Each test is 5 questions with varying difficulty.
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Batch Inference APIs represent a critical paradigm in modern AI infrastructure, moving away from low-latency, request-response cycles toward high-throughput, asynchronous processing. In 2026, as organizations scale LLM and vision model deployments, the ability to process millions of tokens or images in a single job is essential for cost efficiency. These APIs are primarily used for offline data processing, synthetic data generation, and large-scale document analysis. Roles such as ML Engineers, MLOps Engineers, and Backend Systems Engineers are frequently tested on their ability to design robust batch pipelines. Junior candidates are expected to understand the basic request-polling lifecycle, while senior candidates must demonstrate expertise in job orchestration, handling partial failures, managing GPU memory across batch boundaries, and implementing cost-saving strategies like spot instance utilization and throughput-optimized scheduling.
Batch Inference APIs are the primary mechanism for controlling cloud AI spend. By decoupling inference from user-facing latency requirements, engineers can utilize cheaper, preemptible compute resources and maximize GPU utilization through optimal batch sizing. In production, companies like OpenAI and Anthropic utilize massive batch pipelines for model training evaluation and large-scale data processing. From an interview perspective, this topic is high-signal because it reveals whether a candidate understands the trade-offs between system throughput and resource availability. A strong answer demonstrates knowledge of how to handle long-running processes, state management, and the specific failure modes of distributed inference. In 2026, as models grow in size, the ability to optimize for 'tokens per dollar' rather than just 'milliseconds per request' is a defining skill for senior AI infrastructure roles.
The architecture of a batch inference system revolves around a job submission gateway that decouples the client from the inference engine. The system must manage job state, data ingestion from object storage, and the distribution of tasks to GPU-accelerated workers. The execution pipeline typically involves a controller that monitors worker health and job progress, ensuring that failures are retried and results are persisted to a durable store.
The client submits a job manifest to the API. The Orchestrator validates the request and queues tasks. Workers pull tasks, fetch data from Object Storage, perform inference, and write outputs back to storage. The State Store tracks progress for fault tolerance.
[Client]
↓
[API Gateway]
↓
[Task Queue] → [Orchestrator]
↓ ↓
[Object Storage] ← [Inference Workers]
↓ ↓
[Result Store] ← [State Database]Periodically saving the index of processed records in a persistent store to allow resuming from the last successful point after a failure.
Trade-offs: Increases I/O overhead but provides significant resilience against node preemption.
Decoupling data ingestion from model execution using a buffer, allowing the inference worker to pull optimal batch sizes regardless of arrival rate.
Trade-offs: Adds system complexity and potential latency for individual items.
Using a separate process or container to pre-fetch and cache input data locally on the worker node while the GPU processes the current batch.
Trade-offs: Reduces GPU idle time but increases memory footprint per node.
| Reliability | Use dead-letter queues for failed tasks and implement exponential backoff for retries. |
| Scalability | Scale worker pools dynamically based on the number of pending tasks in the queue. |
| Performance | Maximize GPU utilization by tuning batch size and using pinned memory for data transfers. |
| Cost | Utilize spot instances for non-critical batch jobs and implement aggressive resource cleanup. |
| Security | Use IAM roles for fine-grained access to object storage and encrypt data at rest. |
| Monitoring | Track throughput (items/sec), GPU utilization, and queue depth metrics. |
Real-time inference APIs prioritize low latency, typically processing one request at a time with strict response time SLAs. Batch inference APIs prioritize throughput and cost efficiency, processing large volumes of data asynchronously, often using cheaper compute resources and larger batch sizes to maximize hardware utilization.
Standard REST APIs are designed for synchronous request-response cycles. They often have connection timeouts, limited payload sizes, and lack the state management required for long-running batch jobs. Batch APIs use polling or webhooks to handle the asynchronous nature of large-scale processing.
The optimal batch size is determined by the model architecture, GPU memory capacity, and latency requirements. You should profile your model with varying batch sizes to find the 'sweet spot' where throughput is maximized without exceeding GPU memory limits or causing excessive latency.
Spot instances are significantly cheaper than on-demand instances. Since batch inference is asynchronous and non-realtime, it can tolerate the interruptions associated with spot instances, provided you implement robust checkpointing and job resumption logic.
Standard batching waits for a fixed number of requests before triggering inference. Continuous batching allows new requests to be added to the batch as soon as others finish, significantly reducing idle GPU time and improving overall system throughput.
A manifest file acts as a pointer to the input data stored in object storage. Instead of sending raw data, the client sends a manifest containing URIs. This allows the inference system to process datasets that are much larger than the memory capacity of the API gateway.
Implement a task-level retry mechanism. If a specific item in a batch fails, the worker should log the error, move the task to a dead-letter queue, and continue processing the rest of the batch. The overall job status should reflect the number of successful vs failed items.
GPU memory fragmentation occurs when memory is allocated and freed in non-contiguous blocks. Over time, this can prevent the system from allocating large contiguous blocks needed for large tensors, leading to out-of-memory errors even when total free memory appears sufficient.
Model warm-up involves running dummy inference requests before the service starts accepting live traffic. This ensures that the model is loaded into GPU memory, CUDA kernels are compiled, and caches are primed, preventing latency spikes during the first few requests.
Job-level retries restart the entire batch job from the beginning, which is inefficient. Task-level retries target only the specific records that failed, using a persistent state store to track progress, which is significantly more efficient for large-scale datasets.
AI Prep covers AI Agents, Generative AI, ML Fundamentals, NLP & LLMs and a lot more, with adaptive tests and daily challenges. Fully offline on Android. Free to try, one-time unlock for lifetime access.