The primary bottleneck in running Llama 3.3 70B on an RTX 4070 SUPER is the VRAM limitation. Llama 3.3 70B in FP16 precision requires approximately 140GB of VRAM to load the model weights entirely onto the GPU. The RTX 4070 SUPER, equipped with 12GB of GDDR6X, falls significantly short of this requirement. This discrepancy prevents the model from being loaded and processed directly on the GPU without employing techniques like quantization or offloading.
Memory bandwidth also plays a role, though it's secondary to the VRAM constraint. The RTX 4070 SUPER offers 0.5 TB/s of memory bandwidth, which is adequate for smaller models. However, with extensive offloading or extremely aggressive quantization, the limited bandwidth could become a performance bottleneck, as data needs to be constantly transferred between the system RAM and the GPU. CUDA cores and Tensor cores, while important for computational throughput, cannot compensate for the fundamental lack of sufficient VRAM to house the model.
Given the VRAM limitation, running Llama 3.3 70B directly on the RTX 4070 SUPER is impractical without significant compromises. Consider using aggressive quantization techniques like 4-bit or even 3-bit quantization (using libraries like `llama.cpp` with `q4_K_S` or smaller). This will reduce the VRAM footprint, potentially making the model fit, albeit with a loss in accuracy. Offloading layers to system RAM is another option, but it will drastically reduce inference speed due to the slower transfer rates.
Alternatively, explore cloud-based GPU solutions with sufficient VRAM or consider using a distributed inference setup across multiple GPUs if feasible. If high precision isn't crucial, experiment with FP8 or INT8 precision, but be mindful of the potential impact on model accuracy. For local experimentation, consider smaller Llama 3 models or other models that fit within the 12GB VRAM limit of the RTX 4070 SUPER.