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The infamous hyperspeed bug in Peter Jackson's King Kong is now fixed! The game tracks time with CPU cycles, but casts the frequency to a 32 bit value. On modern high-clock CPUs that overflows, so the engine thinks your CPU is way slower than it actually is. It then... show more

Fusion Fix

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72,290 views • 5 months ago •via X (Twitter)

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I asked Codex to design its own CPU and then write a Space Invaders-style game for it in assembly and after a while working on it this is the result Now the real question is... can it run Minecraft?

I asked Codex to design its own CPU and then write a Space Invaders-style game for it in assembly and after a while working on it this is the result Now the real question is... can it run Minecraft?

Angel 🌼

206,525 views • 4 days ago

Microsoft sold every spare CPU it had to Anthropic and OpenAI. Amazon tripled its CPU buys year over year and still can't keep up. Two of AWS's biggest customers asked Andy Jassy if they could buy the entire 2026 production run of Graviton chips. He said no. The ratio inside an AI datacenter used to be 100 megawatts of GPUs to 1 megawatt of CPUs. CPUs handled storage, checkpointing, pre-processing. Light work. GPUs did the actual training and inference. Then OpenAI shipped o1-preview in September 2024. RL post-training went from "check the model output with a regex" to "run classifiers" to "compile the code and run the unit tests" to "spin up a sandbox, call three databases, run a physics simulation, verify the answer." Every rollout now needs a CPU-backed environment to verify against. Codex 5.4 runs agentically for 6-7 hours at a time. Each database call, each cron job, each scraped URL is CPU work. Coding agent revenue went from a couple billion to north of $10B in six months. That compute is sitting on CPUs. The CPU to GPU ratio is now approaching 1:1. The entire global cloud was built for 1:8. That's why GitHub has been unstable for weeks. Nvidia and Arm both announced they're entering the server CPU market in March. TSMC will only meet 80% of server CPU wafer demand this year. High-end server CPU prices are already up 50%. When the GPU king and the IP licensor both pivot to CPUs in the same month, the boring chip isn't boring anymore.

Microsoft sold every spare CPU it had to Anthropic and OpenAI. Amazon tripled its CPU buys year over year and still can't keep up. Two of AWS's biggest customers asked Andy Jassy if they could buy the entire 2026 production run of Graviton chips. He said no. The ratio inside an AI datacenter used to be 100 megawatts of GPUs to 1 megawatt of CPUs. CPUs handled storage, checkpointing, pre-processing. Light work. GPUs did the actual training and inference. Then OpenAI shipped o1-preview in September 2024. RL post-training went from "check the model output with a regex" to "run classifiers" to "compile the code and run the unit tests" to "spin up a sandbox, call three databases, run a physics simulation, verify the answer." Every rollout now needs a CPU-backed environment to verify against. Codex 5.4 runs agentically for 6-7 hours at a time. Each database call, each cron job, each scraped URL is CPU work. Coding agent revenue went from a couple billion to north of $10B in six months. That compute is sitting on CPUs. The CPU to GPU ratio is now approaching 1:1. The entire global cloud was built for 1:8. That's why GitHub has been unstable for weeks. Nvidia and Arm both announced they're entering the server CPU market in March. TSMC will only meet 80% of server CPU wafer demand this year. High-end server CPU prices are already up 50%. When the GPU king and the IP licensor both pivot to CPUs in the same month, the boring chip isn't boring anymore.

Aakash Gupta

290,767 views • 4 months ago

xAI co-founder and chief engineer: "you have your Grok bot agent attempt many solutions, you collect the rewards then you use reinforcement learning to update the weights. that loop is the whole thing" he built Colossus, the cluster everyone else quoted at over a year, by refusing to hire subcontractors of subcontractors on the Cursor acquisition: the data matters, but the real value is the RL environments - hand the agent a bug, then check whether it fixed it and whether it fixed it the right way Watch the talk, then read the article below.

xAI co-founder and chief engineer: "you have your Grok bot agent attempt many solutions, you collect the rewards then you use reinforcement learning to update the weights. that loop is the whole thing" he built Colossus, the cluster everyone else quoted at over a year, by refusing to hire subcontractors of subcontractors on the Cursor acquisition: the data matters, but the real value is the RL environments - hand the agent a bug, then check whether it fixed it and whether it fixed it the right way Watch the talk, then read the article below.

