Mastodawn

🚀 #H100 va a por todas: busca comprar dos firmas de tesorería para superar los 3.500 $BTC en Europa. La carrera por acumular #Bitcoin en los balances corporativos no se detiene. ¿Estamos ante el nuevo estándar de reserva de valor?

sayzard 6d ago

0xSero (@0xSero)

Lambda의 5,000달러 컴퓨트 크레딧, Nvidia의 H100 8장 클라우드 제공, TNG Technology의 B200 2주 사용 제공 등으로 72시간 만에 10만 달러 이상 가치의 자원을 확보했다고 언급한다. AI 개발자를 위한 대규모 컴퓨트 지원 사례로, 최신 GPU 자원 확보가 큰 가치가 있음을 보여준다.

https://x.com/0xSero/status/2035680567274975634

#lambda #nvidia #h100 #b200 #compute

0xSero (@0xSero) on X

In 72 hours I got over 100k of value 1. Lambda gave me 5000$ credits in compute 2. Nvidia offered me 8x H100s on the cloud (20$/h) idk for how long but assuming 2 weeks that'd be 5000$~ 3. TNG technology offered me 2 weeks of B200s which is something like 12000$ in compute

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TechLİfe Mar 10

Snowflake's Arctic Long Sequence Training: How to Train LLMs on 15 Million Tokens Without Selling a Kidney

https://techlife.blog/posts/snowflakes-arctic-long-sequence-training-how-to-train-llms-on-15-million-tokens-without-selling-a-kidney/

#ALST #Snowflake #LongContextTraining #DeepSpeed #HuggingFace #SequenceParallelism #LLMTraining #H100 #Llama8B #Qwen3 #GPUMemoryOptimization

Snowflake's Arctic Long Sequence Training: How to Train LLMs on 15 Million Tokens Without Selling a Kidney

Snowflake AI Research just open-sourced Arctic Long Sequence Training (ALST), a framework that pushes LLM training from a measly 32K tokens to over 15 million — a 469x improvement — using standard Hugging Face models and H100 GPUs. Here's what it means for you.

TechLife

sayzard Mar 6

Andrej Karpathy (@karpathy)

nanochat이 단일 8x H100 노드에서 GPT-2 역량 모델을 약 2시간 만에 학습시켰다고 발표했습니다(한 달 전 약 3시간에서 단축). fp8 지원과 여러 튜닝, 그리고 데이터셋을 FineWeb-edu에서 변경한 것이 주요 개선 포인트로, 실시간 인터랙티브 학습에 한층 근접했다는 기술적 진전입니다.

https://x.com/karpathy/status/2029701092347630069

#nanochat #gpt2 #training #h100 #fp8

Andrej Karpathy (@karpathy) on X

nanochat now trains GPT-2 capability model in just 2 hours on a single 8XH100 node (down from ~3 hours 1 month ago). Getting a lot closer to ~interactive! A bunch of tuning and features (fp8) went in but the biggest difference was a switch of the dataset from FineWeb-edu to

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sayzard Mar 6

Mark Gadala-Maria (@markgadala)

신규 비디오 생성 모델 'HELIOS' 공개: 14B 규모의 autoregressive diffusion 모델로, 단일 텍스트 프롬프트로 최대 60초의 일관된 비디오를 생성한다고 발표됨. 성능은 NVIDIA H100 한 장에서 초당 19.5프레임으로 실시간급 처리에 가까워 같은 규모 모델로는 최초 사례로 보임.

https://x.com/markgadala/status/2029572916141007273

#videogeneration #diffusion #helios #nvidia #h100

Mark Gadala-Maria (@markgadala) on X

🚨 BREAKING: NEW VIDEO MODEL "HELIOS" GENERATES 1 FULL MINUTE OF VIDEO FROM A SINGLE PROMPT >MODEL: 14B autoregressive diffusion model — first of its size to hit real-time >OUTPUT: Up to 60 seconds of coherent video from a single text prompt >SPEED: 19.5 FPS on one NVIDIA H100

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BuySellRam.com Mar 4

As the AI arms race accelerates, the 18-month hardware refresh cycle has transformed GPUs from simple components into high-value infrastructure assets. This article explores why selling hundreds of units—like NVIDIA’s H100 or A100—requires a shift from "peer-to-peer" thinking to "Enterprise ITAD" strategy.

https://medium.com/@samlamucf/where-to-sell-gpus-in-bulk-a-practical-guide-for-ai-and-data-center-hardware-7d9c2216f020

