EPD disaggregation can hit 5x speedups for image-heavy multimodal workloads.

The main architectural challenge for multimodal large language models (MLLMs) often comes from the sharply different computational demands of handling visual data and producing text. In conventional unified pipelines, the vision encoder and the language model's prefill and decode stages compete for the same resources, frequently creating severe efficiency bottlenecks. EPD disaggregation separates the vision encoder from prefill and decode, preventing vision encoding from holding back the LLM's KV cache generation and unlocking substantial performance gains.

Understanding why this matters for multimodal models requires examining the data flow. Image tokens are processed once during the initial encoding phase, while text generation proceeds iteratively. When these stages are coupled, the computational intensity of the vision encoder can stall the pipeline, delaying KV cache generation and reducing both time-to-first-token performance and subsequent throughput. Separating the components allows the system to allocate hardware more precisely for each task, ensuring that vision encoding does not obstruct smooth text generation.

The benefits of this optimization are strongest in particular deployment scenarios. Gains are greatest when running quantized MoE (Mixture-of-Experts) models with high image-to-text ratios and short responses. In these situations, processing large images accounts for much of the total latency, while the resulting text output is minimal. Without disaggregation, the system spends valuable compute cycles waiting for the vision encoder before iterative decoding can begin. Decoupling the stages allows these processes to overlap more effectively or lets vision encoding hardware scale independently of decoding hardware.

When quantized MoE architectures operate alongside a high volume of visual data relative to output length, the efficiency gains become dramatic. Once the vision encoder no longer bottlenecks KV cache generation, the system can achieve throughput improvements far beyond incremental gains. In these optimized environments, EPD disaggregation can hit 5x speedups for image-heavy multimodal workloads.

A disaggregated architecture also enables more granular resource management. Rather than operating the multimodal pipeline as one monolithic block, treating the vision encoder separately from prefill and decode allows the LLM to keep its KV cache highly utilized. Iterative text generation can then proceed without being slowed by the one-time computational burden of image processing, producing the massive speedups observed in high-image-ratio workloads.