Carnage

27,541 views • 7 days ago

*** SEGA GENESIS/MEGADRIVE - SCALING PART 2 *** Whats better on a Friday than seeing some scaling effects on the Megadrive ? .. nothing, so read on ... In the previous part we were software scaling in either axis by a given factor, but I couldn't change the scale factor per scanline on the same frame, so I couldn't skew the base image for tilting into screen eg Mode 7 type effects. - One issue was it was scaling column by column which got wrecked for performance trying per scanline adjustments. So I converted to a row scaler rather than column, this allowed expansions and contractions on a per scanline basis. - Speed up the code significantly , now running a sprite load + music + scaling on cpu with ~50% cpu free. Relied heavily on Python scripts I wrote to generate the 68k assembly for this as it was mind numbing labourious. In doing so I got the scripts competing with each other to come up with the fastest method for each scale , I'm starting to like python ;-) - Tried all sorts to speed up the horizontal interupt - handles vertical scaling, exotic methods eg unsafe interupts / racing the beam & updating the vertical scroll every X cpu cycles, in the end the Z80 cpu (audio) stealing the 68k cpu bus intermittendly just killed these ideas as it would only be 95% stable, ended up just doing smarter interupts that saved a lot of cpu instead. - The good and the bad, spent too long on this is the bad, the good is we will need it in Lufthoheit in a few places to make up for that !! Carsten666 CYBERDEOUS - Crouzet Laurent #SGDK #SegaMegadrive #SegaGenesis

* SEGA GENESIS/MEGADRIVE - SCALING PART 2 * Whats better on a Friday than seeing some scaling effects on the Megadrive ? .. nothing, so read on ... In the previous part we were software scaling in either axis by a given factor, but I couldn't change the scale factor per scanline on the same frame, so I couldn't skew the base image for tilting into screen eg Mode 7 type effects. - One issue was it was scaling column by column which got wrecked for performance trying per scanline adjustments. So I converted to a row scaler rather than column, this allowed expansions and contractions on a per scanline basis. - Speed up the code significantly , now running a sprite load + music + scaling on cpu with ~50% cpu free. Relied heavily on Python scripts I wrote to generate the 68k assembly for this as it was mind numbing labourious. In doing so I got the scripts competing with each other to come up with the fastest method for each scale , I'm starting to like python ;-) - Tried all sorts to speed up the horizontal interupt - handles vertical scaling, exotic methods eg unsafe interupts / racing the beam & updating the vertical scroll every X cpu cycles, in the end the Z80 cpu (audio) stealing the 68k cpu bus intermittendly just killed these ideas as it would only be 95% stable, ended up just doing smarter interupts that saved a lot of cpu instead. - The good and the bad, spent too long on this is the bad, the good is we will need it in Lufthoheit in a few places to make up for that !! Carsten666 CYBERDEOUS - Crouzet Laurent #SGDK #SegaMegadrive #SegaGenesis

Shannon Birt

11,070 views • 1 year ago

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 views • 2 months ago

#DBFZ_GKN So since Goku 236h is actually finally got fixed and now invulved the right way, u now beat superdash with it. Now there are interactions where superdash will miss you but it feels more rare than not(haven't tested it in a real match tho). He gets a phenomenal punish

#DBFZ_GKN So since Goku 236h is actually finally got fixed and now invulved the right way, u now beat superdash with it. Now there are interactions where superdash will miss you but it feels more rare than not(haven't tested it in a real match tho). He gets a phenomenal punish

McBenedict

12,285 views • 8 months ago

"It's just that my life is so overheated. I lived a hard life. I ran it all the way and the CPU stopped. The screen froze and the keys stopped working. I had no choice but to turn it off and on again." The analogy with computers thooo 😔 #LoveNextDoor #LoveNextDoorEp1

"It's just that my life is so overheated. I lived a hard life. I ran it all the way and the CPU stopped. The screen froze and the keys stopped working. I had no choice but to turn it off and on again." The analogy with computers thooo 😔 #LoveNextDoor #LoveNextDoorEp1

alex

112,079 views • 2 years ago

More grounded movement, increased ball freedom, modern football dynamics (0:11) and a more dangerous & unpredictable CPU... these are just a few of the elements you can expect in V9 of my #FC26 realistic gameplay mod. 🧩 Plug & Play Standalones: Complete gameplay mods with unique movement balances. Choose the one that suits you. ⚙️ 650+ Gameplay Options: Fine-tune every aspect of the standalone to find your sweet spot. 🔴 This is realism your way ➡️Join and download now: #EAFC26 #EASPORTS #EAFC #FIFA #footballgaming #footballhighlights #anthjames #EASportsFC26 #FC27 #EAFC27 #fc26mods #authenticgameplay

