Dynamic Batching (Edge Inference)

This is the core mechanism of dynamic batching. Instead of padding all sequences in a batch to the length of the longest one—which wastes compute on padding tokens—the system packs sequences of different lengths together into a contiguous memory block. This is often visualized as a ragged tensor or managed via specialized kernels that track sequence boundaries, ensuring the model's attention mechanism only operates on real tokens. This maximizes the effective tokens per second processed by the GPU or NPU.

An edge dynamic batcher maintains a pending request queue. It does not wait for a fixed batch size or a fixed time window. Instead, it uses a configurable latency budget (e.g., < 100ms p95) and a maximum batch size (constrained by device memory). The scheduler continuously evaluates the queue, batching together requests that have arrived, balancing the trade-off between inference latency and hardware utilization. This is crucial for interactive edge applications where request patterns are bursty and unpredictable.

On edge devices with limited VRAM or SRAM, memory is the primary constraint. The batcher must account for:

• Model Weights: The footprint of the loaded model.
• KV Cache: The growing memory of the Key-Value cache for each sequence in a batch during autoregressive generation.
• Activation Memory: Intermediate tensors during the forward pass.

The scheduler estimates the peak memory consumption of a candidate batch and rejects configurations that would cause out-of-memory (OOM) errors, often preferring smaller, more frequent batches over large, memory-exhausting ones.

Also known as rolling batching, this advanced form is essential for text generation (LLMs) in edge RAG. In a standard batch, all sequences must finish generation together, forcing fast sequences to wait for slow ones. With continuous batching:

• New requests are inserted into the batch as soon as previous requests finish and free up space.
• The system manages a complex state for each sequence (its position, KV cache).
• This leads to near-100% GPU/NPU utilization during generation, dramatically improving throughput for conversational or streaming edge AI applications.

The batcher exposes key knobs that system operators tune for their specific edge deployment:

• Max Batch Size: Larger batches increase throughput (tokens/sec) but also increase latency for the first request in the batch.
• Batch Timeout: The maximum time to wait for new requests before executing the current batch. A shorter timeout favors latency; a longer timeout favors throughput.
• Preferred Batch Size: A target size that the scheduler aims for, optimizing for the hardware's most efficient operating point. On edge devices, this is often small (e.g., 2, 4, or 8) due to memory limits.

SambaNova provides a full-stack solution, including specialized Reconfigurable Dataflow Units (RDUs) and the SambaFlow software suite. Its architecture is designed for sequential batching, a form of dynamic batching where new tokens are generated for multiple sequences in parallel within a single batch. This is ideal for edge inference of decoder-only models common in RAG, as it maintains high compute utilization even during the sequential nature of text generation, significantly improving tokens/sec/watt.

Also known as iteration-level or rolling batching, this is an advanced form of dynamic batching where new inference requests are added to a running batch as soon as individual sequences within the batch finish generation. This eliminates idle padding and maximizes hardware utilization.

• Key Mechanism: Manages a pool of active requests, dynamically scheduling compute for sequences of different lengths.
• Edge Benefit: Dramatically improves throughput for variable-length queries common in interactive edge applications like chatbots or RAG systems.
• Contrast with Static Batching: Unlike static batching which waits for a full batch, continuous batching provides lower latency and higher efficiency.

A memory management algorithm for the attention mechanism's key-value (KV) cache that stores cache blocks in non-contiguous, paged memory. It is foundational for efficient dynamic batching with long contexts.

• Solves Fragmentation: Allows flexible sharing of physical memory between different sequences in a batch, drastically reducing waste.
• Enables Longer Contexts: Makes it feasible to batch requests with varying context lengths on memory-constrained edge hardware.
• Implementation: Popularized by the vLLM inference engine, it is a key enabler for high-performance, batched LLM serving.

A parallel execution strategy that splits a neural network or a multi-stage pipeline (like RAG) across multiple hardware stages. It is often used in conjunction with batching to improve overall system throughput.

• How it Works: Different layers of a model or different components (retriever, reranker, generator) process different micro-batches concurrently.
• Edge Application: On heterogeneous edge systems, pipelining can overlap retrieval compute with generation compute, hiding latency.
• Scheduling Challenge: Requires careful orchestration to balance stages and avoid bottlenecks, which dynamic batching can help manage.

A dynamic resource management strategy where computationally intensive parts of an inference workload are selectively executed on more powerful neighboring hardware (e.g., a local edge server), while lighter tasks remain on the end device.

• Relation to Batching: Dynamic batching decisions may factor in offloading. For example, very large batches might be processed on a nearby server, while small, latency-critical batches stay on-device.
• Use Case: In a RAG system, retrieval might run on-device, while the LLM generation for a batched set of queries is offloaded.
• Goal: Balances latency, privacy, bandwidth, and battery life constraints in distributed edge environments.

The fundamental engineering trade-off managed by dynamic batching. Batching improves throughput (queries processed per second) by amortizing fixed overhead, but can increase latency (time per query) as requests wait to form a batch.

• Dynamic Batching's Role: Aims to optimize this trade-off in real-time by adjusting batch size and composition based on incoming traffic.
• Edge Consideration: Target latency Service Level Agreements (SLAs) are often stricter on edge devices serving interactive users, requiring sophisticated batching schedulers.
• Tuning Parameters: Maximum batch size, waiting timeout, and sequence length awareness are critical knobs for balancing this trade-off.