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Introducing a new KV format: VBR (Variable Bit Rate) VBR dynamically quantizes your KV cache layer-by-layer as your session grows, giving you the highest quality possible within your VRAM constraints. This is my dream format. Pushed to master. Available now. 1/15 🧵

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I just crammed the updated Gemma 4 26B A4B QAT (MoE) with 180k context into an 8GB RTX 4060 (8 GB VRAM + 16 GB RAM only!!) and optimized the batch size. 23 tokens/sec decode, 300 tokens/sec prefill Yesterday I showed you a Gemma 4 31B dense model running flawlessly on an RTX 4090. Today, we're breaking the VRAM bank on a budget card using Unsloth’s new Gemma 4 26B (A4B) QAT quants. Following Google’s chat template update that boosted agentic benchmarks by +10%, I pushed this model to its absolute limits. Here is how you squeeze 250k context out of 8GB of VRAM. # The Setup & The Optimization - Hardware: Nvidia RTX 4060 (8GB VRAM) + 16GB System RAM - Environment: CUDA 13.0 build of llama.cpp - Model: gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf - Prompt: 28,000 tokens of prompt for each run If you read my L2 cache breakdown (attached in replies), you know the 4060’s 24MB cache maxes out at `-b 1024 -ub 1024`. Push past that, and prefill crashes. I locked those flags in for every test below to ensure maximum GEMM throughput. # 1. The Raw Context Push (Unquantized KV Cache) First, I wanted to see how far pure 8GB VRAM + 16GB RAM could stretch without touching the KV cache: - 80k Context: Prefill 385 t/s | Decode 25.5 t/s - 120k Context: Prefill 270 t/s | Decode 24 t/s llama.cpp flags: .\llama-server -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -c 120000 --port 8080 -ub 1024 -b 1024 Without KV quantization, 120k is your hard ceiling. push past that prefill throughput drops off a cliff, making the model practically unusable for large agentic workloads. # 2. The Q8 KV Cache Lifeline To survive 250k context on a budget card, you have to quantize the KV cache. I enabled 8 bit KV cache (`-ctk q8_0 -ctv q8_0`) and re ran: - 180k Context: Prefill 280 t/s | Decode 22.8 t/s - 250k Context: Prefill 115 t/s | Decode 20 t/s llama.cpp flags: .\llama-server -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -c 180000 --port 8080 -b 1024 -ub 1024 -ctk q8_0 -ctv q8_0 Result: Q8 KV cache brings 250k context back from the dead. Decode speed stabilizes at a highly usable 20 t/s. You are trading a very small bit amount of reasoning precision for an extra 130,000 tokens of context window. if you own a single rtx 3050, 3060, 3070, 4050, 4060, 5050 or 5060, you must try this model and optimize your batch size for higher prefill. Hugging Face links to the updated Unsloth's QAT quants and performance graph are in the replies below. What model are you running on your 6GB, 8GB or 12GB cards right now? Let's see your setups.
0:20

