The primary limiting factor for running Llama 3.3 70B on an NVIDIA RTX 4000 Ada is the VRAM. Llama 3.3 70B, in its FP16 (half-precision floating point) format, requires approximately 140GB of VRAM to load the entire model. The RTX 4000 Ada only provides 20GB of VRAM. This creates a substantial VRAM deficit of 120GB, making it impossible to load the model in its native FP16 precision. While the RTX 4000 Ada's Ada Lovelace architecture, 6144 CUDA cores, and 192 Tensor cores are beneficial for AI inference, the insufficient VRAM is a hard constraint.
Even with techniques like offloading layers to system RAM, the performance would be severely degraded due to the relatively slow transfer speeds between the GPU and system memory. The RTX 4000 Ada's memory bandwidth of 0.36 TB/s, while decent, is not sufficient to compensate for the constant data swapping that would be required. Consequently, generating text with Llama 3.3 70B on this setup without significant modifications is not feasible. The expected tokens per second and batch size would be negligible, rendering the model practically unusable for real-time or interactive applications.
Due to the significant VRAM limitation, directly running Llama 3.3 70B in FP16 on the RTX 4000 Ada is not recommended. Consider using quantization techniques to reduce the model's memory footprint. Quantization to 4-bit (Q4) or 8-bit (Q8) precision can drastically decrease the VRAM requirement, potentially making the model runnable, although with a slight reduction in accuracy.
Alternatively, explore cloud-based inference services or consider using a more powerful GPU with significantly more VRAM, such as an NVIDIA RTX 6000 Ada Generation or an A100. If using quantization, tools like `llama.cpp` are highly recommended for their efficient implementation and support for various quantization methods. Experiment with different quantization levels to find a balance between VRAM usage and performance.