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Your GPU spends more time moving data than actually computing. Every separate kernel writes to HBM, the next one reads it back. Six round-trips where two would do. Vertical fusion chains multiple ops
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6.7 billion parameters. 40 transformer layers. Zero labels. DINOv3 starts with 17 billion raw internet images and filters them down to 142 million through hierarchical k-means clustering. Each image
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One attention head is good. Eight are better. Multi-head attention runs 8 parallel heads β€” each learning different patterns: syntax, semantics, position. Concatenate, project, done. Richer representa
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100,000+ citations. Eight authors. One idea that satisfies every sequence model since. RNNs processed tokens one at a time. By token 50, gradients had vanished. Long-range dependencies? Forget it. T
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A GPU isn’t one big processor. It’s 132 mini-processors sharing a 4-level memory hierarchy. Streaming multiprocessors, CUDA cores, tensor cores, warps of 32 threads executing in lockstep. Thread bloc
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Training a 400B model isn’t about picking one parallelism strategy. It’s about combining all five: DP, TP, SP/CP, PP, and EP. That’s 32+ possible configurations, and the right one depends entirely on
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MoE models don’t use one giant FFN. They use a router that picks 2 out of hundreds of experts per token. DeepSeek V3 has 256 experts but only 37B of its 671B parameters activate per token. 256 expert
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Pipeline parallelism splits layers across GPUs. GPU 0 gets layers 1-8, GPU 1 gets 9-16, and so on. Only activations pass between them. The problem: GPUs sit idle waiting for the one ahead to finish.
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TP + SP handles everything except one operation: attention still needs the full sequence on every GPU. At 128K tokens, even activation recomputation can’t save you. Context parallelism splits the se
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Tensor parallelism shards every linear layer. But LayerNorm and Dropout still run full. They need the entire hidden dimension. LayerNorm computes mean and variance across all of h. You can’t split h.
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A 70B model needs 840 GB to train. 140 GB parameters + 140 GB gradients + 560 GB optimizer states. TP = 8 divides everything: 840 / 8 = 105 GB per GPU. A100s have 80 GB. 105 > 80, doesn’t fit. Add Ze
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Every Transformer layer uses the same Megatron pattern twice. MLP: column-parallel Linear₁ expands h to 4h. GELU is element-wise, no sync needed. Row-parallel Linearβ‚‚ shrinks 4h to h. One all-reduce.
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About @atikan003

ATIKAN is a Instagram growing creator. With 25 followers and 12 posts, atikan003's account shows a follower-to-following ratio of 12.5:1. View all content anonymously on Pictame - no login required.

atikan003's engagement rate sits at 29.67%. That's 493% above what you'd typically see for accounts with 1K-10K followers (average 5%) β€” a sign of a genuinely connected audience.

Out of the last 12 posts, 100% are videos and 0% are photos. New posts appear roughly every 2 days β€” a steady, reliable rhythm. Captions lean detailed, averaging 887 characters.

29.67% engagement rate β€” 493% above average for 1K-10K follower accounts.
New content every 2 days β€” a steady rhythm.
12.5:1 follower-to-following ratio.

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atikan003
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@atikan003

ATIKAN

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Top Hashtags
#DeepLearning#DistributedTraining#LLM#TensorParallelism#GPU#Transformers#AttentionIsAllYouNeed#AI

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