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A100 vs H100Specs, memory and LLM performance
The H100 is the A100’s successor. It has the same 80 GB of memory but about 1.6× the bandwidth, roughly 3× the FP16 compute and native FP8 support, which the A100 lacks.
Memory
80 GB vs 80 GB
Bandwidth
2.04 TB/s vs 3.35 TB/s
Power
400 W vs Up to 700 W
Type
Data center GPU vs Data center GPU
Which should you choose?
CHOOSE A100 IF
You already own A100s or can rent them cheaply, and your models fit in 80 GB at BF16 or INT8.
CHOOSE H100 IF
You are buying new. FP8 roughly doubles throughput again, so the H100 is far faster for both inference and training.
Still deciding between A100 and H100?
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A100 vs H100 specs
Manufacturer specifications. Green marks the stronger figure where a higher number is better. Dense compute is shown without sparsity.
| Specification | NVIDIA A100 80GB | NVIDIA H100 |
|---|---|---|
| Type | Data center GPU | Data center GPU |
| Memory | 80 GB | 80 GB |
| Memory type | HBM2e | HBM3 |
| Memory bandwidth | 2.04 TB/s | 3.35 TB/s |
| FP16 / BF16 (dense) | 312 TFLOPS | 989 TFLOPS |
| FP8 (dense) | Not supported | 1,979 TFLOPS |
| FP4 (dense) | Not supported | Not supported |
| Power | 400 W | Up to 700 W |
| Multi-GPU link | NVLink 3: 600 GB/s | NVLink 4: 900 GB/s |
| Form factor | SXM4 module in 4- or 8-GPU servers (PCIe version: 300 W) | SXM5 module in 4- or 8-GPU HGX servers (H100 NVL PCIe card: 94 GB, 3.9 TB/s) |
| Architecture | Ampere, 2020 | Hopper, 2022 |
| 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 | A100 | H100 |
|---|---|---|
| Usable for models | About 72 GB | About 72 GB |
| Largest dense model at 4-bit (1 user, short context) | About 110B parameters | About 110B parameters |
| Llama 3.3 70B at 4-bit, single-user speed ceiling | About 53 tokens/s | About 86 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) | A100 | H100 |
|---|---|---|
| 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 | 4 GPUs |
| DeepSeek-V4.1-Flash 552B · about 320 GB at 4-bit | 8 GPUs | 8 GPUs |
| DeepSeek-R1 671B · about 410 GB at 4-bit | 8 GPUs | 8 GPUs |
| DeepSeek-V3.2 685B · about 419 GB at NVFP4 | 8 GPUs | 8 GPUs |
| GLM-5.2 744B · about 455 GB at NVFP4 | 8 GPUs | 8 GPUs |
| GLM-5.3 753B · about 460 GB at NVFP4 | 8 GPUs | 8 GPUs |
| GLM-5.1 754B · about 461 GB at NVFP4 | 8 GPUs | 8 GPUs |
| Kimi K2 1T · about 579 GB at 4-bit | No: more than 8 GPUs | No: more than 8 GPUs |
| DeepSeek-V4-Pro 1.6T · about 976 GB at FP4 + FP8 | No: more than 8 GPUs | No: more than 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.
A100 vs H100 questions
What is the difference between the A100 and the H100?
The H100 is the A100’s successor. It has the same 80 GB of memory but about 1.6× the bandwidth, roughly 3× the FP16 compute and native FP8 support, which the A100 lacks.
A100 vs H100: which is better for LLM inference?
For generating tokens, the H100 is faster: it has 3.35 TB/s of memory bandwidth versus 2.04 TB/s, and LLM token generation is mostly limited by bandwidth. Choose the A100 if you already own A100s or can rent them cheaply, and your models fit in 80 GB at BF16 or INT8. Choose the H100 if you are buying new. FP8 roughly doubles throughput again, so the H100 is far faster for both inference and training.
Can the A100 and H100 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 A100 runs it on one GPU at about 53 tokens/s at most for one user, and the H100 runs it on one GPU at about 86 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 A100 cost compared with the H100?
A100: No list price: sold in servers through OEMs and cloud providers. H100: 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 A100 and NVIDIA H100. 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.