More grounded movement, increased ball freedom, modern football dynamics (0:11) and a more dangerous & unpredictable CPU... these are just a few of the elements you can expect in V9 of my #FC26 realistic gameplay mod. 🧩 Plug & Play Standalones: Complete gameplay mods with unique movement balances. Choose the one that suits you. ⚙️ 650+ Gameplay Options: Fine-tune every aspect of the standalone to find your sweet spot. 🔴 This is realism your way ➡️Join and download now: #EAFC26 #EASPORTS #EAFC #FIFA #footballgaming #footballhighlights #anthjames #EASportsFC26 #FC27 #EAFC27 #fc26mods #authenticgameplay

Anth James Football Gaming

11,529 views • 1 month ago

Why the character movement in my custom game engine felt janky and how I fixed it. In a game engine, most often, a character moves using the physics engine. Meaning, the player is not just a coordinate in space but a physical body. It has velocity, it handles collisions, and it interacts with the world. Now, as you might know, physics engines need stability. If you run them at variable framerates, things start breaking. Objects phase through walls or fly off into space because the math becomes unpredictable. This is why most game engines lock their physics loop to a 60Hz fixed rate. But here’s the problem: If you have a high-end system, you don't want to limit it at 60 FPS. That's a waste of good hardware. Now, that said, if the GPU is rendering at 144 FPS but the player's position (physics driven) only updates 60 times a second, it creates a micro-stutter that ruins the "smooth" feel of the game. A good way to fix this is to treat the character as two separate things: 1. The Physics Body (Invisible part): This is the "real" character. It lives in the 60Hz physics world, it moves the player and handles collisions. 2. The Visual Model and Camera (Visible part): This is what the player actually sees. It doesn't care about collisions, its only job is to look nice and smooth at whatever framerate the GPU is pushing. Once you have this separation, you can use interpolation to keep them in sync. Every time the physics clock ticks, you save the previous position of the invisible body before moving it to the new one. Between those ticks, calculate how far we are between the last physics update and the next one. By using this to drive the visible parts of the game, the stutters disappear. The physics loop stays fixed behind the scenes, while the visuals slide smoothly between the snapshots. Example: - Right after a tick: blend_weight= 0.0 (The visual model stays at the old physics position). - Halfway to the next: blend_weight= 0.5 (The visual model slides to the middle point). - Just before the next: blend_weight= 0.9 (The visual model is almost at the new physics position). Pro-Tip A critical mistake I made initially, and one many devs make, is parenting the camera and visible parts directly to the player body. If you do this, the camera inherits the discrete 60Hz physics movement by default. In that setup, interpolation won't work because the camera is "stuck" to the physics clock. For this fix to work you must decouple the camera and visuals from the body and move them separately. Player movement processing in Detis Engine: - fixed_process: Physics runs at 60Hz. Handles collisions and raw movement. - process: Variable rate. Mainly used for player input caching in the player case. - late_process: Variable rate. Handles interpolated camera movement after physics and everything else is done being processed. - render. Submits the final interpolated transforms to the GPU. The test environment in the video is running on an old 2070-based laptop. Hopefully the video compression won't introduce any stutter... I’m sharing this in hopes it helps a fellow dev. Cheers.