I just crammed the updated Gemma 4 26B A4B QAT (MoE) with 180k context into an 8GB RTX 4060 (8 GB VRAM + 16 GB RAM only!!) and optimized the batch size. 23 tokens/sec decode, 300 tokens/sec prefill Yesterday I showed you a Gemma 4 31B dense model running flawlessly on an RTX 4090. Today, we're breaking the VRAM bank on a budget card using Unsloth’s new Gemma 4 26B (A4B) QAT quants. Following Google’s chat template update that boosted agentic benchmarks by +10%, I pushed this model to its absolute limits. Here is how you squeeze 250k context out of 8GB of VRAM. # The Setup & The Optimization - Hardware: Nvidia RTX 4060 (8GB VRAM) + 16GB System RAM - Environment: CUDA 13.0 build of llama.cpp - Model: gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf - Prompt: 28,000 tokens of prompt for each run If you read my L2 cache breakdown (attached in replies), you know the 4060’s 24MB cache maxes out at `-b 1024 -ub 1024`. Push past that, and prefill crashes. I locked those flags in for every test below to ensure maximum GEMM throughput. # 1. The Raw Context Push (Unquantized KV Cache) First, I wanted to see how far pure 8GB VRAM + 16GB RAM could stretch without touching the KV cache: - 80k Context: Prefill 385 t/s | Decode 25.5 t/s - 120k Context: Prefill 270 t/s | Decode 24 t/s llama.cpp flags: .\llama-server -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -c 120000 --port 8080 -ub 1024 -b 1024 Without KV quantization, 120k is your hard ceiling. push past that prefill throughput drops off a cliff, making the model practically unusable for large agentic workloads. # 2. The Q8 KV Cache Lifeline To survive 250k context on a budget card, you have to quantize the KV cache. I enabled 8 bit KV cache (`-ctk q8_0 -ctv q8_0`) and re ran: - 180k Context: Prefill 280 t/s | Decode 22.8 t/s - 250k Context: Prefill 115 t/s | Decode 20 t/s llama.cpp flags: .\llama-server -m gemma-4-26B-A4B-it-qat-UD-Q4_K_XL.gguf -c 180000 --port 8080 -b 1024 -ub 1024 -ctk q8_0 -ctv q8_0 Result: Q8 KV cache brings 250k context back from the dead. Decode speed stabilizes at a highly usable 20 t/s. You are trading a very small bit amount of reasoning precision for an extra 130,000 tokens of context window. if you own a single rtx 3050, 3060, 3070, 4050, 4060, 5050 or 5060, you must try this model and optimize your batch size for higher prefill. Hugging Face links to the updated Unsloth's QAT quants and performance graph are in the replies below. What model are you running on your 6GB, 8GB or 12GB cards right now? Let's see your setups.

Alok

36,617 просмотров • 1 месяц назад

Next week we're dropping Open Lovable v3 👀 With this new version you just drop in a URL and it extends your brand styles effortlessly in the web apps you build. This is all powered by Firecrawl's new branding format, which is now available through our /scrape endpoint 🔥
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Next week we're dropping Open Lovable v3 👀 With this new version you just drop in a URL and it extends your brand styles effortlessly in the web apps you build. This is all powered by Firecrawl's new branding format, which is now available through our /scrape endpoint 🔥

Developers Digest

18,378 просмотров • 9 месяцев назад

HAPPY NEW YEAR 2026 🥂 2025 was epic on Sui - and FlowX made it all possible as the ultimate aggregator layer None of this happened without YOU! Connect your wallet to lookback your 2025 journey and unlock a special New Year surprise from FlowX: #SUI2025Lookback
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HAPPY NEW YEAR 2026 🥂 2025 was epic on Sui - and FlowX made it all possible as the ultimate aggregator layer None of this happened without YOU! Connect your wallet to lookback your 2025 journey and unlock a special New Year surprise from FlowX: #SUI2025Lookback

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The Context Cloud for agents is here. Introducing the new supermemory. A new way to do context, for your agents Everything you need for context - Memory, RAG, Filesystems, Profiles, are now available as blocks you can compose to build something for your use case
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The Context Cloud for agents is here. Introducing the new supermemory. A new way to do context, for your agents Everything you need for context - Memory, RAG, Filesystems, Profiles, are now available as blocks you can compose to build something for your use case

