣️“I smoked the record, and Kiss never did a song with me again... Y'all gonna have to reorder your top fives.” For anyone confused... this BagFuel interview started this Jadakiss and 38 Spesh beef, leading right back to Benny’s “Sunday School.” 🎥 Slide 1: Spesh talking spicy on BagFuel 🎧 Slide 2: Spesh’s verse 🎧 Slide 3: Kiss's verse 🎧 Slide 4: Full "Sunday School" track Spesh is like that, and so is Kiss. Is this a back-and-forth you'd wanna see? Who actually had the best verse on Sunday School? 👇

BILLY

61,455 Aufrufe • vor 3 Tagen

My Launch Day Mindseye thoughts. I’m playing on ps5 pro. I’ve experienced no bugs or glitches. Only time frame dropped was during a sandstorm. The driving is smooth and handles really well. Doesn’t feel tanky like other games. The combat is the highlight for me. The shooting feels so crisp and responsive and the adaptive triggers feel great. The movement is swaggy and the slide into cover is tuff. Had 1 crash but forbidden west and halo infinite also crashed on before so this is a nothing burger. This why I’m glad I think for myself and don’t listen to idiots trynna fit in and chase engagement on this app.

My Launch Day Mindseye thoughts. I’m playing on ps5 pro. I’ve experienced no bugs or glitches. Only time frame dropped was during a sandstorm. The driving is smooth and handles really well. Doesn’t feel tanky like other games. The combat is the highlight for me. The shooting feels so crisp and responsive and the adaptive triggers feel great. The movement is swaggy and the slide into cover is tuff. Had 1 crash but forbidden west and halo infinite also crashed on before so this is a nothing burger. This why I’m glad I think for myself and don’t listen to idiots trynna fit in and chase engagement on this app.

Maximum Spider 2099

182,606 Aufrufe • vor 1 Jahr