Best AI Models for NVIDIA H100 SXM (80.0GB)
With 80 GB of memory, this is a high-end configuration for local AI. You can comfortably run most open-source LLMs including large 70B parameter models at good quantization levels, making it one of the best setups for serious local AI work.
At this memory tier, nearly every popular open-source model is within reach. You can run Llama 3 70B at Q4_K_M or even Q5_K_M quantization with room to spare, handle coding assistants like DeepSeek Coder 33B at high quality, and easily run any 7B–30B model at full or near-full precision. Context windows remain generous even with larger models, so multi-turn conversations and long-document processing work smoothly.
Runs Well
- 70B models (Llama 3 70B, Qwen 72B) at Q4–Q5
- 30B models at Q6–Q8 quality
- 7B–14B models at full FP16 precision
- Vision models (LLaVA, CogVLM) without compromise
Challenging
- Mixture-of-experts models like Mixtral 8x22B at higher quants
- 120B+ models still require lower quantizations
What LLMs Can NVIDIA H100 SXM Run?
Showing compatibility for NVIDIA H100 SXM
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
| Q4_K_M | 46.6 GB58% | 46.8 t/s | 131K | GOOD FIT | A74 | |
| Q4_K_M | 46.2 GB58% | 47.2 t/s | 131K | GOOD FIT | A74 | |
| Q4_K_M | 44.6 GB56% | 48.9 t/s | 33K | GOOD FIT | A71 | |
| Q4_K_M | 28.6 GB36% | 76.2 t/s | 33K | FAIR FIT | B51 | |
| Q4_K_M | 19.8 GB25% | 109.8 t/s | 41K | EASY RUN | C40 | |
| Q4_K_M | 20.5 GB26% | 106.3 t/s | 131K | EASY RUN | C41 | |
| Q4_K_M | 20.5 GB26% | 106.3 t/s | 33K | EASY RUN | C41 | |
| Q4_K_M | 72.7 GB91% | 30.0 t/s | 131K | FAIR FIT | B48 |
NVIDIA H100 SXM Specifications
- Brand
- NVIDIA
- Architecture
- Hopper
- VRAM
- 80.0 GB HBM3
- Memory Bandwidth
- 3352.0 GB/s
- CUDA Cores
- 16,896
- Tensor Cores
- 528
- FP16 Performance
- 989.40 TFLOPS
- TDP
- 700W
- Release Date
- 2022-09-01
Get Started
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Frequently Asked Questions
- Can NVIDIA H100 SXM run Llama 3 8B?
Yes, the NVIDIA H100 SXM with 80 GB can run Llama 3 8B at Q4_K_M quantization with good performance. At this VRAM level, you can expect smooth token generation and responsive inference for chat and coding tasks.
- Is NVIDIA H100 SXM good for AI?
The NVIDIA H100 SXM has 80 GB of HBM3, making it excellent for running local LLM models. You can run most popular 7B-30B models at good quality.
- How many parameters can NVIDIA H100 SXM handle?
With 80 GB, the NVIDIA H100 SXM can handle models up to approximately 30-70B parameters depending on quantization. Using Q4_K_M quantization (the typical sweet spot), you can fit roughly 133B parameters.
- What quantization should I use on NVIDIA H100 SXM?
For the best balance of quality and speed on 80 GB, Q4_K_M is the recommended starting point. If you have headroom, try Q5_K_M for better quality. For larger models that barely fit, Q3_K_M or Q2_K can squeeze them in at the cost of some output quality.
- How fast is NVIDIA H100 SXM for AI inference?
Speed depends on the model size and quantization. With 3352.0 GB/s memory bandwidth, the NVIDIA H100 SXM can typically achieve 30-50+ tokens per second on 7B models at Q4_K_M quantization, which is comfortable for interactive chat.