Dhravya Shah

96,954 просмотров • 2 месяцев назад

✨ Happy New Year from Meshcapade! ✨ We're smashing into 2025 with a massive new release. 🎉 Introducing the .SMPL format! #SMPL is a generative model for capturing and generating human behaviors, expressions, soft-tissue movement, hands & finger articulation in 3D. It is the standard for representing humans for #AI and #MachineLearning while being fully compatible with standard graphics engines. Due to its popularity, we are now making it available as a downloadable file format with all the parametric information for training your AI models. Try it out now: ➡️ Download your results as a .SMPL file for use in your AI method. ➡️ Import your own .SMPL files to view them on our online platform. 🏂 Slide into 2025 with Meshcapade & #SMPL! ✨
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✨ Happy New Year from Meshcapade! ✨ We're smashing into 2025 with a massive new release. 🎉 Introducing the .SMPL format! #SMPL is a generative model for capturing and generating human behaviors, expressions, soft-tissue movement, hands & finger articulation in 3D. It is the standard for representing humans for #AI and #MachineLearning while being fully compatible with standard graphics engines. Due to its popularity, we are now making it available as a downloadable file format with all the parametric information for training your AI models. Try it out now: ➡️ Download your results as a .SMPL file for use in your AI method. ➡️ Import your own .SMPL files to view them on our online platform. 🏂 Slide into 2025 with Meshcapade & #SMPL! ✨

Meshcapade

16,363 просмотров • 1 год назад

NEW: Our raw X&Y tracking data is now available via API. FIFA-certified coordinates. Aligned to Hudl events. 120+ competitions. The spatial layer event data alone can't give you, built into the same ecosystem as your events and video. See our new data:
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NEW: Our raw X&Y tracking data is now available via API. FIFA-certified coordinates. Aligned to Hudl events. 120+ competitions. The spatial layer event data alone can't give you, built into the same ecosystem as your events and video. See our new data:

Hudl Statsbomb

14,689 просмотров • 3 месяцев назад

Teshin is here to guide you on your path in The Teacher. This new, early Quest is aimed at setting you on the path to master Mods, with a free Thornbak Rifle as your reward. Coming October 15 with The Vallis Undermind.
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Teshin is here to guide you on your path in The Teacher. This new, early Quest is aimed at setting you on the path to master Mods, with a free Thornbak Rifle as your reward. Coming October 15 with The Vallis Undermind.

WARFRAME

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So i just realised that Gaga is actually pronouncing Amen Fashion as ‘I AM IN FASHION’ and it now adds a whole new layer to the song???! So basically YOU are in fashion, and that’s your religion. Oh gaga the genius that you are, your brain needs to studied!!!!! 😭
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So i just realised that Gaga is actually pronouncing Amen Fashion as ‘I AM IN FASHION’ and it now adds a whole new layer to the song???! So basically YOU are in fashion, and that’s your religion. Oh gaga the genius that you are, your brain needs to studied!!!!! 😭

Punk Scavenger

79,187 просмотров • 1 год назад

We’re excited to introduce the new A/T Pose generator for Image to 3D. We know everyone has their own rigging preference — so now, the choice is yours! Whether your pipeline is built for the classic T-Pose or you prefer the relaxed A-Pose, you can now select the exact format that fits your production line. It's all about giving you the flexibility to generate game-ready characters exactly how you need them. The feature is now integrated into Meshy 6 Preview. Try it out and take full control of your generations! 🚀
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We’re excited to introduce the new A/T Pose generator for Image to 3D. We know everyone has their own rigging preference — so now, the choice is yours! Whether your pipeline is built for the classic T-Pose or you prefer the relaxed A-Pose, you can now select the exact format that fits your production line. It's all about giving you the flexibility to generate game-ready characters exactly how you need them. The feature is now integrated into Meshy 6 Preview. Try it out and take full control of your generations! 🚀

MeshyAI

56,913 просмотров • 8 месяцев назад

Introducing the AI Analyst 3.0✨ Since we launched it last year, the Analyst answered more than 300k of your questions and was used by more than 100k people. But many of you asked us for more capabilities: to help you visualize your analysis, enrich your datasets with new columns, format data and more. We packed all of that feedback into today's launch: a new Analyst rebuilt from the ground up to be a smarter, faster and a more versatile data analyst for your spreadsheets. Available today. See it in action 👇
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Introducing the AI Analyst 3.0✨ Since we launched it last year, the Analyst answered more than 300k of your questions and was used by more than 100k people. But many of you asked us for more capabilities: to help you visualize your analysis, enrich your datasets with new columns, format data and more. We packed all of that feedback into today's launch: a new Analyst rebuilt from the ground up to be a smarter, faster and a more versatile data analyst for your spreadsheets. Available today. See it in action 👇

