The primary limiting factor in running large language models (LLMs) like Llama 3.3 70B is the GPU's VRAM capacity. Llama 3.3 70B, in its FP16 (half-precision floating-point) format, requires approximately 140GB of VRAM to load the model weights. The NVIDIA RTX 4070, equipped with 12GB of GDDR6X memory, falls significantly short of this requirement. This means the entire model cannot be loaded onto the GPU for inference, resulting in a compatibility failure. The 4070's memory bandwidth of 0.5 TB/s is sufficient for smaller models, but becomes a bottleneck when dealing with workarounds involving offloading layers to system RAM, as data transfer speeds between system RAM and the GPU are much slower.
Even if techniques like CPU offloading or NVMe swapping are employed, the performance will be severely degraded. The limited VRAM forces frequent data transfers between the system memory and the GPU, which drastically reduces the inference speed. Furthermore, the 4070's 5888 CUDA cores and 184 Tensor cores would be underutilized due to the constant data transfer bottleneck. The Ada Lovelace architecture is capable, but starved by VRAM constraints. Batch size and context length would be severely limited, further impacting performance and usability.
Due to the substantial VRAM disparity, running Llama 3.3 70B directly on an RTX 4070 is impractical without significant compromises. Consider using a smaller model variant, such as a 7B or 13B parameter model, which can fit within the 12GB VRAM. Quantization is absolutely necessary to get a larger model running, however the 70B model will still likely be too large. Alternatively, explore cloud-based solutions or services offering GPU resources with sufficient VRAM (e.g., NVIDIA A100, H100) for running the 70B model. For local experimentation, consider a multi-GPU setup, though this requires specialized software and configuration and is not always supported by all inference frameworks.
If using the 4070 is unavoidable, focus on extreme quantization methods (4-bit or even 2-bit) and CPU offloading via llama.cpp, but expect very slow inference speeds. Be prepared to experiment with small batch sizes and significantly reduced context lengths. Real-time or interactive performance is unlikely. You may also consider using a model distillation technique to create a smaller, more manageable model that retains the core capabilities of the larger Llama 3.3 70B model, though this requires significant expertise and training data.