Why the character movement in my custom game engine felt janky and how I fixed it. In a game engine, most often, a character moves using the physics engine. Meaning, the player is not just a coordinate in space but a physical body. It has velocity, it handles collisions, and it interacts with the world. Now, as you might know, physics engines need stability. If you run them at variable framerates, things start breaking. Objects phase through walls or fly off into space because the math becomes unpredictable. This is why most game engines lock their physics loop to a 60Hz fixed rate. But here’s the problem: If you have a high-end system, you don't want to limit it at 60 FPS. That's a waste of good hardware. Now, that said, if the GPU is rendering at 144 FPS but the player's position (physics driven) only updates 60 times a second, it creates a micro-stutter that ruins the "smooth" feel of the game. A good way to fix this is to treat the character as two separate things: 1. The Physics Body (Invisible part): This is the "real" character. It lives in the 60Hz physics world, it moves the player and handles collisions. 2. The Visual Model and Camera (Visible part): This is what the player actually sees. It doesn't care about collisions, its only job is to look nice and smooth at whatever framerate the GPU is pushing. Once you have this separation, you can use interpolation to keep them in sync. Every time the physics clock ticks, you save the previous position of the invisible body before moving it to the new one. Between those ticks, calculate how far we are between the last physics update and the next one. By using this to drive the visible parts of the game, the stutters disappear. The physics loop stays fixed behind the scenes, while the visuals slide smoothly between the snapshots. Example: - Right after a tick: blend_weight= 0.0 (The visual model stays at the old physics position). - Halfway to the next: blend_weight= 0.5 (The visual model slides to the middle point). - Just before the next: blend_weight= 0.9 (The visual model is almost at the new physics position). Pro-Tip A critical mistake I made initially, and one many devs make, is parenting the camera and visible parts directly to the player body. If you do this, the camera inherits the discrete 60Hz physics movement by default. In that setup, interpolation won't work because the camera is "stuck" to the physics clock. For this fix to work you must decouple the camera and visuals from the body and move them separately. Player movement processing in Detis Engine: - fixed_process: Physics runs at 60Hz. Handles collisions and raw movement. - process: Variable rate. Mainly used for player input caching in the player case. - late_process: Variable rate. Handles interpolated camera movement after physics and everything else is done being processed. - render. Submits the final interpolated transforms to the GPU. The test environment in the video is running on an old 2070-based laptop. Hopefully the video compression won't introduce any stutter... I’m sharing this in hopes it helps a fellow dev. Cheers.

Ioannis Koukourakis

48,703 views • 7 months ago

A guy who tested every AI Halo mini PC on the market picked a winner: The Minisforum MS-S1 Max They all run the same AMD Ryzen AI Max+ 395 with 128GB unified memory, so the decision came down to everything around the chip What won it: dual 10-gig Ethernet, 80 Gbit/s USB4 V2, internal power supply, a low-profile PCIe slot the competition skipped The overlooked detail: the CPU/GPU memory split quietly changes CPU performance by 15% on identical silicon The honest comparison: NVIDIA's GB10 boxes have nearly closed the price gap and still lead on software maturity. AMD's edge is Windows, gaming, and expansion in one machine The takeaway is not which box is fastest It is that local AI hardware is now a commodity, and the real differences are the boring ones: ports, service, and software

plutos

97,428 views • 2 months ago

PowerInfer - a high-speed inference engine for deploying LLMs locally. Just came across this super interesting project on speeding up inference. It's not MoE but it's a simple approach that exploits the high locality in LLM inference to design a GPU-CPU hybrid inference engine. Hot-activated neurons are preloaded onto the GPU for fast access, while cold-activated neurons (the majority) are computed on the CPU. This approach significantly reduces GPU memory demands and CPU-GPU data transfer. It achieves an average token generation rate of 13.20 tokens/s, with a peak of 29.08 tokens/s, across various LLMs on a single NVIDIA RTX 4090 GPU. It's on only 18% lower than that achieved by a top-tier server-grade A100 GPU. It also significantly outperforms llama.cpp by up to 11.69x while retaining model accuracy. There is a lot more innovation around inference that's coming fast. Really encouraged by the study on sparse computation to enhance the computational efficiency of LLMs. It's now possible to use PowerInfer with Llama 2 and Faclon 40B. Mistral-7B support is coming soon!

PowerInfer - a high-speed inference engine for deploying LLMs locally. Just came across this super interesting project on speeding up inference. It's not MoE but it's a simple approach that exploits the high locality in LLM inference to design a GPU-CPU hybrid inference engine. Hot-activated neurons are preloaded onto the GPU for fast access, while cold-activated neurons (the majority) are computed on the CPU. This approach significantly reduces GPU memory demands and CPU-GPU data transfer. It achieves an average token generation rate of 13.20 tokens/s, with a peak of 29.08 tokens/s, across various LLMs on a single NVIDIA RTX 4090 GPU. It's on only 18% lower than that achieved by a top-tier server-grade A100 GPU. It also significantly outperforms llama.cpp by up to 11.69x while retaining model accuracy. There is a lot more innovation around inference that's coming fast. Really encouraged by the study on sparse computation to enhance the computational efficiency of LLMs. It's now possible to use PowerInfer with Llama 2 and Faclon 40B. Mistral-7B support is coming soon!