Rows

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We’ve introduced a new security layer on MyOneApp to better protect your account. By linking your login to your Safaricom SIM, we ensure it’s always you accessing your services— keeping fraudsters out. When logging in for the first time: - Ensure your Safaricom line is set as your primary data line. - ⁠Use mobile data (not Wi-Fi). - ⁠Complete the quick authentication. After login, using my OneApp doesn’t consume your data. You may need to re-authenticate if you remove your SIM. Safety and Security are App’Graded on #MyOneApp.
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We’ve introduced a new security layer on MyOneApp to better protect your account. By linking your login to your Safaricom SIM, we ensure it’s always you accessing your services— keeping fraudsters out. When logging in for the first time: - Ensure your Safaricom line is set as your primary data line. - ⁠Use mobile data (not Wi-Fi). - ⁠Complete the quick authentication. After login, using my OneApp doesn’t consume your data. You may need to re-authenticate if you remove your SIM. Safety and Security are App’Graded on #MyOneApp.

Safaricom PLC

109,222 просмотров • 4 месяцев назад

Hello Big Stronk Nation!!! This is to announce the Dad Stronk Store is open. There is a live link in my Bio. Also, copy and paste link will be at the bottom of this announcement Right now there is one t-shirt design available. It is separated by size in the catalog. So be sure to click on your size. This was necessary because the size is marked on the front of the shirt. Shirts are available in various colors, so be sure to click through and check for whatever color you want. Sales from this store directly benefit the growth of the channel. Unlike other platforms, I directly control this format. You guys are the greatest and I thank you for all your support as this channel grows. It is a dream of mine to be able to do this full time and provide you all with the kind of content you enjoy!! Thank you again!! And Stay Stronk!! #merch #fitness #strength #thankyou
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Hello Big Stronk Nation!!! This is to announce the Dad Stronk Store is open. There is a live link in my Bio. Also, copy and paste link will be at the bottom of this announcement Right now there is one t-shirt design available. It is separated by size in the catalog. So be sure to click on your size. This was necessary because the size is marked on the front of the shirt. Shirts are available in various colors, so be sure to click through and check for whatever color you want. Sales from this store directly benefit the growth of the channel. Unlike other platforms, I directly control this format. You guys are the greatest and I thank you for all your support as this channel grows. It is a dream of mine to be able to do this full time and provide you all with the kind of content you enjoy!! Thank you again!! And Stay Stronk!! #merch #fitness #strength #thankyou

Dad Stronk

20,407 просмотров • 1 месяц назад

Introducing Bitlight Wallet⚡️ & $BIT airdrop campaign season I. #RGB is now live. 1. Download Bitlight Wallet 2. Go to Airdrop page and generate invite link Show your support to #RGB and native #Bitcoin smart contract layer by: 1. Follow Bitlight Labs⚡️ & Bitlight Wallet⚡️ 2. Quote the tweet & comment "And #RGB said, Let there #Bitlight; and there was bitlight."
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Introducing Bitlight Wallet⚡️ & $BIT airdrop campaign season I. #RGB is now live. 1. Download Bitlight Wallet 2. Go to Airdrop page and generate invite link Show your support to #RGB and native #Bitcoin smart contract layer by: 1. Follow Bitlight Labs⚡️ & Bitlight Wallet⚡️ 2. Quote the tweet & comment "And #RGB said, Let there #Bitlight; and there was bitlight."

