NVIDIA has introduced encode-prefill-decode (EPD) disaggregation for its Dynamo inference framework to separate vision encoding from LLM processing. This architectural approach aims to reduce time-to-first-token and improve throughput for multimodal workloads by allowing independent scaling of encoder and inference workers.
This architectural shift directly addresses the compute imbalance inherent in multimodal models where vision encoding often stalls LLM prefill. By decoupling these stages, streaming platforms can scale encoder and inference workers independently, preventing media-heavy requests from degrading the experience for text-only users. As the industry moves toward more complex video-to-text applications, this optimization becomes critical for maintaining low-latency interactions on heterogeneous GPU clusters. The ecosystem impact is most pronounced for quantized and MoE models where the vision encoder represents a larger share of the total compute budget. Watch for vLLM and SGLang to integrate similar disaggregation stacks to match these performance benchmarks.
NVIDIA's disaggregated inference architecture is gaining momentum beyond single-model serving, with the company positioning Dynamo as a foundational layer for multi-stage AI pipelines across cloud and telecom environments. At DTW Ignite 2026 in Copenhagen, Nokia announced it would run its Autonomous Network Fabric on AWS from later this year, using intent-based networking and agentic AI to deliver observability, analytics, and automation across radio, core, transport, and service domains. The fabric relies on domain models and digital twins that consume data, apply models, and trigger actions, a pattern that mirrors the disaggregated compute philosophy NVIDIA is now formalizing in Dynamo's EPD architecture. Nokia reported operators achieving automation rates above 90 percent and service delivery times of four hours or less using these stacked components. The competitive landscape around GPU-accelerated inference is intensifying as vendors diverge on architectural choices. Ericsson and Nokia are now taking fundamentally different approaches to AI-RAN workloads on GPU hardware, with Nokia designing its entire Layer 1 RAN to run on NVIDIA's CUDA platform and GPUs while Ericsson restricts GPU usage to forward error correction functions only. This split reflects a broader industry debate about how much of the compute stack should be disaggregated onto specialized accelerators versus kept on general-purpose CPUs. NVIDIA's $1 billion investment in Nokia underscores the chipmaker's strategy of embedding its CUDA ecosystem deep into telecom infrastructure, creating demand for the same GPU scheduling and disaggregation techniques that Dynamo's EPD approach formalizes for multimodal inference. On the agentic AI front, Nokia's partnership with Google Cloud demonstrates how disaggregated agent architectures are being deployed in production telecom operations. Nokia and Google Cloud built six specialized Gemini-powered agents for network troubleshooting at DTW Ignite 2026, including a router agent for orchestration, an event triage agent for alarm analysis, and an anomaly reasoner that separates real issues from false alarms. Nokia claims these agents can reduce network problem-solving times by 50 to 80 percent. The company plans to launch the agentic platform in Google Cloud Marketplace in September 2026, with additional agents for topology, services design, and security in its pipeline. This multi-agent decomposition of network operations parallels the encode-prefill-decode separation in Dynamo, where independent specialized workers handle distinct stages of a pipeline rather than a single monolithic process handling everything sequentially. As autonomous AI agents become more prevalent, the need for such efficient, disaggregated compute architectures will only grow.
NVIDIA Dynamo has introduced EPD disaggregation, which separates vision encoding from LLM processing to optimize multimodal AI workloads. This architectural shift eliminates head-of-line blocking, delivering up to a 7x improvement in end-to-end response times. It allows streaming platforms to scale encoder and inference workers independently, ensuring low-latency performance for complex AI applications.
EPD disaggregation is an architectural shift that separates vision encoding from LLM processing. By decoupling these stages, the system prevents vision encoding from stalling LLM prefill, allowing for independent scaling of encoder and inference workers.
EPD disaggregation achieves up to 7x faster end-to-end response times for image-heavy workloads with short-to-medium outputs. Additionally, it reduces text-request time-to-first-token (TTFT) by 42.2% and image-request TTFT by 30.8%.
The architecture supports three primary topologies: aggregated serving, colocated workers on shared GPUs, and disaggregated workers on heterogeneous hardware, such as the RTX 6000D and GB200.
Quantizing LLM weights to NVFP4 while maintaining BF16 for vision encoders increases goodput gains from 1.78x to 2.64x, significantly improving efficiency for multimodal models.
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