Researchers have introduced TurboVLA, a novel Vision-Language-Action (VLA) paradigm designed to overcome the high computational and memory overhead inherent in traditional LLM-centric $V \to L \to A$ architectures. Unlike conventional models that project visual observations into a Large Language Model's representation space, TurboVLA adopts a direct $V + L \to A$ mapping. This approach utilizes independent encoders for visual observations and language instructions, facilitating information exchange through lightweight bidirectional vision-language interaction, followed by continuous action chunk prediction via a compact decoder. This streamlined architecture significantly reduces the resource requirements for policy invocation. When evaluated on the LIBERO benchmark, the 0.2B parameter TurboVLA achieved a 97.7% average success rate, matching or exceeding the performance of much larger VLA policies. Remarkably, the model operates with an inference latency of just 31.2 ms (approximately 32 Hz) and requires less than 1 GB of VRAM (specifically 0.9 GB) when running on a consumer-grade NVIDIA RTX 4090 GPU. By bypassing the heavy LLM-centric bottleneck, TurboVLA provides a highly efficient alternative for real-time robotic manipulation tasks on edge-accessible hardware.