Bitlight Labs⚡️

472,069 просмотров • 2 лет назад

Introducing the Presidential 🇺🇸 Tactic 🧩 For the first time, place bets on the X performance of your favorite political figures by choosing your side—Democrats 🔵 or Republicans 🔴—and the leading personalities within. This tactic is available in both free and paid modes. Compete for a share of over $44,000 in prize pools. Start playing now 🧵
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Introducing the Presidential 🇺🇸 Tactic 🧩 For the first time, place bets on the X performance of your favorite political figures by choosing your side—Democrats 🔵 or Republicans 🔴—and the leading personalities within. This tactic is available in both free and paid modes. Compete for a share of over $44,000 in prize pools. Start playing now 🧵

fantasy.top

48,197 просмотров • 1 год назад

Put on your hats, my wicked fiends! Villainous is now available for all the US fans in @streamingonmax! Very thankful to all of you BH Org members, for your patience and support, as well as the crew, the cast, and everyone who made it possible for the BHO to reach new frontiers!
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Put on your hats, my wicked fiends! Villainous is now available for all the US fans in @streamingonmax! Very thankful to all of you BH Org members, for your patience and support, as well as the crew, the cast, and everyone who made it possible for the BHO to reach new frontiers!

Alan Ituriel

610,204 просмотров • 3 лет назад

Your Enterprise Notebooks just got an upgrade. You can now use Google Cloud Workbench Instances directly from your favorite IDE with the new VS Code extension. With this new extension you can connect to and run notebooks on managed cloud environments directly within your local IDE. As part of our commitment to the developer ecosystem, the extension is fully open-sourced to support community-driven innovation. 🧵
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Your Enterprise Notebooks just got an upgrade. You can now use Google Cloud Workbench Instances directly from your favorite IDE with the new VS Code extension. With this new extension you can connect to and run notebooks on managed cloud environments directly within your local IDE. As part of our commitment to the developer ecosystem, the extension is fully open-sourced to support community-driven innovation. 🧵

Google for Developers

33,144 просмотров • 1 месяц назад

A single RTX 4090 (24 GB VRAM) can run the updated gemma 4 31B (dense) model with a 190,000 context window at 33 tokens/second. The VRAM barrier is dying. Google quietly updated Gemma 4, and Unsloth immediately compiled the new quants. I built llama.cpp from source on Ubuntu 22 to benchmark it. Google's stealth update 2 days ago enabled uniform Flash Attention 4 on Hopper to boost prefill and patched the chat template to improve tool calling. The agentic reasoning gains on the benchmark charts are massive: TB2 (Agents): +4.5% (to 25.8%) Tau2 (Telecom): +10.1% (to 62.7%) Running on Ubuntu 22, CUDA 13.0 with a single NVIDIA GeForce RTX 4090. Here is the exact step by step benchmarking process with a massive 28k tokens prompt and the commands I used to squeeze out maximum context without killing my throughput: # 1. The Baseline (Unquantized KV Cache) I started with full GPU offload (-ngl 99) and pushed the context to 40k. llama.cpp flags: ./build/bin/llama-server -m gemma-4-31B-it-qat-UD-Q4_K_XL.gguf -ngl 99 -c 40000 -fa on --port 8080 -v VRAM: 23.8 GB (maxed out on card) Throughput: Prefill: 2198.81 t/s | Decode: 35.77 t/s (with 28k tokens prompt) # 2. The CPU Split Trap I tried stretching to 80k context by offloading layers to the CPU (-ngl 52). llama.cpp flags: ./build/bin/llama-server -m gemma-4-31B-it-qat-UD-Q4_K_XL.gguf -c 80000 -ngl 52 -fa on --port 8080 -v Throughput: Prefill: 1212.73 t/s | Decode: 5 t/s (with 28k tokens prompt) # 3. The KV Quantization Breakthrough Instead of spilling layers to the CPU, I kept the model fully on card (-ngl 99) but enabled 8-bit KV cache quantization to free up VRAM. flags: ./build/bin/llama-server -m gemma-4-31B-it-qat-UD-Q4_K_XL.gguf -c 100000 --cache-type-k q8_0 --cache-type-v q8_0 -ngl 99 --port 8080 -v VRAM: 23.9 GB Throughput: Prefill: 2139.68 t/s | Decode: 32 t/s (with 28k tokens prompt) Result: 100k tokens of context on a single GPU with practically zero speed loss (and minimal intelligence loss). # 4. The Limit Test (Q4 KV Cache) To find the absolute breaking point, I dropped the KV cache to 4 bit (q4_0) and set -c 190000. flags: ./build/bin/llama-server -m gemma-4-31B-it-qat-UD-Q4_K_XL.gguf -c 190000 --cache-type-k q4_0 --cache-type-v q4_0 -ngl 99 --port 8080 -v VRAM: 23.8 GB Throughput: Prefill: 2206.66 t/s | Decode: 33 t/s (with 28k tokens prompt) (Note: Pushing it to 220k required dropping to -ngl 58 again, which immediately penalized decode down to 17 t/s). # The Tradeoff: For Max Reasoning: Keep your KV cache unquantized (f16). You get pristine reasoning but hit a strict 40k context ceiling. For Massive Document Retrieval: If you need to feed the model giant codebases, use --cache-type-k q4_0. Getting 190k context at 33 tokens/second on a consumer desktop with a 31b dense model is a cheat code. If you’re rocking a single 3090 or 4090 and slept on Gemma 4 earlier, this update is your cue to dust off the terminal. Hugging Face links to the Unsloth QAT quants are in the replies below.
0:20

