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Acrolinx fixes.

Author: AnnaMHuff

articles/virtual-machines/sizes/gpu-accelerated/includes/nd-gb300-v6-series-summary.md

@@ -11,7 +11,7 @@ ms.reviewer: mattmcinnes
 # Customer intent: As a cloud architect, I want to assess the specifications and features of the ND GB300-v6 series, so that I can choose the optimal virtual machine size for our high-performance computing needs.
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-The NDGB300v6 series is Azure’s next-generation GPU VM line purpose-built for largescale AI, especially highthroughput inference for reasoning and agentic systems. These VMs are powered by NVIDIA GB300 NVL72 rackscale systems (Blackwell Ultra GPUs + Grace CPUs).  Each ND-GB300-v6 VM has two NVIDIA Grace CPUs and four NVIDIA Blackwell B300 GPUs. The GPUs are interconnected via fifth-generation NVLINK, providing a total of 4× 1.8 TB/s NVLINK bandwidth per VM. Each GPU also has 800 Gb/s InfiniBand connectivity, enabling cluster-scale performance with low latency. There are 18 VMs per rack, so effectively 72 NVIDIA Blackwell Ultra GPUs with 36 NVIDIA Grace CPUs, exposing ~37 TB of fast memory (~20TB High Bandwidth Memory, ~17TB of CPU Memory), 130 TB/s of intrarack NVLink bandwidth.  
+The NDGB300v6 series is Azure’s next-generation GPU VM line purpose-built for large-scale AI, especially high-throughput inference for reasoning and agentic systems. These VMs are powered by NVIDIA GB300 NVL72 rackscale systems (Blackwell Ultra GPUs + Grace CPUs).  Each ND-GB300-v6 VM has two NVIDIA Grace CPUs and four NVIDIA Blackwell B300 GPUs. The GPUs are interconnected via fifth-generation NVLINK, providing a total of 4× 1.8 TB/s NVLINK bandwidth per VM. Each GPU also has 800 Gb/s InfiniBand connectivity, enabling cluster-scale performance with low latency. There are 18 VMs per rack, so effectively 72 NVIDIA Blackwell Ultra GPUs with 36 NVIDIA Grace CPUs, exposing ~37 TB of fast memory (~20TB High Bandwidth Memory, ~17TB of CPU Memory), 130 TB/s of intrarack NVLink bandwidth.  
 
 At rack scale, ND GB300 v6 delivers up to 1.44 exaFLOPS FP4 Tensor Core performance per NVL72 domain and has demonstrated ~1.1 million tokens/second LLM inference throughput per rack, which is ~27% higher than ND GB200 v6. See here for more details. Compared to GB200, GB300 provides ~1.5× FP4 compute, 50% higher HBM capacity (288 GB HBM3E per GPU vs. 192 GB), and 2x the back-end network bandwidth per GPU (800Gbps vs 400 Gbps).