#DataCenter #ITAD #GPU #EnterpriseTech #NVIDIA #TechStrategy #BuySellRam #CircularEconomy #AI #H100 #Blackwell #GPU #TechNews #EnterpriseAI #AssetRecovery

Where to Sell GPUs in Bulk: A Practical Guide for AI and Data Center Hardware

The secondary GPU market has shifted from a hobbyist landscape to a high-stakes infrastructure commodity market. While individual sellers…

Medium

ServerMO Feb 27

Hello Fediverse! We are ServerMO, providing high-performance infrastructure for 21 years. 🚀

What we offer:
🔹 Elite GPUs: NVIDIA H100 & A100.
🔹 Bare Metal: AMD EPYC & Intel Xeon.
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Instant setup in 24-48hrs. Stop renting, start owning!

🔗 https://www.servermo.com/

#ServerMo #DedicatedServers #BareMetal #GPU #H100 #A100 #SysAdmin #DevOps #AI

ALEXBSR Feb 21

NVIDIA GPU Cluster Liquidation: Maximize ROI and Asset Recovery
The shift to Blackwell is accelerating the depreciation of NVIDIA A100, H100, and H200 clusters. What were recently frontier training assets are now facing mid-life value cliffs due to performance-per-watt gaps, power density limits, and liquid-cooling requirements.

This turns GPU cluster liquidation into a capital strategy, not just decommissioning. Timing the secondary market, preserving service records to capture refurbished premiums, and enforcing IEEE 2883 data sanitization are key to maximizing ROI and funding next-generation deployments.

In compressed AI refresh cycles, asset recovery speed directly impacts infrastructure competitiveness.

https://www.buysellram.com/blog/nvidia-a100-h100-h200-cluster-liquidation-maximize-roi-and-asset-recovery/

#GPU #AIInfrastructure #DataCenter #AssetRecovery #H100 #A100 #H200 #Blackwell #ITAD #AIHardware #GraphicsCard #VideoCard #HPC #tech

NVIDIA GPU Cluster Liquidation: Maximize ROI and Asset Recovery

Liquidating NVIDIA A100 H100 H200Learn how to liquidate NVIDIA A100, H100, and H200 GPU clusters to maximize resale value, ensure secure data sanitization, and fund next-generation upgrades efficiently. clusters: maximize resale value, manage depreciation, ensure data sanitization, and fund Blackwell GPU upgrades efficiently.

BuySellRam

sayzard Feb 19

Python Trending (@pythontrending)

InferenceX라는 오픈소스 연속 추론(continuous inference) 벤치마킹 프로젝트에서 Qwen3.5, DeepSeek, GPTOSS 등 모델을 대상으로 GB200 NVL72, MI355X, B200, GB300 NVL72, H100 등 다양한 추론 하드웨어를 비교하는 벤치마크를 소개하며, 곧 TPUv6e/v7 및 Trainium2/3 지원 예정임을 알립니다.

https://x.com/pythontrending/status/2024088496328081630

#inferencex #benchmarking #opensource #qwen3.5 #h100

Python Trending 🇺🇦 (@pythontrending) on X

InferenceX - Open Source Continuous Inference Benchmarking Qwen3.5, DeepSeek, GPTOSS - GB200 NVL72 vs MI355X vs B200 vs GB300 NVL72 vs H100 & soon™ TPUv6e/v7/Trainium2/3 https://t.co/eDIFZ2JVop

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sayzard Feb 13

Kosseila (CloudDude) (@CloudDude_)

DeepSeek 관련 업데이트로 보이는 게시물: DeepSeek v3.2는 643GB 규모의 가중치로, 16개 GPU(2× H100 노드)로 운영되며 KubeRay로 자동화되어 배포되었다고 공유합니다(텐서 병렬 TP=8, 파이프라인 병렬 PP=2). 대규모 모델 운영 스펙을 공개하는 내용입니다.

https://x.com/CloudDude_/status/2021989676521865320

#deepseek #model #kubray #h100

Kosseila (CloudDude) ☁️📡🍉 (@CloudDude_) on X

#SideQuest of the week unlocked🔥 @deepseek_ai Deepsekv3.2🐳: 𝟲𝟰𝟯𝗚𝗕 of weights, 𝟭𝟲𝘅 𝗚𝗣𝗨𝘀, across 𝟮𝘅𝗛𝟭𝟬𝟬 𝗻𝗼𝗱𝗲𝘀, riding #KubeRay all automated (TP=8,PP=2) #VLLMProductionStack🤌🏻#BadBoy

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