EDGE OSAI HARDWARE COMPARISON
H100 vs H200Specs, memory and LLM performance
The H200 has the same Hopper compute as the H100 but 76% more memory (141 GB of HBM3e vs 80 GB) and 43% more bandwidth (4.8 TB/s vs 3.35 TB/s).
Memory
80 GB vs 141 GB
Bandwidth
3.35 TB/s vs 4.8 TB/s
Power
Up to 700 W vs Up to 700 W
Type
Data center GPU vs Data center GPU
Which should you choose?
CHOOSE H100 IF
Your models fit comfortably in 80 GB and you want lower cost and wider availability.
CHOOSE H200 IF
You serve 70B+ models or many users at once: the extra memory holds bigger models and more KV cache, and the extra bandwidth speeds up token generation.
Still deciding between H100 and H200?
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H100 vs H200 specs
Manufacturer specifications. Green marks the stronger figure where a higher number is better. Dense compute is shown without sparsity.
| Specification | NVIDIA H100 | NVIDIA H200 |
|---|---|---|
| Type | Data center GPU | Data center GPU |
| Memory | 80 GB | 141 GB |
| Memory type | HBM3 | HBM3e |
| Memory bandwidth | 3.35 TB/s | 4.8 TB/s |
| FP16 / BF16 (dense) | 989 TFLOPS | 989 TFLOPS |
| FP8 (dense) | 1,979 TFLOPS | 1,979 TFLOPS |
| FP4 (dense) | Not supported | Not supported |
| Power | Up to 700 W | Up to 700 W |
| Multi-GPU link | NVLink 4: 900 GB/s | NVLink 4: 900 GB/s |
| Form factor | SXM5 module in 4- or 8-GPU HGX servers (H100 NVL PCIe card: 94 GB, 3.9 TB/s) | SXM5 module in 4- or 8-GPU HGX servers (H200 NVL PCIe card: up to 600 W) |
| Architecture | Hopper, 2022 | Hopper, 2024 |
| Software | CUDA, TensorRT-LLM, vLLM, SGLang | CUDA, TensorRT-LLM, vLLM, SGLang |
| Price | No list price: sold in servers through OEMs and cloud providers | 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 | H100 | H200 |
|---|---|---|
| Usable for models | About 72 GB | About 127 GB |
| Largest dense model at 4-bit (1 user, short context) | About 110B parameters | About 200B parameters |
| Llama 3.3 70B at 4-bit, single-user speed ceiling | About 86 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) | H100 | 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 | 8 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 | No: more than 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.
H100 vs H200 questions
What is the difference between the H100 and the H200?
The H200 has the same Hopper compute as the H100 but 76% more memory (141 GB of HBM3e vs 80 GB) and 43% more bandwidth (4.8 TB/s vs 3.35 TB/s).
H100 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 3.35 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 80 GB. Choose the H100 if your models fit comfortably in 80 GB and you want lower cost and wider availability. Choose the H200 if you serve 70B+ models or many users at once: the extra memory holds bigger models and more KV cache, and the extra bandwidth speeds up token generation.
Can the H100 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 H100 runs it on one GPU at about 86 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 H100 cost compared with the H200?
H100: No list price: sold in servers through OEMs and cloud providers. 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 H100 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.