The NVIDIA RTX 3070 Ti, with its 8GB of GDDR6X VRAM, falls significantly short of the 140GB VRAM required to load the Llama 3.3 70B model in FP16 (half-precision floating point). This discrepancy of -132GB in VRAM headroom means the model, in its full FP16 form, simply cannot fit within the GPU's memory. Even with the RTX 3070 Ti's memory bandwidth of 0.61 TB/s and 6144 CUDA cores, the limiting factor is the insufficient VRAM, making direct inference impossible without substantial modifications.
While the RTX 3070 Ti's Ampere architecture and 192 Tensor Cores are designed to accelerate AI workloads, they cannot compensate for the fundamental lack of memory. The model's 70 billion parameters require a large memory footprint to store the weights and activations during inference. Attempting to load the full model would result in an out-of-memory error. Therefore, strategies like quantization are essential to reduce the model's size and make it fit within the available VRAM.
To run Llama 3.3 70B on an RTX 3070 Ti, you'll need to aggressively quantize the model. Experiment with 4-bit (Q4) or even 3-bit quantization using libraries like `llama.cpp` or `AutoGPTQ`. Quantization reduces the memory footprint, potentially bringing it within the 8GB VRAM limit. However, be aware that extreme quantization levels can impact the model's accuracy and coherence.
Consider using CPU offloading in conjunction with quantization. This involves moving some layers of the model to system RAM, freeing up VRAM. However, this will significantly slow down inference speed due to the slower transfer rates between system RAM and the GPU. If feasible, explore cloud-based solutions or distributed inference across multiple GPUs to overcome the VRAM limitation.