The NVIDIA RTX 4060, equipped with 8GB of GDDR6 VRAM, falls short of the 24GB VRAM demanded by the FLUX.1 Schnell model when running in FP16 (half-precision floating point). This 16GB VRAM deficit prevents the model from loading and operating effectively. The RTX 4060's memory bandwidth of 0.27 TB/s, while decent for its class, would likely become a bottleneck even if VRAM capacity were sufficient, as the model would be constantly swapping data between the GPU and system memory. The Ada Lovelace architecture's Tensor Cores would accelerate certain operations, but the primary limitation remains the insufficient VRAM, rendering performance optimization strategies largely ineffective in a direct, unoptimized implementation.
Due to the significant VRAM shortfall, directly running FLUX.1 Schnell on the RTX 4060 in FP16 is not feasible. Consider employing quantization techniques like Q4_K_M or even lower to reduce the model's memory footprint. Utilize inference frameworks such as llama.cpp, which are designed to handle large models on limited hardware through CPU offloading and memory management strategies. Be prepared for significantly reduced inference speeds and severely limited batch sizes. If possible, explore using cloud-based GPU resources or consider a GPU with more VRAM, like an RTX 3090 or an A4000.