A single RTX 4090 (24 GB VRAM) can run the updated gemma 4 31B (dense) model with a 190,000 context window at 33 tokens/second. The VRAM barrier is dying. Google quietly updated Gemma 4, and Unsloth immediately compiled the new quants. I built llama.cpp from source on Ubuntu 22 to benchmark it. Google's stealth update 2 days ago enabled uniform Flash Attention 4 on Hopper to boost prefill and patched the chat template to improve tool calling. The agentic reasoning gains on the benchmark charts are massive: TB2 (Agents): +4.5% (to 25.8%) Tau2 (Telecom): +10.1% (to 62.7%) Running on Ubuntu 22, CUDA 13.0 with a single NVIDIA GeForce RTX 4090. Here is the exact step by step benchmarking process with a massive 28k tokens prompt and the commands I used to squeeze out maximum context without killing my throughput: # 1. The Baseline (Unquantized KV Cache) I started with full GPU offload (-ngl 99) and pushed the context to 40k. llama.cpp flags: ./build/bin/llama-server -m gemma-4-31B-it-qat-UD-Q4_K_XL.gguf -ngl 99 -c 40000 -fa on --port 8080 -v VRAM: 23.8 GB (maxed out on card) Throughput: Prefill: 2198.81 t/s | Decode: 35.77 t/s (with 28k tokens prompt) # 2. The CPU Split Trap I tried stretching to 80k context by offloading layers to the CPU (-ngl 52). llama.cpp flags: ./build/bin/llama-server -m gemma-4-31B-it-qat-UD-Q4_K_XL.gguf -c 80000 -ngl 52 -fa on --port 8080 -v Throughput: Prefill: 1212.73 t/s | Decode: 5 t/s (with 28k tokens prompt) # 3. The KV Quantization Breakthrough Instead of spilling layers to the CPU, I kept the model fully on card (-ngl 99) but enabled 8-bit KV cache quantization to free up VRAM. flags: ./build/bin/llama-server -m gemma-4-31B-it-qat-UD-Q4_K_XL.gguf -c 100000 --cache-type-k q8_0 --cache-type-v q8_0 -ngl 99 --port 8080 -v VRAM: 23.9 GB Throughput: Prefill: 2139.68 t/s | Decode: 32 t/s (with 28k tokens prompt) Result: 100k tokens of context on a single GPU with practically zero speed loss (and minimal intelligence loss). # 4. The Limit Test (Q4 KV Cache) To find the absolute breaking point, I dropped the KV cache to 4 bit (q4_0) and set -c 190000. flags: ./build/bin/llama-server -m gemma-4-31B-it-qat-UD-Q4_K_XL.gguf -c 190000 --cache-type-k q4_0 --cache-type-v q4_0 -ngl 99 --port 8080 -v VRAM: 23.8 GB Throughput: Prefill: 2206.66 t/s | Decode: 33 t/s (with 28k tokens prompt) (Note: Pushing it to 220k required dropping to -ngl 58 again, which immediately penalized decode down to 17 t/s). # The Tradeoff: For Max Reasoning: Keep your KV cache unquantized (f16). You get pristine reasoning but hit a strict 40k context ceiling. For Massive Document Retrieval: If you need to feed the model giant codebases, use --cache-type-k q4_0. Getting 190k context at 33 tokens/second on a consumer desktop with a 31b dense model is a cheat code. If you’re rocking a single 3090 or 4090 and slept on Gemma 4 earlier, this update is your cue to dust off the terminal. Hugging Face links to the Unsloth QAT quants are in the replies below.

