The Architecture of Efficiency: A Comprehensive Analysis of Continuous Batching in Large Language Model Inference

1. The Inference Efficiency Paradox: Deterministic Hardware in a Stochastic Age

The ascendancy of Large Language Models (LLMs) has precipitated a fundamental crisis in the architectural design of machine learning inference systems. For the better part of a decade, the optimization of deep learning workloads was predicated on the assumption of static, predictable tensor shapes. Convolutional Neural Networks (CNNs) and earlier Transformer architectures like BERT processed inputs in a holistic, bidirectional manner where the computational graph was immutable and the execution time was deterministic. In this regime, efficiency was a function of massive parallelism: batching inputs to saturate the arithmetic logic units (ALUs) of a GPU was a trivial matter of matrix concatenation.

The emergence of autoregressive generative models, however, introduced a stochastic element that shattered these paradigms. The generation of text is inherently sequential and variable; the production of token $t$ is causally dependent on tokens $0$ to $t-1$, and the termination condition—the End of Sequence (EOS) token—is determined dynamically by the model itself.1 This introduces a workload profile characterized by extreme variance in request duration. One user query might necessitate the generation of a brief, ten-token acknowledgement, while a concurrent request might demand a comprehensive four-thousand-token analytical essay.

When traditional “static batching” strategies—which group requests and process them in lockstep—are applied to this workload, the system falls victim to the “straggler problem.” The entire batch is held hostage by the longest-running sequence, forcing the GPU to perform redundant computations on completed sequences (padding) or simply idle its compute cores while waiting for the final token to be generated.2 This inefficiency is not merely a matter of latency; it represents a catastrophic underutilization of high-bandwidth memory (HBM) and compute capacity, rendering the economic cost of serving LLMs prohibitive at scale.

Continuous batching—variously referred to as iteration-level scheduling, in-flight batching, or dynamic batching—emerged as the definitive architectural response to this paradox. By decomposing the atomic unit of scheduling from the request to the iteration, continuous batching allows inference engines to manage the GPU as a fluid stream of tokens rather than a rigid processor of batches.1 This report provides an exhaustive examination of the theoretical underpinnings, algorithmic mechanics, memory management innovations, and system architectures that define the state of the art in continuous batching as of 2025.

 

2. Theoretical Foundations: The Physics of Inference

To understand the necessity of continuous batching, one must first rigorously analyze the hardware constraints of modern accelerators and the specific computational profile of the Transformer architecture during inference.

 

2.1 The Roofline Model and Arithmetic Intensity

The performance of any computational kernel is governed by the “Roofline Model,” which dictates whether a process is bound by the speed of calculation (Compute-Bound) or the speed of data movement (Memory-Bound). This relationship is defined by Arithmetic Intensity, the ratio of floating-point operations (FLOPs) performed per byte of memory accessed from the High-Bandwidth Memory (HBM).

$$\text{Arithmetic Intensity} = \frac{\text{FLOPs}}{\text{Bytes Accessed}}$$

LLM inference is bifurcated into two distinct phases with diametrically opposed arithmetic intensities: the Prefill Phase and the Decode Phase.5

 

The Prefill Phase: The Compute-Bound Regime

The prefill phase, or prompt processing, is the initialization step where the model processes the user’s input context. Because all tokens in the prompt are available simultaneously, the attention mechanism can compute the causal relationships between all token pairs in parallel. For a prompt of length $L$ and a hidden dimension $H$, the matrix multiplications involve tensors of shape $[L, H]$.

Crucially, the model weights are loaded from HBM once and reused across the $L$ tokens. This high degree of weight reuse results in high arithmetic intensity. In this phase, modern GPUs like the NVIDIA H100 are typically compute-bound; they are utilizing their Tensor Cores to their maximum theoretical TFLOPS, and the primary latency driver is the raw speed of matrix multiplication.5

 

The Decode Phase: The Memory-Bound Regime

 

The decode phase is the autoregressive generation loop. Here, the model generates one token at a time. To generate token $t+1$, the model must process the state of the previous token $t$ against the stored Key-Value (KV) cache of the entire history.

