Best AI Models for NVIDIA A100 40GB PCIe (40.0GB)
40 GB positions this hardware in the professional tier for local AI. Most popular open-source models run comfortably, and even large 70B parameter models are accessible at lower quantization levels.
This memory amount is a sweet spot for enthusiasts and professionals. You can run 13B–30B models like DeepSeek R1 Distill at Q5 or Q6 quality with smooth token generation, and 7B models at near-lossless precision. The 70B class of models (Llama 3 70B, Qwen 72B) becomes possible at Q2–Q3 quantization, though with some quality trade-off. For day-to-day use with coding assistants, chat models, and reasoning tasks, this tier delivers an excellent experience.
Runs Well
- 7B–13B models at Q6–Q8 quality
- 14B–30B models at Q4–Q5 quality
- Small models (3B–7B) at FP16 precision
- Vision-language models at good quality
Challenging
- 70B models only at Q2–Q3 (noticeable quality loss)
- Large context windows with 30B+ models
What LLMs Can NVIDIA A100 40GB PCIe Run?
Showing compatibility for NVIDIA A100 40GB PCIe
| Model | Quant | VRAM | Speed | Context | Status | Grade |
|---|---|---|---|---|---|---|
| Q4_K_M | 1.0 GB3% | 1000.7 t/s | 2K | EASY RUN | D27 | |
| Q4_K_M | 0.7 GB2% | 1531.4 t/s | 131K | EASY RUN | D26 | |
| Q4_K_M | 0.7 GB2% | 1531.4 t/s | 33K | EASY RUN | D26 | |
| Q4_K_M | 2.9 GB7% | 354.6 t/s | 131K | EASY RUN | D29 | |
| Q4_K_M | 1.3 GB3% | 765.7 t/s | 8K | EASY RUN | D27 |
NVIDIA A100 40GB PCIe Specifications
- Brand
- NVIDIA
- Architecture
- Ampere
- VRAM
- 40.0 GB HBM2e
- Memory Bandwidth
- 1555.0 GB/s
- CUDA Cores
- 6,912
- Tensor Cores
- 432
- FP16 Performance
- 312.00 TFLOPS
- TDP
- 250W
- Release Date
- 2020-05-14
Get Started
Frequently Asked Questions
- Can NVIDIA A100 40GB PCIe run Llama 3 8B?
Yes, the NVIDIA A100 40GB PCIe with 40 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 A100 40GB PCIe good for AI?
The NVIDIA A100 40GB PCIe has 40 GB of HBM2e, making it excellent for running local LLM models. You can run most popular 7B-30B models at good quality.
- How many parameters can NVIDIA A100 40GB PCIe handle?
With 40 GB, the NVIDIA A100 40GB PCIe 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 66B parameters.
- What quantization should I use on NVIDIA A100 40GB PCIe?
For the best balance of quality and speed on 40 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 A100 40GB PCIe for AI inference?
Speed depends on the model size and quantization. With 1555.0 GB/s memory bandwidth, the NVIDIA A100 40GB PCIe can typically achieve 30-50+ tokens per second on 7B models at Q4_K_M quantization, which is comfortable for interactive chat.