The primary limiting factor for running large language models (LLMs) like LLaVA 1.6 34B is VRAM. This model, in FP16 (half-precision floating point) format, requires approximately 68GB of VRAM to load the model weights and perform computations. The NVIDIA RTX 4070, while a capable card, only offers 12GB of VRAM. This creates a significant shortfall of 56GB, preventing the model from being loaded and executed directly. While the RTX 4070's memory bandwidth of 0.5 TB/s and its Ada Lovelace architecture contribute to efficient data transfer and computation, they cannot compensate for the lack of sufficient VRAM to hold the model.
Furthermore, the number of CUDA and Tensor cores influences computational throughput. The RTX 4070's 5888 CUDA cores and 184 Tensor cores are adequate for smaller models, but the sheer size of LLaVA 1.6 34B means that even with these cores, performance would be severely limited if the model could somehow fit into the available VRAM. Without enough VRAM, the system would likely rely on swapping data between the GPU and system RAM, resulting in drastically reduced inference speeds, rendering the model practically unusable in real-time applications. The model would not be able to load, thus the estimated tokens/sec and batch size are listed as 'None'.
Due to the substantial VRAM deficit, running LLaVA 1.6 34B on an RTX 4070 is not feasible without significant modifications. The most practical approach is to explore model quantization techniques. Quantization reduces the memory footprint of the model by using lower-precision numerical formats (e.g., 4-bit or 8-bit integers) instead of FP16. However, even with aggressive quantization, fitting the entire model into 12GB of VRAM may be challenging, and some performance degradation is to be expected.
Alternatively, consider using cloud-based inference services or platforms that offer access to GPUs with larger VRAM capacities, such as NVIDIA A100 or H100. Another option is to use CPU offloading, but this will result in very slow performance. If running locally is a must, explore smaller models or fine-tuned versions of LLaVA that are designed to run on consumer-grade hardware with limited VRAM. Distributed inference across multiple GPUs is also a possibility, but it requires significant technical expertise and infrastructure setup.