In a naive implementation with a batch size of 1, the arithmetic intensity collapses. The massive model weights (often exceeding 100GB for models like Llama-3 70B) must be streamed from HBM to the Stream Multiprocessors (SMs) for every single token generated. However, these weights are applied only to a single token vector. The ratio of computation to memory access is extremely low. Consequently, the GPU spends the vast majority of its cycle time idling, waiting for data to arrive from memory. The process is strictly memory-bandwidth bound.7

 

2.2 The Role of Batching in Bandwidth Amortization

 

Batching is the primary mechanism used to escape the memory-bound regime of the decode phase. By processing $N$ requests simultaneously, the inference engine can load the model weights once and apply them to $N$ token vectors in parallel. This increases the arithmetic intensity by a factor of roughly $N$.

If the memory bandwidth is $BW$ (bytes/sec) and the model size is $M$ (bytes), the time to load weights is $M/BW$. If the time to compute the forward pass for one token is $T_{compute}$, and $T_{compute} \ll M/BW$, the GPU is efficient only when we can increase the compute load to match the memory load time.

However, the efficacy of this optimization is entirely contingent on Occupancy. In static batching, occupancy decays over time.

Consider a static batch of 32 requests.

• At $t=0$, all 32 slots are active. The GPU is efficient.
• At $t=50$, the short requests (e.g., “Hello, how are you?”) finish. The effective batch size drops to 20.
• At $t=500$, only the RAG-heavy summarization tasks remain. The effective batch size drops to 2.

For the remainder of the generation, the GPU is effectively running with a batch size of 2, returning to the memory-bound regime and wasting the vast majority of the hardware’s potential throughput.3 The “sawtooth” utilization pattern of static batching is physically inherent to the variance in request lengths.

 

3. The Mechanics of Continuous Batching

 

Continuous batching solves the occupancy problem by redefining the lifecycle of a batch. In this paradigm, a batch is not a fixed container but a dynamic stream.

 

3.1 The Iteration-Level Scheduler

 

The defining innovation of continuous batching is the shift in scheduling granularity from the request level to the iteration level.1 The scheduler does not wait for a batch to complete; it makes a scheduling decision after every single token generation step.

The operational loop of a continuous batching engine (like vLLM or Orca) proceeds as follows:

1. Step Completion & Evaluation: The engine completes a forward pass, generating one token for each of the $N$ active requests.
2. Termination Check: The scheduler inspects the generated tokens. If request $R_i$ generates an EOS token or reaches its length limit, it is immediately marked as complete.
3. Eviction & Cleanup: The completed request $R_i$ is evicted from the active processing list. Its occupied resources—specifically the KV cache slots in HBM—are freed and returned to the memory pool.6
4. Admission & Injection: The scheduler checks the global request queue. If there are waiting requests and sufficient free memory (blocks), it admits new requests (say, $R_{new}$) into the batch.
5. Context Aggregation: The scheduler constructs the input tensors for the next step. This batch now contains a mix of:

• Decoding Requests: Existing requests needing their $(n+1)^{th}$ token.
• Prefill Requests: Newly admitted requests needing their prompt processed.

1. Execution: The model runs the next iteration.

This loop ensures that the GPU always operates at or near its maximum batch capacity (saturation point). As soon as a slot opens, it is filled. The latency for a new request is no longer dependent on the stragglers of the previous batch, but only on the time it takes for any slot to free up.1

 

3.2 The Orca Paradigm and Selective Batching

 

The academic foundation for this technique was established by the Orca system, presented at OSDI ’22.2 Orca introduced the concept of Selective Batching to handle the distinct mathematical requirements of continuous batches.

In a Transformer, most operations (Linear layers, MLPs, LayerNorms) are sequence-length independent at the token level—they operate on the hidden state dimension. However, the Attention mechanism is inherently sequence-length dependent; the attention score calculation depends on the number of past tokens (history).

In a continuous batch, requests have wildly different history lengths. Request A might be at token 5, while Request B is at token 2,000. Standard tensor operations cannot batch these disparate shapes into a single dense rectangle easily. Orca solved this by “selecting” specific operations to batch and managing the attention mechanism separately, effectively flattening the batch into a 1D stream of tokens for linear layers and using specialized kernels or padding management for the attention layers.10

Orca’s scheduler employs a First-Come-First-Served (FCFS) algorithm by default, but because it re-evaluates at every iteration, it prevents the Head-of-Line blocking phenomenon associated with static batching. The “batch” is effectively a virtual construct that is reconstituted every few milliseconds.11

 

3.3 Continuous vs. Dynamic Batching

 

It is a common misconception to conflate continuous batching with “dynamic batching,” a term utilized in older serving frameworks like TensorFlow Serving or Triton (prior to the LLM era).

