RTX PRO 6000 vs H100: which GPU is right for your business?
Short answer: choose the H100 if you are training AI models, or if every user needs fast replies. Choose the RTX PRO 6000 if your model is a little too big for an H100. It has 96 GB of memory against the H100's 80 GB. It also suits video and graphics work, and teams that want to share one GPU.
The tables below compare the two cards, show which jobs suit each, and help you check whether your model fits.
How are the RTX PRO 6000 and H100 different?
They are built for different jobs. The H100 is NVIDIA's data-centre GPU for AI. We compare the H100 SXM, the version that comes built into servers of four or eight GPUs. Those GPUs are joined by NVLink, NVIDIA's fast link between GPUs, so they work together as one big machine. That makes the H100 strong for training, and for models too big for one card.
The RTX PRO 6000 Server Edition comes from NVIDIA's newer Blackwell generation (the H100 is from the older Hopper generation). It is a card that fits standard servers. It has more memory and built-in video and graphics engines, and it can be split between teams. But these cards talk to each other only over PCIe, the standard and slower server connection. So the RTX PRO 6000 works best when each job fits on one card.
A tip if you compare speed tests: some use the desktop Workstation Edition, which has slightly faster memory. Check that the numbers are for the Server Edition, the version used in data centres. For the wider picture, see our Hopper vs Blackwell comparison.
The key numbers side by side
Feature | RTX PRO 6000 Server Edition | H100 SXM |
|---|---|---|
Memory on the card | 96 GB (GDDR7) | 80 GB (HBM3) |
Memory speed (bandwidth) | 1,597 GB/s | 3,350 GB/s, about 2.1 times faster |
Connection between GPUs (NVLink or PCIe) | PCIe only, 128 GB/s; no NVLink | NVLink, 900 GB/s per GPU, about seven times faster |
AI processing power, 4-bit (FP4) | 4 PFLOPS | No built-in 4-bit support |
AI processing power, 8-bit (FP8) | 2 PFLOPS | 3.96 PFLOPS |
AI processing power, 16-bit (FP16/BF16) | 1 PFLOPS | 1.98 PFLOPS |
Rendering and simulation power (FP32) | 120 TFLOPS | 67 TFLOPS |
High-precision science (FP64) | About 1.9 TFLOPS (our estimate) | 34 TFLOPS |
Splitting one GPU (MIG) | Up to 4 slices of 24 GB, with graphics | Up to 7 slices of 10 GB, no graphics or video encoding |
Video and graphics engines | 4 video encoders, 4 decoders, 188 ray-tracing cores | No video encoders, 7 decoders, no ray-tracing cores |
Power draw | Up to 600 W, can be set lower | Up to 700 W, can be set lower |
All figures are NVIDIA's published numbers. PFLOPS and TFLOPS count calculations per second, so higher is faster (1 PFLOPS is 1,000 TFLOPS). The AI figures are NVIDIA's best-case peak ratings, which assume a shortcut called sparsity. Without it, they are half these figures for both cards. NVIDIA does not publish the RTX PRO 6000's FP64 figure, so that one is our estimate.
What this means for you: on paper, the H100 has about twice the AI processing power. That holds for the 8-bit and 16-bit formats most models use today. Its memory is also about twice as fast. In NVIDIA's own tests the real gap is smaller, and it varies by model. The RTX PRO 6000 catches up, on paper, when a model runs in the newer 4-bit format. The H100 has no built-in 4-bit support. The RTX PRO 6000 is also ahead for graphics, video, rendering and simulation.
Where each GPU wins
The RTX PRO 6000 wins on memory per card, video, graphics and sharing one GPU. The H100 wins on training, fast replies and jobs that span several GPUs.