elvis

261,622 views • 2 years ago

I've been playing The Killing Antidote by OUR VIDEO GAME IS OUT NOW! and I know it looks like yet another eye-candy game but it actually has some solid gameplay. The combat feels decent, the world is fun to explore and it is not just about the aesthetic… though I have to admit, it doesn't hurt (I am biased, I like fan service). Still a bit rough since it is in Early Access but definitely has potential!

I've been playing The Killing Antidote by OUR VIDEO GAME IS OUT NOW! and I know it looks like yet another eye-candy game but it actually has some solid gameplay. The combat feels decent, the world is fun to explore and it is not just about the aesthetic… though I have to admit, it doesn't hurt (I am biased, I like fan service). Still a bit rough since it is in Early Access but definitely has potential!

Ruby

38,562 views • 1 year ago

Damage predicion on low ping is by far the biggest gameplay advantage you can get, assuming it is accurate of course. Shooting feedback and kill registration feel so nice but the fact that this is toggleable option goes to show that CS2's kill delay is still a thing and they know it. It is a day and night difference and when this gets properly implemented in the game, CS2 will feel CS2. CS2 !

Damage predicion on low ping is by far the biggest gameplay advantage you can get, assuming it is accurate of course. Shooting feedback and kill registration feel so nice but the fact that this is toggleable option goes to show that CS2's kill delay is still a thing and they know it. It is a day and night difference and when this gets properly implemented in the game, CS2 will feel CS2. CS2 !

elias

243,732 views • 6 months ago

$AMD will become a multi-trillion dollar company. The AI boom is no longer just about GPUs. In early AI data centers, the CPU-to-GPU ratio was around 1:8. Now it’s moving closer to 1:1 because agentic AI requires dramatically more CPU compute. Lisa Su: “Based on the demand signals we are seeing today and the structural increase in CPU compute requirements driven by agentic AI, we now expect the server CPU TAM to grow at greater than 35% annually, reaching over $120 billion by 2030.” Add to that the 6GW AI infrastructure deals with $META and OpenAI, plus deep collaboration on multiple future chip generations, and it becomes clear why $AMD is positioned to compound for years. I’m not chasing $AMD here after a +110% move in just over a month. But the long-term story remains intact.

$AMD will become a multi-trillion dollar company. The AI boom is no longer just about GPUs. In early AI data centers, the CPU-to-GPU ratio was around 1:8. Now it’s moving closer to 1:1 because agentic AI requires dramatically more CPU compute. Lisa Su: “Based on the demand signals we are seeing today and the structural increase in CPU compute requirements driven by agentic AI, we now expect the server CPU TAM to grow at greater than 35% annually, reaching over $120 billion by 2030.” Add to that the 6GW AI infrastructure deals with $META and OpenAI, plus deep collaboration on multiple future chip generations, and it becomes clear why $AMD is positioned to compound for years. I’m not chasing $AMD here after a +110% move in just over a month. But the long-term story remains intact.

Daniel

39,622 views • 3 months ago

Lisa Su just dropped a massive reality check on the state of the AI hardware scramble, and the sheer scale of what is coming is wild. If you asked her six months ago, demand was just "strong." But now? The entire landscape has shifted into absolute overdrive. She specifically noted that in the last 60 to 90 days, there has been a massive acceleration, especially when looking at the forecasts and demand patterns for 2026. The most insane takeaway is her blunt assessment that "there's not enough compute out there for everything that wants to be done." It is a complete bottleneck of global ambition versus available silicon. What is really flying under the radar here is that this isn't exclusively a GPU story anymore. The desire to ramp compute quickly for heavy AI enterprise workloads is actively pulling massive CPU content right along with it. The demand is practically insatiable across the entire board. The heavyweights are scrambling, the trenches are fighting for allocation, and the hardware supercycle is officially accelerating to a whole new level.