• Dynamic Batching (Classic): A server waits for a small time window (e.g., 5ms) to accumulate incoming requests. Once the window closes or the max batch size is reached, it dispatches the batch. Crucially, once dispatched, the batch is immutable. The GPU computes until the batch is done.
• Continuous Batching (LLM): There is no “accumulation window” necessary. Requests can be injected instantaneously into a running loop. The immutability constraint is removed.7

As highlighted in comparative analyses, while classic dynamic batching is suitable for uniform workloads (like ResNet image classification), it fails for LLMs because it cannot handle the generation variance. Continuous batching is the specialized adaptation of dynamic batching for autoregressive workloads.13

 

4. Memory Management: The PagedAttention Revolution

 

If iteration-level scheduling is the logic of continuous batching, PagedAttention is the enabling technology. Without advanced memory management, the fragmentation costs of continuous batching would negate its throughput benefits.

 

4.1 The Fragmentation Bottleneck

 

In early LLM serving systems, the Key-Value (KV) cache—the memory storing the attention context for each sequence—was allocated as a contiguous tensor. Because the final length of a generated sequence is unknown at the start, the system had to over-provision memory based on the max_sequence_length.

This led to severe memory waste:

1. Internal Fragmentation: If a request reserved space for 2,048 tokens but only generated 100, 95% of that memory block was wasted.
2. External Fragmentation: As requests of different sizes were allocated and freed, the GPU memory heap became fragmented. The allocator might report 2GB of free memory, but if that memory was scattered in small non-contiguous chunks, it could not accommodate a new large request requiring a contiguous block.14

This fragmentation meant that the “effective” batch size was severely limited by memory constraints, often capping concurrency far below the GPU’s compute potential.

 

4.2 PagedAttention: Virtualizing GPU Memory

 

Introduced by the vLLM project, PagedAttention applies the principles of Operating System virtual memory to LLM inference.14

Instead of requiring contiguous physical memory, PagedAttention partitions the KV cache into fixed-size blocks (e.g., holding 16 or 32 tokens each). These blocks can be stored anywhere in the GPU’s HBM.

• The Block Table: The system maintains a virtual-to-physical mapping table for each request. As a request generates tokens, it fills up its current block.
• On-Demand Allocation: When a block is full, the memory manager allocates a new physical block from the free pool and links it in the Block Table. This allocation happens dynamically, token by token.
• Elimination of Fragmentation: Because blocks are fixed-size and non-contiguous, external fragmentation is eliminated. Any free block can be used by any request. Internal fragmentation is restricted to only the last partially filled block of a sequence.14

 

4.3 Advanced Memory Capabilities: Copy-on-Write

 

The block-based architecture enables sophisticated optimizations beyond simple storage. A prime example is Parallel Sampling (e.g., generating three different responses for the same prompt) or Beam Search.

In a contiguous memory system, generating three outputs would require copying the entire prompt’s KV cache three times. With PagedAttention, the system uses a Copy-on-Write mechanism. The three requests initially share the same physical blocks for the prompt. Their Block Tables point to the same memory. Only when the sequences diverge (generate different tokens) does the system allocate new, separate blocks for the divergent paths. This reduces memory usage by massive margins (often 55% or more) in complex sampling scenarios, further increasing the available capacity for batching.15

 

4.4 TensorRT-LLM and In-Flight Batching Memory

 

NVIDIA’s TensorRT-LLM implements a parallel concept under the “In-Flight Batching” moniker. It utilizes a C++ runtime that manages a pre-allocated pool of KV cache blocks.

• Tuning Parameters: Administrators must configure parameters such as max_num_tokens and free_gpu_memory_fraction. The system typically reserves a large slice (e.g., 85-90%) of available HBM for this cache pool at startup.18
• Batch Manager: The TRT-LLM BatchManager handles the orchestration, ensuring that requests are only scheduled if sufficient blocks are available in the pool. This explicit management allows TRT-LLM to guarantee stability under high load, preventing Out-Of-Memory (OOM) crashes that could occur with less rigorous allocators.19

 

5. The Scheduling Challenge: Prefill-Decode Interference and Chunking

 

While continuous batching maximizes utilization, it introduces a new antagonism between the two phases of inference: Prefill-Decode Interference.

