The NVIDIA RTX 4060, with its 8GB of GDDR6 VRAM, falls significantly short of the 140GB VRAM requirement for running Llama 3.3 70B in FP16 precision. This enormous disparity means the entire model cannot be loaded onto the GPU at once. The RTX 4060's memory bandwidth of 0.27 TB/s, while decent for its class, would be a bottleneck even if sufficient VRAM were available, as the model's weights and activations would need to be constantly swapped between system RAM and GPU memory, drastically reducing inference speed. With only 3072 CUDA cores and 96 Tensor Cores, the RTX 4060 lacks the computational power to efficiently process such a large model, further exacerbating performance issues.
Even with aggressive quantization techniques, fitting Llama 3.3 70B onto an 8GB card is highly improbable without severely impacting performance. The model's large context length of 128000 tokens adds further strain on memory resources. The lack of sufficient VRAM not only prevents the model from running efficiently but also makes it impossible to determine meaningful estimates for tokens per second or batch size on this configuration. Any attempt to run the model directly on the RTX 4060 would likely result in out-of-memory errors or extremely slow processing speeds, rendering it impractical for real-world applications.
Due to the substantial VRAM deficit, running Llama 3.3 70B directly on an RTX 4060 is not feasible. Consider exploring cloud-based solutions like Google Colab Pro, AWS SageMaker, or similar platforms that offer access to GPUs with significantly more VRAM (e.g., A100, H100). Alternatively, investigate distributed inference solutions that split the model across multiple GPUs, although this approach requires considerable technical expertise and specialized hardware.
If using the RTX 4060 is unavoidable, you might experiment with extreme quantization methods (e.g., 4-bit or even lower) combined with CPU offloading, but expect a substantial performance degradation. Even then, success is not guaranteed. A more practical approach for local experimentation might be to use a smaller model, such as Llama 3 8B or a similar-sized model, which can be quantized to fit within the RTX 4060's VRAM.