The NVIDIA RTX 3090 Ti, equipped with 24GB of GDDR6X VRAM and a memory bandwidth of 1.01 TB/s, faces significant challenges when running the Llama 3.1 405B model. Even with INT8 quantization, the model demands 405GB of VRAM, far exceeding the 3090 Ti's capacity. This enormous discrepancy means the entire model cannot reside on the GPU's memory, leading to a 'FAIL' compatibility verdict. The Ampere architecture's 10752 CUDA cores and 336 Tensor cores, while powerful, cannot compensate for the fundamental limitation imposed by insufficient VRAM. Memory bandwidth, though substantial, becomes a secondary concern when the model's size necessitates offloading data to system RAM, severely bottlenecking performance.
In practical terms, attempting to run Llama 3.1 405B on an RTX 3090 Ti without substantial modifications will result in out-of-memory errors. The model's parameters simply overwhelm the available resources. Even with aggressive quantization techniques beyond INT8, the memory footprint remains prohibitively large. While the RTX 3090 Ti can handle smaller models effectively, the sheer scale of Llama 3.1 405B necessitates a multi-GPU setup or a system with significantly more VRAM. The expected tokens per second and achievable batch size are essentially zero in this configuration, as the model cannot be loaded and processed effectively.
Given the VRAM constraints, running Llama 3.1 405B on a single RTX 3090 Ti is not feasible. Consider exploring distributed inference across multiple GPUs, each with sufficient VRAM to hold a portion of the model. Alternatively, investigate cloud-based solutions or services that offer access to hardware configurations capable of running such large models. For local experimentation, focus on smaller Llama 3 models or other models that fit within the 3090 Ti's memory capacity.
If you are determined to experiment with Llama 3.1 405B locally, explore extreme quantization methods like 4-bit or even 2-bit quantization, understanding that this will significantly impact the model's accuracy and coherence. Even then, you might need to offload some layers to the CPU, which will drastically reduce inference speed. A more practical approach would be to utilize a cloud-based inference service or rent a more powerful GPU with sufficient VRAM.