 

5.1 The Inter-Token Latency (ITL) Spike

 

In a naive continuous batching implementation, when a new request is injected into the batch, the engine must perform the prefill computation for that request’s prompt. If the prompt is long (e.g., a 4,000-token document for summarization), the prefill step is computationally heavy and takes significant time (e.g., 200ms).

During this 200ms window, the GPU is fully occupied by the prefill. Consequently, all existing requests in the batch—which are in the decode phase and expecting to generate a token every 20ms—are stalled. They cannot run their decode step until the massive prefill finishes.

This phenomenon manifests as a spike in Inter-Token Latency (ITL) or Time-Between-Tokens (TBT). For a user interacting with a chatbot, the stream of text smoothly flows, then suddenly “hiccups” or freezes for a fraction of a second every time a new user joins the system.20 This degradation of the “Quality of Service” (QoS) is unacceptable for latency-sensitive applications.

 

5.2 Chunked Prefills (Sarathi-Serve)

 

To mitigate this interference, the Sarathi-Serve research introduced the concept of Chunked Prefills.20

Instead of processing a long prompt as an atomic, indivisible unit, the scheduler decomposes the prefill into smaller chunks (e.g., 512 tokens). The execution flow is altered:

1. Iteration N: The batch includes ongoing decodes + the first 512 tokens of the new request.
2. Iteration N+1: The batch includes ongoing decodes + the next 512 tokens of the new request.
3. …
4. Iteration N+k: The final chunk of the prompt is processed, and the new request transitions to the decode phase.

By capping the computation amount in any single iteration, the system bounds the iteration time. The prefill is amortized over multiple steps. The existing users see a stable TBT (perhaps slightly elevated due to the larger batch, but without massive spikes).

Trade-offs:

• TBT Improvement: Tail latency is significantly reduced.
• TTFT Degradation: The Time To First Token for the new request increases, as its prompt processing is spread out over time rather than blasted through in one go.
• Throughput Overhead: Loading weights for multiple iterations introduces slight overhead compared to a single massive kernel launch.23

 

5.3 Implementation in Major Frameworks

 

• vLLM: Supports chunked prefill as an optional feature. vLLM’s scheduler logic prioritizes decode requests to maintain low latency. It calculates a “token budget” for the iteration and fills the remaining budget with prefill chunks. If a prompt is too long, it is split.23
• TGI (Text Generation Inference): TGI v3 places a heavy emphasis on chunked prefill (and what it calls “FlashDecodes”). It claims to handle this transition more aggressively than vLLM, optimizing the kernels to allow prefill chunks to “piggyback” on decode steps with minimal overhead.25
• TensorRT-LLM: Supports enable_chunked_context to decouple memory consumption from context length. This allows the system to accept requests with long contexts even when memory is tight, processing them piece-meal.27

 

5.4 The Fairness Problem: FairBatching

 

A nuanced critique of the Sarathi-style “stall-free” schedulers comes from the recent FairBatching research.28

Standard schedulers like vLLM’s often prioritize decodes to minimize TBT. However, this creates “Computational Unfairness.” If a system is flooded with decode requests, new prefill requests might be starved, leading to excessive queuing delays.

Furthermore, the “Time-Between-Tokens” metric is non-monotonic; simply minimizing it doesn’t always yield the best user experience if it leads to extreme TTFT for new users.

FairBatching proposes a scheduler that dynamically adjusts the batch capacity and enforces fair resource allocation between prefill and decode tasks, rather than blindly prioritizing one over the other. It moves away from the rigid “decode-first” paradigm to a more fluid budget allocation, reducing TTFT tail latency by up to 2.29x while maintaining TBT SLOs.28

 

6. Advanced Architectures: Prefill-Decode Disaggregation

 

As context windows grow to 100k+ tokens (RAG, document analysis), the interference problem becomes intractable even with chunking. The sheer volume of prefill computation overwhelms the decode capacity. This has led to the emergence of Prefill-Decode Disaggregation (PDD) or “Splitwise” architectures.29

 

6.1 The Disaggregated Cluster

 

In a standard “aggregated” setup, every GPU performs both prefill and decode. In a PDD setup, the cluster is specialized:

1. Prefill Instances (The “Brain”): Equipped with compute-heavy GPUs (e.g., NVIDIA H100s with massive FP8 throughput). These instances strictly process prompts and generate KV caches. They do not generate output tokens.
2. Decode Instances (The “Mouth”): Equipped with memory-capacity-heavy GPUs (e.g., NVIDIA A100 80GB or L40S). These instances strictly generate tokens autoregressively using the KV caches provided by the Prefill instances.

