Best AI Models for NVIDIA DGX H100 (640.0GB)
640.0 GB unified − 3.5 GB OS overhead = 636.5 GB available for AI models
With 640 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 DGX H100 Run?
Showing compatibility for NVIDIA DGX H100
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
| BF16 | 145.0 GB23% | 120.2 t/s | 131K | EASY RUN | C38 | |
| Q4_K_M | 5.5 GB1% | 3155.8 t/s | 41K | EASY RUN | D26 | |
| Q4_K_M | 13.3 GB2% | 1311.7 t/s | 131K | EASY RUN | D26 | |
| Q4_K_M | 5.3 GB1% | 3299.2 t/s | 131K | EASY RUN | D26 | |
| Q4_K_M | 46.6 GB7% | 374.1 t/s | 131K | EASY RUN | D29 | |
| Q4_K_M | 44.6 GB7% | 390.7 t/s | 33K | EASY RUN | D29 | |
| Q4_K_M | 46.2 GB7% | 377.1 t/s | 131K | EASY RUN | D29 | |
| Q4_K_M | 2.9 GB0% | 6027.7 t/s | 41K | EASY RUN | D25 |
NVIDIA DGX H100 Specifications
- Brand
- NVIDIA
- Chip
- 8x H100 SXM5
- Type
- Server
- Unified Memory
- 640.0 GB
- Memory Bandwidth
- 26800.0 GB/s
- GPU Cores
- 135168
- CPU Cores
- 112
- Release Date
- 2022-09-01
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Frequently Asked Questions
- Can NVIDIA DGX H100 run Llama 3 8B?
Yes, the NVIDIA DGX H100 with 640 GB unified memory can run Llama 3 8B at multiple quantization levels. At Q4_K_M (the recommended starting point), you'll get smooth token generation suitable for interactive chat and coding assistance.
- How much memory is available for AI on NVIDIA DGX H100?
The NVIDIA DGX H100 has 640 GB unified memory. After macOS overhead (~3.5 GB), approximately 636.5 GB is available for AI models. This unified memory architecture is efficient since the GPU and CPU share the same memory pool without copy overhead.
- Is NVIDIA DGX H100 good for AI?
With 640 GB unified memory and 26800.0 GB/s bandwidth, the NVIDIA DGX H100 is excellent for running local LLM models. Apple Silicon's unified memory and Metal acceleration provide a premium local AI experience.
- What's the best model for NVIDIA DGX H100?
For the NVIDIA DGX H100, we recommend starting with Llama 3 70B at Q3_K_M for maximum capability, or Qwen 2.5 7B at Q6 for best quality-to-speed ratio. Use Ollama or LM Studio for easy setup.
- How fast is NVIDIA DGX H100 for AI inference?
Token generation speed depends on the model and quantization. With 26800.0 GB/s memory bandwidth, you can expect 30-60+ tokens per second on 7B models at Q4_K_M, which is comfortable for real-time chat interaction.