#gpumemory — Public Fediverse posts
Live and recent posts from across the Fediverse tagged #gpumemory, aggregated by home.social.
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Dentro la gerarchia della memoria delle GPU: come i server AI spostano i dati dagli SSD alla HBM
Ti sei mai chiesto come i modelli di intelligenza artificiale trasferiscono i dati alla GPU? Questo articolo spiega in modo semplice i diversi livelli di memoria all'interno di un server AI, perché la memoria della GPU è diventata un collo di bottiglia e quali nuove tecnologie potrebbero migliorare le prestazioni dell'intelligenza artificiale.
buysellram.com/blog/inside-the…
#HBM #HBM4 #GPUMemory #AIInfrastructure #DataCenter #CXL #HighBandwidthFlash #AIHardware #MemoryHierarchy #AIInference #NVMe #ITAD
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Dentro la gerarchia della memoria delle GPU: come i server AI spostano i dati dagli SSD alla HBM
Ti sei mai chiesto come i modelli di intelligenza artificiale trasferiscono i dati alla GPU? Questo articolo spiega in modo semplice i diversi livelli di memoria all'interno di un server AI, perché la memoria della GPU è diventata un collo di bottiglia e quali nuove tecnologie potrebbero migliorare le prestazioni dell'intelligenza artificiale.
buysellram.com/blog/inside-the…
#HBM #HBM4 #GPUMemory #AIInfrastructure #DataCenter #CXL #HighBandwidthFlash #AIHardware #MemoryHierarchy #AIInference #NVMe #ITAD
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A modern AI server runs five layers of memory, from nanosecond on-chip SRAM to petabyte-scale SSDs, and keeping the GPU fed is the whole engineering game. This piece walks the full hierarchy: why HBM became the bottleneck, why adding more isn't simple, and where HBF, CXL, and PIM fit into the next generation.
#HBM #HBM4 #GPUMemory #AIInfrastructure #DataCenter #CXL #HighBandwidthFlash #AIHardware #MemoryHierarchy #AIInference #NVMe #ITAD #buysellram
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Why can't a GPU just carry more HBM? Interposers max out in size, stacking 12 to 16 DRAM dies compounds yield losses, and every stack sits beside a kilowatt-class package that hates sharing heat. So capacity climbs in careful steps — 80 GB on the H100, 141 on the H200, 192 on the B200, 288 on Blackwell Ultra — while KV caches for long-context inference balloon past 40 GB per request.
That gap between what models demand and what packaging permits is reshaping server design. NVIDIA's Rubin platform treats CPU memory and HBM as one coherent pool. SanDisk and SK hynix are standardizing High Bandwidth Flash as a capacity tier under HBM, with first samples due this half. CXL 4.0 pooling hardware is landing in racks now.
This article walks the whole memory hierarchy, from on-chip SRAM to NVMe, and explains what each emerging technology actually solves — and what it doesn't.
#HBM #HBM4 #GPUMemory #AIInfrastructure #DataCenter #CXL #HighBandwidthFlash #AIHardware #MemoryHierarchy #AIInference #NVMe #ITAD #technology
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Why can't a GPU just carry more HBM? Interposers max out in size, stacking 12 to 16 DRAM dies compounds yield losses, and every stack sits beside a kilowatt-class package that hates sharing heat. So capacity climbs in careful steps — 80 GB on the H100, 141 on the H200, 192 on the B200, 288 on Blackwell Ultra — while KV caches for long-context inference balloon past 40 GB per request.
That gap between what models demand and what packaging permits is reshaping server design. NVIDIA's Rubin platform treats CPU memory and HBM as one coherent pool. SanDisk and SK hynix are standardizing High Bandwidth Flash as a capacity tier under HBM, with first samples due this half. CXL 4.0 pooling hardware is landing in racks now.
This article walks the whole memory hierarchy, from on-chip SRAM to NVMe, and explains what each emerging technology actually solves — and what it doesn't.
#HBM #HBM4 #GPUMemory #AIInfrastructure #DataCenter #CXL #HighBandwidthFlash #AIHardware #MemoryHierarchy #AIInference #NVMe #ITAD #technology
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A modern AI server runs five layers of memory, from nanosecond on-chip SRAM to petabyte-scale SSDs, and keeping the GPU fed is the whole engineering game. This piece walks the full hierarchy: why HBM became the bottleneck, why adding more isn't simple, and where HBF, CXL, and PIM fit into the next generation.
#HBM #HBM4 #GPUMemory #AIInfrastructure #DataCenter #CXL #HighBandwidthFlash #AIHardware #MemoryHierarchy #AIInference #NVMe #tech
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A modern AI server runs five layers of memory, from nanosecond on-chip SRAM to petabyte-scale SSDs, and keeping the GPU fed is the whole engineering game. This piece walks the full hierarchy: why HBM became the bottleneck, why adding more isn't simple, and where HBF, CXL, and PIM fit into the next generation.
#HBM #HBM4 #GPUMemory #AIInfrastructure #DataCenter #CXL #HighBandwidthFlash #AIHardware #MemoryHierarchy #AIInference #NVMe #tech
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GPU RAM RECLAIM WOES PLAGUE PYTORCH USERS
PyTorch users face GPU memory release issues. This affects deep learning tasks, causing errors and requiring workarounds. Learn why it matters.
#PyTorchBug, #GPUMemory, #DeepLearning, #MachineLearning, #TechIssue
https://newsletter.tf/pytorch-gpu-memory-release-errors-deep-learning/
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Many PyTorch users are finding that their GPU memory is not being freed up after use. This is a bigger problem than last month.
#PyTorchBug, #GPUMemory, #DeepLearning, #MachineLearning, #TechIssue
https://newsletter.tf/pytorch-gpu-memory-release-errors-deep-learning/
