H200 vs B300 vs RTX PRO 6000: Which GPU for AI Inference?

Short answer: For inference, pick the GPU by memory first and memory bandwidth second. The RTX PRO 6000 (96 GB) is the cost-efficient choice for models up to about 70B parameters when quantized, and for image and video generation. The H200 (141 GB, 4.8 TB/s) suits 70B-class models with long context and many users at once. The B300 (288 GB, up to 8 TB/s) is for the largest models, 16-bit 70B models, mixture-of-experts models, very long context and running several models on one card.

The three GPUs at a glance

NVIDIA RTX PRO 6000 Blackwell (Server Edition). 96 GB of GDDR7 memory, 1,597 GB/s of memory bandwidth, up to 600 W. Blackwell architecture with FP4 support, and Multi-Instance GPU (MIG) that splits one card into up to four isolated GPUs. It is also a full graphics card, so the same server can render.

NVIDIA H200. 141 GB of HBM3e memory, 4.8 TB/s of bandwidth, up to 700 W (SXM). Hopper architecture with FP8 support. NVIDIA rates it 1.9 times faster than the H100 on Llama 2 70B inference, mostly thanks to the larger and faster memory.

NVIDIA B300 (Blackwell Ultra). 288 GB of HBM3e memory and up to 8 TB/s of bandwidth per GPU. Blackwell Ultra architecture with native FP4, built for reasoning models and large-scale serving. In an 8-GPU HGX B300 server the cards are linked by NVLink at 1.8 TB/s per GPU.

Why memory matters most for inference

When a model generates text, every new token requires reading the model's weights from GPU memory. Two things follow. First, the weights plus the KV cache (the stored context of every active conversation) must fit in memory, or the model won't run on that card at all. Second, once it fits, the speed each user sees is set largely by memory bandwidth, not by raw compute. That is why the H200 is much faster than the H100 at inference despite having the same compute, and why the B300's 8 TB/s matters.

A quick sizing rule: parameters times bytes per parameter (2 at 16-bit, 1 at FP8, about 0.5 at 4-bit), plus room for context.

• 8B to 32B models: fit easily on an RTX PRO 6000 at any precision, with lots of room for concurrent users.
• 70B at FP8 (about 70 GB): fits on an RTX PRO 6000 with modest context, or on an H200 with long context and high concurrency.
• 70B at 16-bit (about 140 GB): too tight for an H200 once context is added; comfortable on a B300.
• 120B to 200B at FP8, or large mixture-of-experts models: B300.
• 400B+ models: two or more B300s, or several H200s linked by NVLink.

Which GPU for which job

Choose the RTX PRO 6000 when you serve small and mid-size models (chatbots, RAG, coding assistants, agents) up to 70B quantized, run image or video generation such as Stable Diffusion or Flux, need graphics and AI on the same server, or want to split one card into four with MIG for several small models. It gives the most memory per dollar of the three.

Choose the H200 when you serve 70B-class models in production with long prompts and many users at once, need the mature Hopper and FP8 software stack, or also fine-tune models on the same hardware. Its bandwidth is about three times that of the RTX PRO 6000, so each user gets tokens faster.

Choose the B300 when the model is too big for anything else, you want to run a 70B model at full 16-bit quality on one card, you serve reasoning models with very long outputs, or you want to host several models on a single GPU. FP4 support lets it fit and serve even larger models at lower cost per token.

What about cost?

The right question is cost per token, not cost per hour. A larger GPU that serves many more users at once can be cheaper per token than a small one that is overloaded, and a small GPU that fits your model is far cheaper than a large one that sits half empty. Start with the smallest card that fits the model with the context length you need, measure tokens per second under real load, then compare. Our guide to the cost of running AI in production shows the calculation.

Quick decision guide

• Model under 32B, or image/video generation: RTX PRO 6000.
• 70B quantized, moderate traffic: RTX PRO 6000.
• 70B with long context and high concurrency: H200.
• 70B at 16-bit, 100B+ models, MoE, several models per card: B300.
• Training or fine-tuning large models: H200 or B300.

Rent H200, B300 and RTX PRO 6000 on OMC Cloud

OMC Cloud offers all three as dedicated GPUs with full passthrough, billed hourly or monthly, alongside L4, L40S, RTX 6000 Ada, A16 and H100. Servers come with CUDA, PyTorch and TensorFlow pre-configured, Gen 5 NVMe storage, a 99.95% uptime SLA and 24/7 support from GPU specialists. See the GPU cloud page or our GPU guide.

Sources: NVIDIA official specifications for H200, RTX PRO 6000 Blackwell Server Edition and HGX B300.