Next-Gen GPUs Explained: H200, GB200, B200, MI300X

NVIDIA H200: The Memory Upgrade

The H200 isn't a new architecture — it's the H100 Hopper chip with upgraded memory. Instead of 80GB HBM3, the H200 packs 141GB HBM3e with 4.8 TB/s bandwidth. That's 1.4x more memory and significantly higher bandwidth, which translates to roughly 2x inference performance on large language models like Llama 2 70B.

When you need it: Running 70B+ parameter models at full precision, or fitting large KV caches for long-context inference. The extra memory eliminates the need for model parallelism on models that would otherwise require 2x H100s.

When you don't: If your model fits in 24GB (most 7-13B models with quantization), an RTX 4090 at $0.49/hr delivers excellent inference performance at 1/9th the cost.

NVIDIA GB200 NVL72: The Supercluster

The GB200 NVL72 is NVIDIA's flagship system — a single rack containing 36 Grace CPUs and 72 Blackwell GPUs, connected by NVLink and NVSwitch for massive aggregate bandwidth. This is designed for training and running trillion-parameter models as a single unified system.

At this scale, we're talking about workloads like pre-training GPT-class models, running massive mixture-of-experts architectures, and real-time inference on models too large for any single GPU. The GB200 NVL72 is not a cloud instance you rent by the hour — it's an AI factory component.

Who it's for: OpenAI, Anthropic, Meta, Google-scale labs. Companies spending $10M+ per year on compute. Not startups.

NVIDIA B200: The Next Mainstream Enterprise GPU

The B200 is the Blackwell-architecture successor to the H100. It delivers roughly 2x the performance of H100 across training and inference workloads, with 192GB HBM3e memory and 8 TB/s bandwidth. Think of it as what the H100 was to the A100 — a generational leap.

The B200 will likely become the standard enterprise GPU for AI over the next 2 years, replacing H100 in new deployments. Cloud pricing is expected to be in the $4-6/hr range for on-demand instances.

AMD MI300X: The NVIDIA Challenger

AMD's Instinct MI300X is the most serious challenger to NVIDIA's data center GPU dominance. With 192GB of HBM3 memory (more than the H100's 80GB), the MI300X can run larger models without splitting across multiple GPUs.

The MI300X wins on raw specs — more memory, more bandwidth, more TFLOPS. But NVIDIA's CUDA ecosystem remains the industry standard. ROCm compatibility has improved dramatically, and frameworks like PyTorch and vLLM now support MI300X well, but the software story is still NVIDIA's strongest advantage.

Do You Actually Need Next-Gen GPUs?

Here's the uncomfortable truth: for most production inference workloads, you don't need an H200, GB200, or MI300X.

The most popular open-source models — Llama 3 8B, Mistral 7B, Stable Diffusion XL, Whisper — fit comfortably in 24GB VRAM. An RTX 4090 delivers excellent inference performance for these models at $0.49/hr on VectorLay. That's 88% cheaper than an H200 on Crusoe ($4.29/hr).

The VectorLay Advantage

VectorLay offers both consumer GPUs (RTX 4090 at $0.49/hr, RTX 3090 at $0.29/hr) and enterprise GPUs (H100 at $2.49/hr, A100 at $1.64/hr) — all with built-in fault tolerance via our overlay network. For the majority of inference workloads, you get better economics on VectorLay than renting next-gen hardware at premium rates.

As next-gen GPUs like H200 and B200 become available in our provider network, we'll add them at the same competitive pricing philosophy. The future of affordable AI compute is distributed.