The NVIDIA RTX 4070 SUPER, with its 12GB of GDDR6X VRAM, falls significantly short of the 68GB VRAM required to load LLaVA 1.6 34B in FP16 precision. This memory shortfall is the primary bottleneck, preventing the model from being loaded onto the GPU for inference. While the RTX 4070 SUPER boasts a respectable 0.5 TB/s memory bandwidth and 7168 CUDA cores based on the Ada Lovelace architecture, these specifications become irrelevant when the model's memory footprint exceeds the available VRAM. The 224 Tensor Cores, designed to accelerate matrix multiplications crucial for deep learning, also cannot be effectively utilized in this scenario.
Due to the substantial VRAM deficit, running LLaVA 1.6 34B directly on the RTX 4070 SUPER without significant modifications is not feasible. The model's 34 billion parameters necessitate a large memory footprint, and FP16 precision, while offering a good balance between speed and accuracy, still demands considerable VRAM. The context length of 4096 tokens further contributes to the memory requirements, as larger context windows necessitate storing more data during inference. Without sufficient VRAM, the system will likely encounter out-of-memory errors, preventing successful model execution.
To run LLaVA 1.6 34B, you'll need to significantly reduce the model's memory footprint. Quantization is crucial. Consider using 4-bit quantization (Q4) via `llama.cpp` or similar frameworks. This reduces the VRAM requirement considerably. Alternatively, offload some layers to system RAM, but expect a significant performance decrease. If neither of these is sufficient, consider using a cloud-based GPU with sufficient VRAM or explore smaller models like LLaVA 1.5 7B, which would be compatible with the 4070 SUPER.
If you opt for quantization and/or CPU offloading, use `llama.cpp` with appropriate flags or `text-generation-inference` for optimized inference. Monitor VRAM usage closely to ensure you are not exceeding the 12GB limit. Experiment with different batch sizes to find a balance between throughput and latency. If performance is still unsatisfactory, consider distributing the model across multiple GPUs, though this requires more advanced setup.