 

6.2 The KV Cache Transfer Bottleneck

 

The fundamental challenge of PDD is the handover. Once the Prefill instance computes the KV cache, this massive state object must be transferred to the Decode instance.

For a large model and long context, the KV cache can be Gigabytes in size. Transferring GBs of data over standard Ethernet is too slow; the latency of transfer would negate the speedup of the prefill.

Solutions:

• High-Speed Interconnects: PDD architectures rely on RDMA (Remote Direct Memory Access) over Infiniband or RoCE (RDMA over Converged Ethernet) to transfer KV caches directly from GPU memory to GPU memory, bypassing the CPU.30
• KV Cache Compression: Techniques to quantize the KV cache (e.g., to FP4 or INT8) are essential to reduce the transfer bandwidth requirement.
• Global Cache Awareness: The scheduler must be “cache aware,” routing decodes to instances that might already hold a partial cache for that document (Prefix Caching), minimizing the need for transfer.32

Frameworks like DistServe and Splitwise (and increasingly vLLM/TRT-LLM via specific configurations) utilize this architecture to scale throughput linearly with cluster size, independently scaling “input processors” and “output generators” based on the specific traffic shape (e.g., long prompt/short output vs. short prompt/long output).31

 

7. Framework Deep Dives: The 2025 Landscape

 

Three frameworks currently dominate the landscape of production LLM serving. Each approaches continuous batching with a distinct philosophy.

 

7.1 vLLM (Virtual Large Language Model)

 

Philosophy: The open-source standard. High throughput, flexibility, and community-driven innovation.

• Architecture: vLLM uses a centralized scheduler (Python-based, moving to C++) and a distributed set of workers. Its core differentiator is the PagedAttention kernel.
• Scheduling: vLLM’s scheduler is highly configurable. It supports max_num_seqs (max batch size) and max_num_batched_tokens (iteration budget). It uses a “Block Manager” to track PagedAttention blocks.
• Chunked Prefill: vLLM implements chunked prefill by prioritizing decodes. If enable_chunked_prefill=True, it fills the batch with decodes first, then uses the remaining max_num_batched_tokens budget for prefill chunks.23
• Performance: vLLM excels in high-concurrency regimes. Its PagedAttention implementation ensures near-zero memory waste, allowing for higher batch sizes than naive implementations. However, the Python overhead of the scheduler has historically been a criticism for low-latency/low-batch scenarios, prompting the rewrite of the core loop in C++.32

 

7.2 Hugging Face Text Generation Inference (TGI)

 

Philosophy: Production readiness, safety, and “Zero Config” performance.

• Architecture: TGI uses a Rust-based Router (WebServer) and a Python/C++ Model Server. The Rust router handles the continuous batching logic, queueing, and token budgeting. This separation allows the request handling logic to run asynchronously and extremely fast, independent of the Python GIL.34
• V3 Innovations: TGI v3 introduced a massive overhaul. It claims to be “Zero Config,” automatically tuning batch parameters based on hardware.
• Performance Claim: TGI reports processing 3x more tokens and being 13x faster than vLLM on very long prompts (200k+ tokens). This is achieved through an optimized “Radix” style tree structure for prefix caching (reusing the “conversation history” without re-computation) and highly optimized kernels that handle the prefill-decode transition more efficiently than vLLM’s generic kernels.25
• FlashAttention: TGI relies heavily on FlashAttention-2 and custom kernels rather than PagedAttention in some configurations, arguing that PagedAttention’s indirection layer can add overhead compared to purely contiguous optimized kernels where possible.36

 

7.3 NVIDIA TensorRT-LLM

 

Philosophy: Maximum raw performance via compilation and hardware specificity.

• Architecture: TRT-LLM is a toolkit to build “Engines.” Unlike vLLM which executes eagerly, TRT-LLM compiles the model graph, fusing layers (e.g., fusing Linear+Activation+Bias) and optimizing memory pointers for the specific GPU architecture (e.g., Hopper H100).
• In-Flight Batching: Implemented via a C++ BatchManager. It is rigorous and static in its resource allocation. It supports advanced features like FP8 quantization natively, which can double the throughput on H100s compared to FP16.37
• Tuning: Achieving peak performance in TRT-LLM requires careful tuning of max_batch_size, max_num_tokens, and the KV cache block size. Benchmarks suggest that setting max_batch_size to large values (e.g., 2048) allows the internal scheduler to maximize parallelism, provided memory limits (max_num_tokens) are respected.27
• Disadvantages: The compilation step (building the engine) takes time and makes rapid prototyping difficult. It is less flexible than vLLM for research but superior for stable, high-volume production.39

 

8. Quantitative Analysis and Benchmarks

 

The transition to continuous batching yields quantifiable improvements, but the magnitude depends on the workload.

