LLM VRAM Requirements
Understand GPU memory requirements for popular AI models including Llama, Mixtral, DeepSeek, and Stable Diffusion.
VRAM determines whether a model can load and run reliably on your GPU, especially as context windows, batch size, and generation speed increase. Quantization lowers memory usage by compressing model weights, but it can trade off accuracy and throughput depending on workload. GPU count also matters because very large models often need memory sharding across multiple cards, and interconnect bandwidth can become a performance bottleneck.
VRAM Reference Table
| Model | FP16 VRAM | 8-bit VRAM | 4-bit VRAM | Typical GPU Setup |
|---|---|---|---|---|
| Llama 3 8B | 16 GB | 10 GB | 6 GB | 1× RTX 4090 (24 GB) |
| Llama 3 70B | 140 GB | 88 GB | 52 GB | 2× A100 80 GB or 2× H100 80 GB |
| Mixtral 8x7B | 90 GB | 56 GB | 34 GB | 1–2× A100/H100 depending on context length |
| DeepSeek 67B | 134 GB | 84 GB | 50 GB | 2× A100 80 GB or 2× H100 80 GB |
| SDXL | 12 GB | 8 GB | N/A | 1× RTX 4090 or RTX 6000 Ada |
| FLUX | 24 GB | 16 GB | 10 GB | 1× RTX 4090 (inference), A100/H100 for scale |
Quantization Levels
- FP16: Baseline high-fidelity precision used for strong quality and stable training.
- 8-bit: Reduces memory significantly with modest quality impact for many inference tasks.
- 4-bit: Maximizes memory savings and enables larger models on fewer GPUs, but may reduce output quality depending on model and use case.
GPU Recommendations
For single-GPU inference, the RTX 4090 (24 GB) remains a top value choice. The RTX 6000 Ada (48 GB) offers professional reliability and larger memory headroom. For heavy multi-user inference and training, A100 and H100 systems provide the memory capacity and bandwidth needed for large checkpoints and longer context windows.
Plan Your Build with ComputeAtlas
Get a practical hardware estimate for your exact model, precision, and workload.