The primary limiting factor for running Llama 3.3 70B on an NVIDIA RTX 3080 12GB is the VRAM. Llama 3.3 70B in FP16 (half-precision floating point) requires approximately 140GB of VRAM to load the model weights and manage activations during inference. The RTX 3080 12GB only provides 12GB of VRAM, resulting in a significant deficit. This means the model, in its full FP16 precision, cannot fit entirely within the GPU's memory, leading to a 'FAIL' verdict.
While the RTX 3080's memory bandwidth of 0.91 TB/s and its 8960 CUDA cores are substantial, they are rendered less effective when the model exceeds VRAM capacity. Attempting to run the model without sufficient VRAM will result in out-of-memory errors or extremely slow performance due to constant data swapping between the GPU and system RAM. The Ampere architecture and Tensor Cores would normally accelerate matrix multiplications, but VRAM limitations severely bottleneck the process.
Without sufficient VRAM, estimating tokens per second or batch size is impractical. Any attempt to run the model without addressing the VRAM issue will likely result in unusable performance. The model's large context length of 128000 tokens is also irrelevant, as the model won't even load in its entirety.
To run Llama 3.3 70B on an RTX 3080 12GB, you must significantly reduce the model's memory footprint. The most effective method is quantization, specifically using techniques like 4-bit or even 3-bit quantization. This will compress the model weights, drastically lowering the VRAM requirement. Tools like `llama.cpp` and frameworks such as vLLM are designed to handle quantized models efficiently. Be aware that aggressive quantization will affect the model's accuracy and coherence, so experiment with different levels to find a balance between performance and quality.
Consider offloading some layers to system RAM if quantization alone isn't sufficient. However, this will drastically reduce inference speed. Using a framework that supports multi-GPU inference (if you have access to additional GPUs) could also be an option, but this is generally more complex to set up. If acceptable performance cannot be achieved even with quantization and offloading, consider using a smaller model or cloud-based inference services.