Workload | Better fit | Why |
|---|---|---|
Running a model that fits in 96 GB but not 80 GB, such as a 70 billion parameter model in 8-bit | RTX PRO 6000 | The whole model fits on one card; on the H100 it usually runs on two GPUs. |
Running models in the newer 4-bit format | RTX PRO 6000 | Built-in 4-bit processing for extra speed; the H100 has none. |
Compressing, converting and streaming video | RTX PRO 6000 | Four video encoders on the card; the H100 has none. |
3D rendering, simulation and virtual desktops | RTX PRO 6000 | 188 ray-tracing cores, 120 against 67 TFLOPS of FP32 power, and virtual desktop support. |
Sharing one GPU between teams or apps | RTX PRO 6000 | Splits into four 24 GB slices with graphics and video. The H100 gives more slices, but each is only 10 GB. |
Light fine-tuning on one GPU (adapting a model to your data, for example with LoRA) | Either | Both handle it; the extra 16 GB helps with larger models. |
Generating images and video with AI | H100 | Faster with popular image and video models, such as SDXL and Stable Video Diffusion, in NVIDIA's own tests. |
Training and heavy fine-tuning | H100 | NVLink at 900 GB/s against PCIe at 128 GB/s, so jobs that use many GPUs at once run far better. |
Models split across several GPUs | H100 | Same reason: the faster link between GPUs. The H100 also led in most of NVIDIA's own eight-GPU tests. |
Fast replies for chat, coding assistants and AI agents | H100 | 2.1 times the memory speed, so each user gets replies faster. |
High-precision science (FP64) | H100 | 34 TFLOPS against about 1.9. |
Results vary by model, so test your own before you commit. If fast replies matter most, the H200, with 141 GB of memory, is faster still. For why the link between GPUs matters so much, see our guide to NVLink, NVSwitch and InfiniBand.
Will your AI model fit on one GPU?
An AI model runs best when the whole model, plus some working space for the conversations it is handling, fits in one GPU's memory. The RTX PRO 6000 has 96 GB of memory and the H100 has 80 GB. That 16 GB difference decides a lot.
Your model | RTX PRO 6000 (96 GB) | H100 (80 GB) |
|---|---|---|
Small, around 8 billion parameters | Fits easily, with room to share the card | Fits easily |
Medium, around 32 billion | Fits | Fits |
Large, around 70 billion, in 8-bit (a common compressed format) | Fits on one card, with room for about 30 medium-length conversations at once (our estimate, at around 4,000 tokens, or word pieces, each) | Only just; in practice it usually runs on two H100s or one H200 |
Large, around 70 billion, in the newer 4-bit format | Fits with plenty of room, with built-in 4-bit processing for extra speed | Fits, but does its calculations in 8-bit or 16-bit, so it misses the 4-bit speed boost |
Very large, 400 billion and up | Needs several GPUs | Needs several GPUs; the H100's faster links between GPUs usually make it the better choice |
Not sure where your model sits? Tell us the model and how many users you expect, and we will tell you which GPU and how many. For the detailed maths, see our GPU memory guide.
Which one should you choose?
If this sounds like you | Choose |
|---|---|
Your model fits in 96 GB but not in 80 GB | RTX PRO 6000: one card instead of two |
You want every user to get fast replies: chat, coding assistants, AI agents | H100, or the H200 for faster still |
You are training a model, or doing heavy fine-tuning | |
You compress, convert or stream video, or run 3D rendering or virtual desktops | RTX PRO 6000 |
You want to split one GPU between several teams or apps | RTX PRO 6000 |
You run scientific work that needs high precision (FP64) | H100 |
Getting either GPU through FlexiCloud
We source dedicated GPU servers across more than ten partner providers in India, the EU and the US. Tell us what you are building, and we will match the card to the job and come back with options. There is no charge for the advice. If you need more memory per GPU, the H200, with 141 GB, is another option.
More on each card is on our H100 and RTX PRO 6000 pages. Still deciding whether to rent at all? Our rent, buy or API guide sets out the trade-offs. Current rates are on the pricing page, or talk to an engineer.