docs(gpu): fix wording (#5933)

* docs(gpu): fix wording

* docs(gpu): fix typo

* docs(gpu): add nvlink

Author: bene2k1

pages/gpu/reference-content/blackwell-vs-hopper-choosing-the-right-architecture.mdx

@@ -29,15 +29,17 @@ The B300 delivers exceptional memory and bandwidth:
 This massive capacity enables entire 1-trillion-parameter models, huge batch sizes, and ultra-long context windows (*up to 1 million+ tokens*) to reside on just a few GPUs,reducing the need for complex multi-node communication and drastically lowering inter-node overhead.
 Equipped with **fifth-generation Tensor Cores**, the B300 introduces native hardware support for FP4 and FP6 precision, a major advancement over Hopper. On H100, FP4 operations are emulated using INT8 arithmetic, which limits efficiency and real-world performance. In contrast, Blackwell’s Tensor Cores process FP4 natively, unlocking significantly higher throughput and energy efficiency for ultra-low-precision AI workloads.
 
-Combined with an enhanced **second-generation Transformer Engine**, these improvements enable the B300 to deliver up to **15 PFLOPS of dense FP4 performance**, achieving multiple times higher inference throughput than the H100-SXM on modern reasoning workloads such as DeepSeek-R1 and Llama 3.1 405B+.
+Scaleway's B300-SXM Instances are equipped with **fifth-generation NVLink** that vastly boosts scalability in large multi-GPU systems, allowing GPUs to seamlessly share memory and coordinate computations across training, inference, and reasoning workloads. Each NVIDIA Blackwell GPU includes up to [18 NVLink 100 GB/s](https://www.nvidia.com/en-us/data-center/nvlink/) connections, providing a total of 1.8 TB/s of bandwidth—twice that of the previous generation and more than 14× the bandwidth of PCIe Gen5.
+
+Combined with an enhanced **second-generation Transformer Engine**, these improvements enable the B300 to deliver up to **18 PFLOPS of dense FP4 performance**, achieving multiple times higher inference throughput than the H100-SXM on modern reasoning workloads such as DeepSeek-R1 and Llama 3.1 405B+.
 Compared to B200, B300 delivers higher FP4 perfomance at the expense of the FP64 performance. As a result, the B300 delivers lower FP64 performance compared to the H100-SXM, making it less well-suited for traditional scientific computing and HPC simulations that rely on high-precision arithmetic.
 With its combination of vast memory and ultra-efficient low-precision compute, the B300 shines in “*big-AI*” scenarios, including:
 * Training and fine-tuning trillion-parameter dense or Mixture-of-Experts (MoE) models
 * Real-time, high-throughput inference at scale
 * Retrieval-Augmented Generation (RAG) with massive context
 * AI reasoning pipelines requiring extended token sequences
 
-The architecture's strong focus on frontier AI means the B300 is *not optimized for HPC workloads* such as computational physics, quantum chemistry, or climate modeling,domains where FP64 accuracy is critical. In these cases, performance may be inferior to H100, despite Blackwell’s generational leap in AI capabilities.
+The architecture's strong focus on frontier AI means the B300 is *not optimized for HPC workloads* such as computational physics, quantum chemistry, or climate modeling; domains where FP64 accuracy is critical.
 Moreover, the B300’s extreme capabilities make it over-provisioned for smaller or mid-sized models (≤70B parameters), prototyping, or general-purpose AI tasks. For these use cases, Scaleway’s H100-SXM GPU Instances remain a more economical and practical choice.
 
 ## NVIDIA H100-SXM: The reliable standard for AI and HPC