EDGE OSAI HARDWARE COMPARISON
RTX PRO 6000 vs H200Specs, memory and LLM performance
The H200 has 47% more memory (141 GB vs 96 GB) and 2.7× the bandwidth (4.8 TB/s vs 1.8 TB/s), with NVLink between GPUs. The RTX PRO 6000 is a PCIe card that fits standard servers and workstations at far lower cost and power.
Memory
96 GB vs 141 GB
Bandwidth
1.79 TB/s vs 4.8 TB/s
Power
Up to 600 W vs Up to 700 W
Type
Workstation and server GPU vs Data center GPU
Which should you choose?
CHOOSE RTX PRO 6000 IF
You want 96 GB per GPU in a standard server or workstation to serve small and mid-size teams.
CHOOSE H200 IF
You run large models across many GPUs at high concurrency, where NVLink and HBM bandwidth matter.
Still deciding between RTX PRO 6000 and H200?
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RTX PRO 6000 vs H200 specs
Manufacturer specifications. Green marks the stronger figure where a higher number is better. Dense compute is shown without sparsity.
| Specification | NVIDIA RTX PRO 6000 Blackwell | NVIDIA H200 |
|---|---|---|
| Type | Workstation and server GPU | Data center GPU |
| Memory | 96 GB | 141 GB |
| Memory type | GDDR7 with ECC | HBM3e |
| Memory bandwidth | 1.79 TB/s | 4.8 TB/s |
| FP16 / BF16 (dense) | Not published | 989 TFLOPS |
| FP8 (dense) | Not published | 1,979 TFLOPS |
| FP4 (dense) | 2,000 TFLOPS | Not supported |
| Power | Up to 600 W (Max-Q edition: 300 W) | Up to 700 W |
| Multi-GPU link | PCIe Gen5 x16 (no NVLink) | NVLink 4: 900 GB/s |
| Form factor | Dual-slot PCIe card: Workstation, Max-Q and Server editions | SXM5 module in 4- or 8-GPU HGX servers (H200 NVL PCIe card: up to 600 W) |
| Architecture | Blackwell, 2025 | Hopper, 2024 |
| Software | CUDA, TensorRT-LLM, vLLM, SGLang | CUDA, TensorRT-LLM, vLLM, SGLang |
| Price | No NVIDIA list price: sold through partners | No list price: sold in servers through OEMs and cloud providers |
What they mean for running LLMs
For large language models, memory decides which models fit and how many users you can serve, and memory bandwidth decides how fast each user gets tokens. These are planning figures for one GPU or system using a 4-bit model.
| For LLMs | RTX PRO 6000 | H200 |
|---|---|---|
| Usable for models | About 86 GB | About 127 GB |
| Largest dense model at 4-bit (1 user, short context) | About 140B parameters | About 200B parameters |
| Llama 3.3 70B at 4-bit, single-user speed ceiling | About 46 tokens/s | About 124 tokens/s |
Which open-weight models fit
Memory needed for one user with 8K context at the lowest precision available for each model. Open a model for team-size and fine-tuning figures.