 

8.1 Throughput vs. Latency Trade-offs

 

• Throughput: Continuous batching typically improves throughput by 2x to 23x compared to static batching. The gains are highest when request length variance is high. vLLM benchmarks show near-linear scaling of tokens/second with batch size until the memory bandwidth limit is hit.7
• Latency (TTFT): Continuous batching can actually increase TTFT slightly compared to a batch-size-of-1 baseline, because a new request must wait for the current iteration to finish and potentially queue behind other requests. However, compared to static batching (where it waits for a whole batch to finish), it is orders of magnitude faster.
• Latency (TBT): This is the critical metric. Optimized stacks (TRT-LLM/vLLM) on H100 GPUs can maintain TBT < 50ms for Llama-3 70B even under load, provided the batch size is managed to prevent the ITL spike discussed earlier.37

 

8.2 Baseten & Mistral 7B Case Study

 

Benchmarks conducted by Baseten on Mistral 7B (FP8 on H100) using TensorRT-LLM reveal the ceiling of current performance:

• TTFT: ~130ms.
• Throughput: ~170 tokens/second/user (for a single stream, but total system throughput is much higher).
• Total Response Time: 700ms for 100 tokens.
  These numbers demonstrate that with continuous batching and FP8, LLM inference is approaching real-time, conversational latency even for reasonably large models.12

 

8.3 Comparative Throughput (2025)

 

• Short/Medium Contexts: vLLM, TGI, and TRT-LLM are within 10-15% of each other. The choice is often one of ecosystem preference.
• Long Contexts (RAG): TGI v3 and TRT-LLM (with correct tuning) pull ahead of vLLM due to better handling of the massive prefill workload and prefix caching mechanisms.16

 

9. Conclusion

 

Continuous batching has evolved from a novel research idea in the Orca paper to the fundamental operating principle of the generative AI industry. It is the architectural “gearbox” that converts the raw, volatile horsepower of modern GPUs into a smooth, efficient stream of intelligence.

The journey has moved through three phases:

1. The Logic Phase: Orca proving that iteration-level scheduling was possible.
2. The Memory Phase: vLLM and PagedAttention solving the fragmentation crisis, enabling massive concurrency.
3. The Architecture Phase: The current era of Chunked Prefills, FairBatching, and Prefill-Decode Disaggregation, which seek to optimize the complex interplay between latency, fairness, and massive context windows.

As we look toward the future, the line between the “Scheduler” and the “Operating System” will continue to blur. Inference engines are becoming specialized OS kernels, managing the virtual memory of KV caches and the process scheduling of token generation. For the practitioner, the choice of framework—whether the flexible vLLM, the robust TGI, or the highly-tuned TensorRT-LLM—depends less on the simple presence of continuous batching (which is now table stakes) and more on the specific nuances of their workload’s context length, latency SLOs, and hardware infrastructure.

 

Table 1: Feature Comparison of Major Continuous Batching Frameworks (2025)

 

Feature	vLLM	TGI (Text Generation Inference)	TensorRT-LLM
Core Batching Logic	PagedAttention (Python/C++ Scheduler)	Rust Router + FlashDecodes	In-Flight Batching (C++ BatchManager)
Memory Management	Block Table (Virtual Memory)	PagedAttention / Optimized Kernels	Paged KV Cache Pool (Pre-allocated)
Prefill Strategy	Chunked Prefill (Optional, Decode-Prioritized)	Native Chunking (Aggressive optimization)	Chunked Context (Decoupled memory)
Performance Profile	High Throughput, Linear Scaling	Superior on Long Contexts (RAG)	Max Raw Token/Sec on NVIDIA H100
Configuration	Highly Configurable (block size, swap space)	“Zero Config” (Auto-tuning)	Compilation-based (Engine Build)
Ecosystem	Open Source Standard, Ray/K8s Integration	Hugging Face Hub Native	NVIDIA Enterprise / Triton Integration