The NVIDIA RTX 3090, with its 24GB of GDDR6X VRAM, falls short of the 35GB VRAM requirement for running Llama 3.1 70B (70.00B) quantized to Q4_K_M. This quantization reduces the model's memory footprint, but the 11GB VRAM deficit prevents the model from loading and running effectively on the RTX 3090. While the RTX 3090 boasts a high memory bandwidth of 0.94 TB/s and a substantial number of CUDA and Tensor cores, the primary bottleneck here is insufficient VRAM, precluding meaningful inference.
Even with aggressive quantization, the model's parameters and computational graph exceed the RTX 3090's memory capacity. Memory bandwidth becomes relevant only once the model resides in VRAM. Since the model cannot be fully loaded, the theoretical memory bandwidth and computational power of the RTX 3090 are not utilized. Consequently, users can expect the model to either fail to load or encounter out-of-memory errors during inference. Performance metrics like tokens/sec and batch size are irrelevant in this scenario due to the fundamental VRAM constraint.
Due to the VRAM limitations, running Llama 3.1 70B (70.00B) on an RTX 3090 is not feasible without significant compromises. Consider using a smaller model variant, such as Llama 3.1 8B, which has a much lower VRAM footprint. Alternatively, offloading some layers to system RAM (CPU) using frameworks like `llama.cpp` might allow the model to load, but this will drastically reduce inference speed. Another option is to distribute the model across multiple GPUs if available.
If you're committed to running the 70B model, upgrading to a GPU with more VRAM (e.g., NVIDIA RTX 4090, or professional-grade GPUs like the A100 or H100) is the most straightforward solution. Cloud-based GPU instances also provide access to high-VRAM GPUs without requiring a hardware purchase. Experiment with different quantization levels, but be aware that extreme quantization can impact model accuracy. Finally, consider using inference frameworks optimized for memory efficiency, such as vLLM, which can reduce memory overhead.