| Model (1 user, 8K context, lowest available precision) | RTX PRO 6000 | H200 |
|---|---|---|
| gpt-oss-20b 21B · about 15 GB at MXFP4 | Yes | Yes |
| Gemma 4 26B A4B 25.2B · about 16 GB at 4-bit | Yes | Yes |
| Qwen3.6-27B 27.78B · about 20 GB at 4-bit | Yes | Yes |
| Qwen3.8-27B 27.8B · about 24 GB at NVFP4 | Yes | Yes |
| Gemma 4 31B 30.7B · about 20 GB at 4-bit | Yes | Yes |
| Qwen3.6-35B-A3B 35B · about 24 GB at NVFP4 | Yes | Yes |
| Llama 3.3 70B Instruct 70.6B · about 46 GB at INT4 | Yes | Yes |
| gpt-oss-120b 117B · about 70 GB at MXFP4 | Yes | Yes |
| MiniMax-M2.5 229B · about 135 GB at 4-bit | 2 GPUs | 2 GPUs |
| GLM-5.3-Flash 320B · about 192 GB at NVFP4 | 4 GPUs | 2 GPUs |
| DeepSeek-V4.1-Flash 552B · about 320 GB at 4-bit | 4 GPUs | 4 GPUs |
| DeepSeek-R1 671B · about 410 GB at 4-bit | 8 GPUs | 4 GPUs |
| DeepSeek-V3.2 685B · about 419 GB at NVFP4 | 8 GPUs | 4 GPUs |
| GLM-5.2 744B · about 455 GB at NVFP4 | 8 GPUs | 4 GPUs |
| GLM-5.3 753B · about 460 GB at NVFP4 | 8 GPUs | 4 GPUs |
| GLM-5.1 754B · about 461 GB at NVFP4 | 8 GPUs | 4 GPUs |
| Kimi K2 1T · about 579 GB at 4-bit | 8 GPUs | 8 GPUs |
| DeepSeek-V4-Pro 1.6T · about 976 GB at FP4 + FP8 | No: more than 8 GPUs | 8 GPUs |
| Kimi K3 2.8T · about 1.62 TB at MXFP4 | No: more than 8 GPUs | No: more than 8 GPUs |
Models in amber need several of these GPUs working as one. Buying the GPUs is the easy part. Vanik Edge OS splits the model across them, keeps it running if a server fails and manages it all from one console. See how Edge OS clusters GPU servers
FROM SIZING TO RUNNING
Sizing the hardware is the easy part.
Getting several GPUs to work as one system for a whole company is where most projects slow down. That is the part Vanik Edge OS does for you.
STEP 01 · THIS TOOL
Size it
Work out the GPU memory your models need and which GPUs or servers can hold them.
STEP 02 · VANIK EDGE OS
Cluster it
Edge OS joins your GPU servers into one cluster, splits a large model across them and keeps it running if a server fails. NVIDIA and AMD servers work side by side.
STEP 03 · VANIK EDGE OS
Run it
Your team gets AI apps with single sign-on and an audit trail, managed from one console, with your data staying inside your building.
RTX PRO 6000 vs H200 questions
What is the difference between the RTX PRO 6000 and the H200?
The H200 has 47% more memory (141 GB vs 96 GB) and 2.7× the bandwidth (4.8 TB/s vs 1.8 TB/s), with NVLink between GPUs. The RTX PRO 6000 is a PCIe card that fits standard servers and workstations at far lower cost and power.
RTX PRO 6000 vs H200: which is better for LLM inference?
For generating tokens, the H200 is faster: it has 4.8 TB/s of memory bandwidth versus 1.79 TB/s, and LLM token generation is mostly limited by bandwidth. The H200 holds larger models and more users’ context with 141 GB of memory versus 96 GB. Choose the RTX PRO 6000 if you want 96 GB per GPU in a standard server or workstation to serve small and mid-size teams. Choose the H200 if you run large models across many GPUs at high concurrency, where NVLink and HBM bandwidth matter.
Can the RTX PRO 6000 and H200 run Llama 3.3 70B?
Llama 3.3 70B needs about 46 GB of memory at 4-bit for one user with 8K context. The RTX PRO 6000 runs it on one GPU at about 46 tokens/s at most for one user, and the H200 runs it on one GPU at about 124 tokens/s at most for one user. Speeds are theoretical ceilings from memory bandwidth; real speeds are usually 50 to 80% of that.
How much does the RTX PRO 6000 cost compared with the H200?
RTX PRO 6000: No NVIDIA list price: sold through partners. H200: No list price: sold in servers through OEMs and cloud providers. Data center GPUs are usually bought as part of 4- or 8-GPU servers, so compare full-system quotes.
How are these numbers calculated?
Memory, bandwidth, compute and power are manufacturer specifications. Model memory needs come from our VRAM calculator: weights plus KV cache plus about 5 to 10% runtime overhead, keeping 10% of each GPU free (15% on GB10 systems, 25% on Mac). The speed ceiling is memory bandwidth divided by the model’s weight size. All figures are planning estimates.
Related comparisons
Specifications from the manufacturer pages for the NVIDIA RTX PRO 6000 Blackwell and NVIDIA H200. Speed ceilings are bandwidth divided by model size, before batching; real throughput depends on the inference engine and settings. Last checked November 10, 2026.
Not sure which hardware fits your team?
Vanik Edge OS runs open-weight models on NVIDIA DGX, AMD Instinct and other GPU servers on your premises, and helps you size the hardware first.