The primary limiting factor for running Llama 3.3 70B on an NVIDIA RTX 3070 is the VRAM. Llama 3.3 70B in FP16 (half-precision floating point) requires approximately 140GB of VRAM to load the entire model. The RTX 3070 only has 8GB of VRAM. This means the model cannot be loaded in its entirety onto the GPU, leading to a significant shortfall of 132GB. While techniques like CPU offloading and quantization can help, they come with significant performance penalties.
Even if aggressive quantization is applied, the 8GB VRAM of the RTX 3070 is insufficient to hold the entire model and the necessary working memory for inference. Memory bandwidth, while decent at 0.45 TB/s, becomes a bottleneck when data needs to be constantly swapped between the GPU and system RAM due to insufficient VRAM. The Ampere architecture of the RTX 3070, with its CUDA and Tensor Cores, is capable of accelerating matrix multiplications, but these capabilities are severely hampered by the VRAM constraint. Without enough VRAM, the model will likely be unusable for interactive inference.
Due to the severe VRAM limitations, running Llama 3.3 70B directly on an RTX 3070 is not feasible for practical use. Consider using cloud-based inference services or platforms that offer access to GPUs with significantly larger VRAM capacities, such as those found in NVIDIA A100, H100, or similar data center GPUs. Alternatively, explore using a much smaller model that fits within the RTX 3070's VRAM, or consider using a CPU-based inference approach, understanding that it will be significantly slower.
If you still want to experiment locally, focus on extreme quantization (e.g., 4-bit or even lower) and offloading layers to system RAM. Be prepared for extremely slow inference speeds, potentially several minutes per response. Using a framework like `llama.cpp` with appropriate quantization settings is crucial for this scenario. However, even with these optimizations, the performance will likely be unsatisfactory for most use cases.