Continuous Batching

Unlike static batching, which waits for every sequence in a batch to finish decoding before accepting new work, continuous batching inserts new requests into the GPU compute stream the moment a sequence in the current batch completes. This eliminates idle GPU Streaming Multiprocessors and is essential for serving DNA language models where prompt lengths can range from short promoter regions to entire chromosome arms.

• Eliminates head-of-line blocking caused by long sequences
• Achieves 2-10x throughput improvement over static batching
• Critical for variable-length genomic inputs like metagenomic reads

Continuous batching operates at the granularity of individual forward passes (iterations), not complete requests. The scheduler evaluates the state of all active sequences after every transformer layer execution and makes sub-millisecond decisions about which tokens to process next. For genomic foundation models using FlashAttention kernels, this fine-grained control prevents the memory fragmentation that plagues traditional serving systems.

• Scheduling decisions occur at microsecond latency
• Prevents KV-cache fragmentation in GPU memory
• Enables preemption of low-priority genomic inference tasks

A core innovation enabling continuous batching is intelligent key-value cache allocation. As sequences grow during autoregressive decoding, the system dynamically reserves and frees GPU memory blocks for attention states. This paged attention approach treats the KV-cache like virtual memory, eliminating the need to pre-allocate maximum-length buffers for every sequence—a critical optimization when serving genomic models that process sequences up to millions of base pairs.

• Uses block-level memory allocation to minimize fragmentation
• Enables memory sharing across related genomic queries
• Reduces GPU memory waste by up to 55% compared to static allocation

Continuous batching is particularly valuable for genomic inference workloads because biological sequences exhibit extreme length heterogeneity. A batch might simultaneously process a 20-base-pair transcription factor binding site query alongside a 100,000-base-pair structural variant analysis. Static batching would force the short query to wait for the long one, wasting compute. Continuous batching interleaves token generation so the short query completes and frees resources immediately.

• Handles 10bp to 1Mbp+ sequence length variance
• Prevents throughput collapse in mixed-length genomic workloads
• Essential for real-time clinical sequencing pipelines with SLAs

Advanced continuous batching implementations support request preemption, where a low-priority genomic analysis can be paused mid-generation, its KV-cache swapped to CPU memory, and a high-priority clinical diagnostic request inserted. Once the urgent request completes, the preempted sequence resumes from its exact interruption point. This is critical for hospital MLOps platforms where STAT variant calling must meet sub-minute turnaround times.

• Enables priority-based scheduling for clinical genomics
• Supports KV-cache offloading to CPU or NVMe for preempted requests
• Maintains deterministic latency SLAs for urgent diagnostics

A precursor to continuous batching where the server waits to form a fixed-size batch before processing. Unlike continuous batching, requests are grouped at the start and must complete together.

• Limitation: A single long sequence holds up the entire batch
• Use case: Suitable for workloads with uniform sequence lengths
• Contrast: Continuous batching evicts completed sequences and adds new ones mid-batch

An IO-aware exact attention algorithm that minimizes reads and writes to GPU high-bandwidth memory. Critical for serving long-sequence DNA models where attention computation is the bottleneck.

• Reduces memory footprint from O(N²) to O(N) in practice
• Enables processing of full-length chromosomes without truncation
• Pairs with continuous batching to maximize GPU utilization during attention computation

A compression technique that reduces model weights from FP32 or FP16 to INT8 or INT4 precision. Quantized models consume less memory, allowing larger batch sizes within the same GPU memory budget.

• INT8 quantization typically preserves >99% of genomic model accuracy
• Enables higher concurrency when combined with continuous batching
• Reduces memory bandwidth pressure during weight loading

The continuous monitoring process that identifies when a deployed genomic model's predictive performance degrades. While continuous batching optimizes throughput, drift detection ensures the outputs remain valid.

• Monitors data drift in incoming sequence distributions
• Triggers model retraining or rollback when performance thresholds are breached
• Essential for clinical genomics where stale models risk misclassification

A cross-platform inference accelerator that optimizes model execution graphs for diverse hardware targets. It provides graph-level optimizations that complement the batch-level optimizations of continuous batching.

• Fuses operations to reduce kernel launch overhead
• Supports execution on CPUs, GPUs, and edge accelerators
• Integrates with serving frameworks to accelerate individual sequence processing within each batch