Introduction
Large language models (LLMs) have moved past the training bottleneck and are now constrained by how efficiently they can serve tokens in real‑world traffic. In 2025, the industry has converged on a handful of inference runtimes that balance latency, throughput, and cost, each making distinct trade‑offs in three core areas: request batching, the overlap of prefill and decode phases, and the storage and reuse of key‑value (KV) caches. This post dissects the six most prominent runtimes—
- TensorRT‑LLM – NVIDIA’s GPU‑centric engine that leverages TensorRT’s optimizations.
- FlashAttention‑2 – a PyTorch‑based runtime that introduces a new attention kernel for memory‑efficient inference.
- vLLM – a community‑driven framework that focuses on dynamic batching and KV cache sharing.
- DeepSpeed‑Inference – Microsoft’s solution built on ZeRO‑3 and optimized for large‑batch inference.
- ONNX Runtime with QLinear – a cross‑platform engine that supports quantized models.
- OpenAI’s Inference API – a managed service that abstracts the underlying runtime.
By examining how each runtime handles the three pillars of inference performance, we can understand why certain workloads favor one engine over another and how to make an informed choice for production deployments.
Main Content
Batching Strategies: From Static to Dynamic
Batching is the most straightforward lever for improving GPU utilization. Traditional static batching requires the user to predefine a batch size and pad all requests to that size, which can waste memory and increase latency for short prompts. Modern runtimes have moved toward dynamic batching, where requests are queued and grouped on the fly based on token length and available GPU memory.
TensorRT‑LLM employs a fixed‑size batch approach that is highly optimized for homogeneous workloads, making it ideal for data‑center inference where request patterns are predictable. In contrast, vLLM introduces a dynamic scheduler that aggregates requests until a threshold is reached, then dispatches them as a single kernel launch. This approach reduces kernel launch overhead but can introduce jitter for latency‑sensitive applications.
DeepSpeed‑Inference takes a hybrid route: it uses a small static batch for the prefill stage and then switches to dynamic batching during the decode phase. This design acknowledges that the prefill phase is often the bottleneck for long‑context models, while the decode phase benefits from larger, more flexible batches.
Prefill‑Decode Overlap: The Two‑Stage Pipeline
Large transformers process input in two distinct phases. The prefill stage computes attention for the entire prompt, while the decode stage generates tokens one at a time. Overlap between these stages can dramatically reduce overall latency.
FlashAttention‑2 introduces a prefill‑decode overlap mechanism that streams the prefill results directly into the decode pipeline. By overlapping the two stages, the runtime can hide the prefill latency behind the decoding of earlier tokens. This is especially beneficial for conversational models where the prompt is short but the response is long.
vLLM, on the other hand, focuses on prefill‑only optimization, leveraging KV cache reuse to avoid recomputing the prompt for each request. When a new request shares a prefix with an existing cache, vLLM can skip the prefill entirely, jumping straight to the decode phase. This cache‑sharing strategy is powerful for workloads with high redundancy, such as FAQ systems or templated responses.
TensorRT‑LLM and DeepSpeed‑Inference both support prefill‑decode overlap but implement it differently. TensorRT‑LLM uses pipeline parallelism, allocating separate GPU streams for prefill and decode, while DeepSpeed‑Inference relies on asynchronous kernel launches that interleave the two stages.
KV Cache Storage and Reuse
The KV cache holds the key and value tensors produced during the prefill stage and is reused during decoding. Efficient cache management is critical for both memory usage and speed.
ONNX Runtime with QLinear introduces quantized KV caches that reduce memory footprint by storing keys and values in 8‑bit integer format. Although quantization can introduce a small accuracy loss, the trade‑off is often acceptable for cost‑sensitive inference.
DeepSpeed‑Inference implements ZeRO‑3 for KV cache sharding, allowing the cache to be distributed across multiple GPUs. This approach scales to models with billions of parameters without exceeding per‑GPU memory limits.
vLLM’s KV cache is dynamic and shareable. When two requests share a prefix, the runtime maps them to the same cache slice, eliminating redundant memory allocation. This design is particularly effective for multi‑tenant inference services where many users request similar content.
OpenAI’s Inference API abstracts all of these details behind a simple HTTP endpoint. Internally, it uses a combination of dynamic batching, KV cache sharing, and prefill‑decode overlap, but the exact implementation is proprietary. The advantage for developers is that they can focus on application logic while the service handles scaling and optimization.
Performance Benchmarks and Use‑Case Scenarios
In a recent benchmark suite, TensorRT‑LLM achieved the lowest latency for a 16‑token prompt on an NVIDIA A100, thanks to its highly tuned static batching. FlashAttention‑2 outperformed all others on long‑prompt generation, delivering a 30% speed‑up over the baseline due to its efficient attention kernel.
vLLM excelled in a multi‑tenant environment where 80% of requests shared a common prefix. By reusing KV caches, vLLM reduced GPU memory usage by 40% and increased throughput by 25% compared to a naive implementation.
DeepSpeed‑Inference demonstrated the best scalability across 8 GPUs for a 13‑billion‑parameter model, leveraging ZeRO‑3 to keep per‑GPU memory below 40 GB.
ONNX Runtime with QLinear was the most cost‑effective for edge deployments, as its 8‑bit quantization allowed it to run on consumer GPUs without sacrificing too much accuracy.
OpenAI’s Inference API consistently delivered competitive latency while providing automatic scaling and zero‑maintenance, making it the go‑to choice for startups that cannot afford to manage infrastructure.
Conclusion
The landscape of LLM inference runtimes in 2025 is rich with specialized solutions that address distinct performance knobs. Whether you need raw speed on a single GPU, scalable throughput across a cluster, or cost‑effective edge inference, there is a runtime that aligns with your priorities. The key to selecting the right engine lies in understanding the trade‑offs between batching strategy, prefill‑decode overlap, and KV cache management, and then mapping those to your workload characteristics.
As LLMs continue to grow in size and complexity, the importance of efficient inference will only increase. Developers and operators must stay informed about the evolving capabilities of these runtimes and be prepared to iterate on their deployment strategies to keep pace with the rapid advancements in the field.
Call to Action
If you’re planning to deploy an LLM in production, start by profiling your typical request patterns: Are most prompts short or long? Do many users share common prefixes? What GPU resources are available? Use these insights to pick a runtime that matches your needs—whether that’s TensorRT‑LLM for low‑latency inference, vLLM for dynamic batching, or ONNX Runtime for edge deployments. Experiment with the open‑source runtimes, benchmark them against your workload, and consider a managed service like OpenAI’s API if you prefer a maintenance‑free solution. By making an informed choice today, you’ll position your application for the next wave of LLM innovation.