Alok

76,069 просмотров • 1 месяц назад

Rewired for Feet: Training Program This is a training clip… for My greedy feet. Not a normal stroke clip. Not a normal tease. This is psychological foot conditioning, where your body learns one very simple rule: My feet decide if you’re allowed to touch; because if My feet don’t make you hard, you don’t deserve to touch at all. You either go back in your cage or sit on your hands and feel exactly how pathetic it is to fail such a simple test. But don’t worry, you’ll get desperate enough, obedient Mhmmm, you’re only allowed to pump to My feet. I’ll tease you, deny you, laugh at you, and train your arousal until your cock starts understanding what it belongs to. Feet on screen, you stroke. Feet gone, you stop. This clip is also perfect to layer with My BYOB audio files for an even deeper conditioning session. Access to My BYOB audios is $50, with a few available in My audio store. Put My feet on the screen, layer My audio files underneath, and let the obsession sink in properly. Enjoy being conditioned to ONLY get hard for My pretty Princess feet…. Heh.
0:20

Rewired for Feet: Training Program This is a training clip… for My greedy feet. Not a normal stroke clip. Not a normal tease. This is psychological foot conditioning, where your body learns one very simple rule: My feet decide if you’re allowed to touch; because if My feet don’t make you hard, you don’t deserve to touch at all. You either go back in your cage or sit on your hands and feel exactly how pathetic it is to fail such a simple test. But don’t worry, you’ll get desperate enough, obedient Mhmmm, you’re only allowed to pump to My feet. I’ll tease you, deny you, laugh at you, and train your arousal until your cock starts understanding what it belongs to. Feet on screen, you stroke. Feet gone, you stop. This clip is also perfect to layer with My BYOB audio files for an even deeper conditioning session. Access to My BYOB audios is $50, with a few available in My audio store. Put My feet on the screen, layer My audio files underneath, and let the obsession sink in properly. Enjoy being conditioned to ONLY get hard for My pretty Princess feet…. Heh.

Brielle

35,885 просмотров • 1 месяц назад

$ALAB is one of the cleanest pure plays on rack-scale AI fabric sitting in traffic-control layer that keeps data moving between chips, memory, switches and accelerators. The near-term driver is Scorpio with the X-Series 320-lane fabric switch ramping now and management guiding it to become Astera’s largest product line by year-end as two more hyperscalers taking Scorpio P later this year. The second leg is Leo which is deploying with $MSFT Azure M-series VMs as first cloud CXL-attached memory service with a new KV-cache design win shipping in 2027 as hyperscalers use CXL to fight rising DRAM costs.
1:10

$ALAB is one of the cleanest pure plays on rack-scale AI fabric sitting in traffic-control layer that keeps data moving between chips, memory, switches and accelerators. The near-term driver is Scorpio with the X-Series 320-lane fabric switch ramping now and management guiding it to become Astera’s largest product line by year-end as two more hyperscalers taking Scorpio P later this year. The second leg is Leo which is deploying with $MSFT Azure M-series VMs as first cloud CXL-attached memory service with a new KV-cache design win shipping in 2027 as hyperscalers use CXL to fight rising DRAM costs.

Shay Boloor

73,291 просмотров • 1 месяц назад