Lisa Su just dropped a massive reality check on the state of the AI hardware scramble, and the sheer scale of what is coming is wild. If you asked her six months ago, demand was just "strong." But now? The entire landscape has shifted into absolute overdrive. She specifically noted that in the last 60 to 90 days, there has been a massive acceleration, especially when looking at the forecasts and demand patterns for 2026. The most insane takeaway is her blunt assessment that "there's not enough compute out there for everything that wants to be done." It is a complete bottleneck of global ambition versus available silicon. What is really flying under the radar here is that this isn't exclusively a GPU story anymore. The desire to ramp compute quickly for heavy AI enterprise workloads is actively pulling massive CPU content right along with it. The demand is practically insatiable across the entire board. The heavyweights are scrambling, the trenches are fighting for allocation, and the hardware supercycle is officially accelerating to a whole new level.

Ian Miles Cheong

100,225 views • 4 months ago

V2 of my #EAFC25 Realistic Gameplay Mod is OUT!!!!!!!!!!!! The first real taste of my #FC25 mod! + MAJOR positional changes + FC IQ is no longer RIGID! + NATURAL spacing and movement + ATTACKING fluidity and variety + SO much more This was has been some of the most challenging, but rewarding work since I started modding. A very large part of what you see in this video is CPU behaviour! Joing the 'Full Gameplay Package' over on Patreon to access V2 + 60+ OPTIONS to fine-tune your own gameplay experience! #FC25 #EASPORTS #EAFC #FIFA #PES #eFootball2025

V2 of my #EAFC25 Realistic Gameplay Mod is OUT!!!!!!!!!!!! The first real taste of my #FC25 mod! + MAJOR positional changes + FC IQ is no longer RIGID! + NATURAL spacing and movement + ATTACKING fluidity and variety + SO much more This was has been some of the most challenging, but rewarding work since I started modding. A very large part of what you see in this video is CPU behaviour! Joing the 'Full Gameplay Package' over on Patreon to access V2 + 60+ OPTIONS to fine-tune your own gameplay experience! #FC25 #EASPORTS #EAFC #FIFA #PES #eFootball2025

Anth James Football Gaming

77,453 views • 1 year ago

Jane Street engineers, in an internal tech talk on training performance: "GPUs are really slow at doing one floating point operation. It's around a microsecond. CPUs are 1,000 times faster than GPUs." Not a podcast. A talk given inside the firm that trades billions a day, to a room of engineers who already train models. The whole industry sells you the GPU as the fast thing. On a single operation it is a thousand times slower than the CPU sitting in your laptop. The GPU was never fast. It was wide. If your training loop feeds it one thing at a time, you are paying rack prices for the slowest chip in the building.

Jane Street engineers, in an internal tech talk on training performance: "GPUs are really slow at doing one floating point operation. It's around a microsecond. CPUs are 1,000 times faster than GPUs." Not a podcast. A talk given inside the firm that trades billions a day, to a room of engineers who already train models. The whole industry sells you the GPU as the fast thing. On a single operation it is a thousand times slower than the CPU sitting in your laptop. The GPU was never fast. It was wide. If your training loop feeds it one thing at a time, you are paying rack prices for the slowest chip in the building.

zostaff

12,502 views • 1 month ago

Windows 11’s hidden CPU boost is now available in the May optional update! I installed KB5089573 (Build 26200.8524) and tested the Low Latency Profile again. For now, this feature does not make your apps launch faster, as that particular change is not included in today's update. However, the Low Latency Profile currently makes OS flyouts load faster. This means the Start menu opens with less lag, and Action Center feels smoother. Plus, right-click menus show less of that annoying rendering delay. On slower PCs, this should matter even more because Windows briefly pushes the CPU harder during shell interactions, then lets it drop back to idle. Some people call this a lazy band-aid, but that misses the point. macOS and Linux already have similar tricks. This is how modern systems make interactions feel instant. The real win comes when Microsoft combines this with native WinUI 3 work and less WebView2 bloat. Windows 11 finally feels like it’s being tuned for responsiveness again.

Windows Latest

220,035 views • 3 months ago

BREAKING: The Digitoys mobile app on iOS is officially live! Everything you love about Digitoys is now available in a fast and easy to use app that lets you take your collection anywhere you go. Download it now via the link below 👇

BREAKING: The Digitoys mobile app on iOS is officially live! Everything you love about Digitoys is now available in a fast and easy to use app that lets you take your collection anywhere you go. Download it now via the link below 👇

Digitoys

15,836 views • 1 month ago