The NVIDIA RTX 4060 Ti 16GB, with its 16GB of GDDR6 VRAM, falls significantly short of the 140GB VRAM required to load and run the Llama 3.3 70B model in FP16 precision. This discrepancy stems from the sheer size of the model, which has 70 billion parameters. Each parameter in FP16 (half-precision floating-point) format requires 2 bytes of memory. While the RTX 4060 Ti boasts a respectable 4352 CUDA cores and 136 Tensor cores, enabling it to perform AI computations, the insufficient VRAM becomes the primary bottleneck, preventing the model from even being loaded. The memory bandwidth of 0.29 TB/s, while adequate for many tasks, is irrelevant in this scenario as the model cannot fit into the available memory.
Even with optimizations like offloading layers to system RAM, the performance would be severely impacted due to the slow transfer speeds between the GPU and system memory. The Ada Lovelace architecture provides advantages like increased efficiency and support for newer features, but these are rendered useless when the model exceeds the GPU's memory capacity. The estimated tokens per second and batch size are therefore unavailable, as the model cannot be executed without significant adjustments.
Due to the large VRAM requirement of Llama 3.3 70B, running it directly on an RTX 4060 Ti 16GB is not feasible without substantial modifications. Consider using quantization techniques like 4-bit or 8-bit quantization (using libraries like `llama.cpp` or `AutoGPTQ`) to significantly reduce the model's memory footprint. Another option is to explore cloud-based GPU services or renting a more powerful GPU with sufficient VRAM (e.g., an NVIDIA A100 or H100 with 80GB VRAM). If local execution is a must, explore model parallelism, which distributes the model across multiple GPUs, although this requires more complex setup and code modifications.
Alternatively, explore smaller models, such as Llama 3 8B or other models with fewer parameters, which can run comfortably on the RTX 4060 Ti 16GB. Carefully consider the trade-off between model size and performance. If you are committed to running Llama 3.3 70B locally, investigate CPU offloading and page-locked memory techniques, understanding that performance will be significantly reduced.