The NVIDIA RTX 3090, while a powerful GPU, falls short when running the full Llama 3.1 70B model, even with INT8 quantization. The primary bottleneck is VRAM. Llama 3.1 70B quantized to INT8 requires approximately 70GB of VRAM. The RTX 3090 only offers 24GB, leaving a deficit of 46GB. This discrepancy prevents the model from loading and running effectively, as the entire model and its intermediate computations cannot fit within the GPU's memory. Memory bandwidth, while substantial at 0.94 TB/s, becomes irrelevant if the data cannot reside on the GPU in the first place. The Ampere architecture and its CUDA and Tensor cores cannot be fully utilized due to the VRAM limitation. Consequently, direct inference is not feasible without significant adjustments.
Given the VRAM constraint, running the full Llama 3.1 70B model directly on the RTX 3090 is impractical. Consider offloading some layers to system RAM (CPU) using libraries like `llama.cpp` with appropriate flags. This will drastically reduce inference speed. Alternatively, explore smaller Llama 3 models (e.g., 8B or 13B) or consider using a cloud-based GPU service with sufficient VRAM, such as those offered by NelsaHost. Quantization to lower bit precisions like Q4_K_M may reduce VRAM usage further, but will impact accuracy. Another option is to split the model across multiple GPUs if available, but this requires advanced setup and specialized software.