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@one.gaming776: নিজের জন্য না পরিবারের জন্য 🥹🥹#vairal #foryoupage #onegaming
ONE GAMING
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Sunday 06 September 2026 05:12:14 GMT
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🎀lamiya Islam˚˖𓍢🌷 :
পুরুষ নামটা সহজ হলেও কাজ গুলোও অনেক অনেক কঠিন হয়😅😞🥺🥺🥺🥺🥺🥺🥺🥺🥺🥺🥺🥺🥺🥺🥺🥺🥺
2026-09-06 13:11:32
8
J..I..H..A..D..¡! :
🥹🥹🥹
2026-09-06 15:07:37
0
Md Siyam Khan :
এখনতো কিছু টা বুঝি বড় হলে আরও বুঝবো ছেলে দের জীবন টা কত কষ্ট 🥺🥺
2026-09-06 10:56:40
2
𝑩𝒂𝒅 𝑽𝒊𝒃𝒆𝒔 ⚡ :
সোহাগ ভাই বিগ ফ্যান ভাই রিপ্লাই পাবো😔
2026-09-06 05:16:43
4
☠️🚩Md.Ahad🚩☠️ :
হারে বাবা😭😭😭😭😭😭
2026-09-06 10:59:49
2
ᴹᴿメȺ ʀ i ꜰ ᴜ ʟ :
2026-09-06 15:52:53
0
তপন রায় :
এটাই গরিবের জীবন 🥺🥺🥺
2026-09-06 05:17:11
2
√~°HABIB°~√ :
2026-09-06 15:44:47
0
🍁EMAM🍁 :
🥺🥺
2026-09-06 15:45:05
0
Rayhan jr :
🥺🥺😔😔🥺🥺ভাই আমি আপনার সাথে এড হবো প্লিজ প্লিজ প্লিজ প্লিজ প্লিজ প্লিজ প্লিজ প্লিজ প্লিজ প্লিজ প্লিজ প্লিজ প্লিজ 🥺🥺🥺😔🥺🥹😒🙂↕️10572461374
2026-09-06 15:44:35
0
ᴳᵒᵈ々SOHAG :
😫😫😫😫
2026-09-06 15:34:10
0
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China Is Coming for Your Local AI Box Xiaomi and Alibaba just went after the last layer of local AI they don't own: the box on your desk. This is what a local AI box actually does, why the spec on the sticker is the wrong one, and whether you should buy one. Dedicated AI boxes (NVIDIA DGX Spark, AMD Strix Halo mini PCs, the big-memory Mac Studio) sell you 128GB and up of model memory for the price of a bare DDR5 kit. The number that decides how fast a local LLM talks is memory bandwidth, not the petaflop headline. Two Chinese announcements landed six days apart, and the buying advice changes depending on which model you plan to run. Get the hotter takes in your inbox every Tuesday: https://devsplainers.c... CHAPTERS 00:00 Xiaomi, Alibaba, and the last layer China doesn't own 00:47 Where the local AI box came from: VRAM, quantization, MoE 01:41 The DGX Spark lesson: one petaflop, three tokens per second 02:35 The RAM shortage, and why a whole box beats a memory kit 03:53 China wants the box: Xiaomi's O100 and Alibaba's RISC-V slide 05:37 How NVIDIA, AMD and Apple fight back (CUDA, price, bandwidth) 06:40 The BYD question: does the EV playbook map onto silicon? 07:48 Should you buy a local AI box? 08:42 The signal to watch next WHAT IS A LOCAL AI BOX? A local AI box is a small computer built around one big pool of unified memory shared between CPU and GPU, sold for running large language models on your own hardware instead of in the cloud. Capacity decides which model fits. Memory bandwidth decides how fast it writes each word. Compute mostly sets prefill, the speed it reads your prompt. That is why a 273 GB/s box with 128GB can hold a model an RTX 5090 cannot, and still lose badly on tokens per second. COVERED IN THIS VIDEO Why 24GB of VRAM stopped being the ceiling for local LLMs Quantization and mixture-of-experts models, explained without jargon DGX Spark benchmarks: 1 petaflop on the box, under 3 tok/s on dense 70B Memory bandwidth versus compute, and which one you actually feel DDR5 and HBM: how the DRAM shortage made a $2,000 mini PC the cheap seat Apple dropping its 512GB and 256GB Mac Studio configs Xiaomi's AI Cube, the O100, and 1.22 TB/s of near-memory bandwidth Alibaba's XuanTie C950 running a 27B model at 30 tok/s with no GPU Why a llama.cpp or vLLM backend on launch day is the credibility test The buying verdict: when a box wins, and when a 5090 wins SOURCES LMSYS, DGX Spark In-Depth Review (Llama 3.1 70B FP8 decode) NVIDIA DGX Spark product page and February 2026 pricing announcement AMD Ryzen AI Max product pages Reuters, Xiaomi chip event coverage, August 2026 Xiaomi Xring presentation coverage (gizmochina, VideoCardz, Notebookcheck) Alibaba XuanTie C950 announcement and slide, August 2026 Counterpoint Research, DRAM market share Q2 2026 TrendForce, foundry revenue share Q1 2026 and DRAM price forecast Tom's Hardware, DDR5 kit pricing, August 2026 MacRumors, AppleInsider and 9to5Mac on Mac Studio memory config removals Alex Ziskind, "Your local LLM is 10x slower than it should be" Level1Techs forum and MindStudio gpt-oss-120B runs on Strix Halo r/LocalLLaMA community threads on Xiaomi, Alibaba and Strix Halo #LocalLLM #LocalAI #DGXSpark #AIHardware #Xiaomi
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