docs(gpu): add doc blackwell vs hopper (#5930)

* docs(gpu): add doc blackwell vs hopper

* fix(gpu): fix link

* docs(gpu): fix link

Author: bene2k1

pages/gpu/menu.ts

@@ -62,6 +62,10 @@ export const gpuMenu = {
           label: 'Choosing the right GPU Instance type',
           slug: 'choosing-gpu-instance-type',
         },
+        {
+          label: 'Blackwell vs Hopper - Choosing the right NVIDIA GPU architecture',
+          slug: 'blackwell-vs-hopper-choosing-the-right-architecture',
+        },
         {
           label:
             'GPU Instances internet and Block Storage bandwidth overview',

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

@@ -0,0 +1,62 @@
+---
+title: Blackwell vs Hopper - Choosing the right NVIDIA GPU architecture
+description: This page provides information about the NVIDIA Blackwell and Hopper GPU architectures.
+tags: NVIDIA GPU cloud instance
+dates:
+  validation: 2025-12-05
+  posted: 2025-12-05
+---
+
+A GPU architecture defines the underlying design of NVIDIA’s Graphics Processing Units (GPUs), optimized for accelerating AI training, inference, and high-performance computing (HPC) workloads.
+
+* **[Blackwell](https://www.nvidia.com/en/us/data-center/technologies/blackwell-architecture/)**, announced in 2024 and shipping in late 2025, represents the newest evolution: featuring a dual-die design in GPUs such as the Scaleway [B300-SXM GPU Instances](https://www.scaleway.com/en/b300-sxm/). Engineered for trillion-parameter AI at unprecedented scale, Blackwell pushes the boundaries of performance and efficiency.
+* **[Hopper](https://www.nvidia.com/en/us/data-center/technologies/blackwell-architecture/)**, introduced in 2022, powers flagship data center GPUs like the H100. Available in multiple configurations like Scaleway [H100-SXM GPU Instances](https://www.scaleway.com/en/h100/), it excels at mixed-precision computing for large language models (LLMs) and general-purpose AI. 
+
+Choosing between Blackwell and Hopper ultimately depends on your workload’s requirements for performance, memory capacity, precision needs, and cost-efficiency.
+
+## B300-SXM Instances: The specialized Instance for frontier AI
+
+Frontier AI refers to the most advanced AI models out there, the ones that can match or even beat human performance on a whole bunch of different tasks and that require massive
+computing performance. Scaleway’s [B300-SXM GPU Instances](https://www.scaleway.com/en/b300-sxm/), powered by the **Blackwell Ultra architecture**, are engineered for the new era of AI reasoning and trillion-parameter models.
+
+Launched by Scaleway, during the [AI pulse event in December 2025](https://www.scaleway.com/en/news/scaleway-announces-at-ai-pulse-major-advancements-in-ai-model-accessibility-new-compute-capabilities-and-expansion-of-its-presence-across-europe/),
+the B300 SXM GPU marks the current masterpiece of data center AI performance, making it the preferred platform for hyperscale AI factories running massive language models, long-context reasoning, and high-throughput inference.
+
+The B300 delivers exceptional memory and bandwidth:
+* 288 GB of faster HBM3e memory: over 3.5× more than the H100-SXM (80 GB HBM3)
+* Up to 7.7 TB/s of memory bandwidth (double than what HBM3 provides)
+
+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+.
+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.
+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
+
+Scaleway’s [H100-SXM GPU Instances](https://www.scaleway.com/en/h100/), built on the 2022 Hopper architecture, are based on the most widely adopted and battle-tested data center GPU. With several years of production deployment, the H100 remains the industry standard, offering a robust balance of AI acceleration, high-precision computing, and broad software compatibility across cloud providers and supercomputing environments.
+
+Its maturity ensures unmatched stability and predictability. Drivers, frameworks (PyTorch, JAX, TensorFlow), and ecosystem tooling are fully optimized, making the H100-SXM the default choice for:
+- Open-source model development
+- Enterprise AI pipelines
+- Scientific research and academic workloads
+
+Powered by **fourth-generation Tensor Cores** and the **first-generation Transformer Engine**, the H100 supports automatic mixed-precision (FP8, FP16, BF16, TF32), delivering up to 1,979 TFLOPS (FP16 TC).
+
+While the H100 can perform FP4-like operations, it does so via software emulation using INT8, which is *less efficient* and *less accurate* than true FP4 computation. This limits its peak performance and efficiency in low-precision scenarios compared to Blackwell.
+
+Crucially, the H100 maintains strong FP64 performance, a key advantage for legacy HPC, scientific simulations, and engineering workloads where double-precision accuracy is essential. This makes Hopper a true **dual-use architecture**, capable of excelling in both AI and traditional HPC.
+
+Additional features enhance flexibility and efficiency: [NVLink 4.0](/gpu/reference-content/understanding-nvidia-nvlink/) enables 900 GB/s of GPU-to-GPU bandwidth, and [Multi-Instance GPU (MIG)](/gpu/how-to/use-nvidia-mig-technology/) allows secure, isolated workloads on a single GPU, ideal for Kubernetes cloud environments  
+
+Scaleway’s H100-SXM Instances offer the best cost-performance ratio for most applications, including fine-tuning 7B–70B parameter models, running large-scale inference or RAG pipelines, as well as for computer vision and speech processing.
+
+That said, the 80 GB HBM3 memory can become a bottleneck for models exceeding 400 billion parameters or when processing very long contexts. In such cases, advanced techniques like model parallelism or offloading are often required.