# How can AI systems efficiently manage concurrent video, speech, LLM, and assessment workloads across shared cloud infrastructure while maintaining predictable latency, scalability, and reliability? AI applications are rapidly evolving from single-model systems into **complex, multimodal platforms**. A single user interaction can now involve video processing, speech recognition, large language models, contextual reasoning, and analytical engines—all operating simultaneously. For platforms such as **Presear**, where multiple AI interviews can run concurrently, this creates a fundamental architectural challenge: **how do we make different AI workloads coexist efficiently on shared cloud infrastructure without compromising performance or reliability?** The answer requires thinking beyond model deployment and designing an architecture around **workload orchestration**. ### 1\. Start With the Workload, Not the Infrastructure The first step is to understand that a single AI session is actually a collection of independent but interconnected workloads. An interview session, for example, can involve: * Real-time video and face analysis * Audio streaming and speech-to-text * LLM-based conversation and response generation * Personality and behavioural assessment * Context and session management Each workload has different computational characteristics and latency requirements. **Video processing is continuous. Speech processing is streaming. LLM inference is often burst-driven. Assessment workloads may be periodic or asynchronous.** Treating all of them as the same workload creates unnecessary resource contention. ![](https://cdn.hashnode.com/uploads/covers/5f217f2cb3e9823711b007c1/d27d453a-6cc3-41a6-a8f5-802a95bf6851.png align="center") ### 2\. Define the Session as the Fundamental Unit Instead of managing every AI request independently, the architecture should establish a **session as the logical boundary**. A session maintains the candidate's context, conversation state, processing state, and assessment information while individual AI services operate independently underneath it. This separation is important because the underlying compute resources can scale, move, or fail without necessarily affecting the user's session. The architecture therefore becomes: **Candidate Session → AI Workloads → Shared Compute → Results** rather than tightly coupling a session to a particular server or GPU. ### 3\. Introduce an AI Workload Orchestration Layer The next architectural layer is an **AI Workload Orchestrator**. Its role is to sit between the application and the underlying compute infrastructure and decide how workloads should be executed. The orchestrator can manage: * Workload scheduling * Priority management * Resource allocation * Model placement * SLA enforcement * Queue management * Scaling decisions This creates an abstraction between **what the application needs** and **where and how the AI workload actually runs**. Instead of applications directly managing GPUs, the orchestrator manages a shared pool of compute resources. ### 4\. Separate Workloads by Their Latency Requirements One of the most important architectural decisions is to classify workloads according to their real-time requirements. **Real-time workloads** These directly affect the user experience: * Face detection * Speech recognition * Interactive LLM responses * Live conversation processing **Asynchronous workloads** These can be processed independently: * Detailed behavioural analysis * Personality scoring * Historical analytics * Report generation This separation allows the architecture to protect real-time workloads from background processing. For example, a large assessment process should not consume resources required to keep an active interview responsive. ### 5\. Make Resource Allocation AI-Aware Traditional cloud infrastructure generally thinks in terms of: **CPU → Memory → Network → Instances** AI workloads require additional dimensions. The platform needs to understand: * Model size * GPU memory requirements * Context length * Token consumption * Inference duration * Batchability * Streaming requirements * Latency targets The orchestrator can then make resource decisions based on the **characteristics of the AI workload**, rather than simply the number of requests. This is particularly important when multiple models share the same GPU infrastructure. ### 6\. Manage LLM Context and Tokens LLM workloads introduce another architectural consideration: **context management**. During a long-running conversation, the system continuously accumulates: * Previous questions * Candidate responses * System instructions * Retrieved information * Tool outputs * Assessment context Sending all available information to the model on every interaction can increase latency, memory consumption, and cost. A robust architecture therefore needs a **context management layer** capable of deciding what information should remain active, what can be summarised, and what can be retrieved when required. This makes token management part of the broader infrastructure architecture rather than merely an API-level concern. ### 7\. Optimize GPU Utilisation GPUs are among the most expensive resources in an AI cloud environment. Simply allocating dedicated GPUs to every AI component can result in significant underutilisation. On the other hand, excessive sharing can create resource contention and unpredictable latency. The architecture therefore needs to balance: **GPU utilisation + workload isolation + latency + cost** This can involve intelligent model placement, workload batching, GPU partitioning, scheduling policies, and dynamic resource allocation. The objective is not simply to use fewer GPUs. It is to **extract the maximum useful AI workload from the available infrastructure while preserving performance guarantees**. * * * The key architectural shift is moving from: > **"How do we deploy an AI model?"** to: > **"How do we orchestrate multiple AI workloads efficiently across shared infrastructure?"** For a multimodal AI platform, the architecture ultimately looks like: **Candidate Sessions → Session Management → AI Workload Orchestration → Intelligent Scheduling → Shared Compute → AI Model Serving → Assessment & Results** This approach allows the infrastructure to evolve independently of individual AI models and applications. **Ready to see it in action?** [**Book a demo**](https://pspl.setmore.com/) with Presear Softwares to explore how AI Video Interviewer can help your organization.