Large language model (LLM) serving has become a critical cost factor for enterprises that want to deliver AI‑powered experiences at scale. Selecting the right inference engine can mean the difference between a profitable service and a budget‑draining experiment.
LLM serving engines refer to specialized software stacks that manage model weights, token generation, and GPU resources to deliver high‑throughput inference.
Why HBM Bandwidth Is the Real Bottleneck
Modern GPUs such as the NVIDIA H100 provide a 3.35 TB/s memory bus, yet streaming a 70 billion‑parameter model in FP8 still requires about 9.5 ms per forward pass just to move weights from HBM to the compute cores. In contrast, a single user decoding at roughly 40 tokens/sec consumes less than 2 % of the available FLOPs. This imbalance shows that HBM bandwidth, not raw compute, dominates LLM serving cost (benchmark data).
vLLM – PagedAttention and CUDA Graph Capture
vLLM introduces PagedAttention, which swaps inactive KV‑cache pages to host memory, dramatically reducing on‑GPU memory pressure. By pre‑capturing the execution graph with CUDA Graphs, vLLM eliminates kernel launch overhead, delivering higher token‑per‑second rates for long‑running sessions.
- Best for workloads with many concurrent users and large context windows.
- Relies on efficient host‑GPU paging; performance degrades if PCIe bandwidth is a secondary bottleneck.
TensorRT‑LLM – RadixAttention and Deep GPU Optimizations
TensorRT‑LLM leverages RadixAttention, a cache‑friendly attention algorithm that aligns memory accesses with the GPU’s warp scheduler. Combined with aggressive kernel fusion and INT8/FP8 quantization, it maximizes the utilization of the H100’s compute units while keeping weight streaming minimal.
- Ideal for single‑user or low‑latency applications where raw throughput matters.
- Requires careful model preparation; not all transformer variants are supported out‑of‑the‑box.
SGLang – Speculative Decoding and Hybrid Paging
SGLang focuses on speculative decoding, where a smaller “draft” model generates candidate tokens that are then verified by the full model. This approach can cut the number of expensive forward passes by up to 50 % in ideal conditions. SGLang also supports hybrid KV‑cache paging, allowing developers to choose between on‑GPU and host storage dynamically.
- Great for latency‑sensitive services that can tolerate a small verification overhead.
- Benefits most when the draft model is well‑matched to the target task.
How to Choose the Right Engine for Your Project
Consider three practical dimensions:
- Concurrency vs. latency: vLLM excels with high concurrency; TensorRT‑LLM shines for low‑latency single‑session use cases; SGLang offers a middle ground with speculative speed‑ups.
- Memory budget: If GPU memory is scarce, vLLM’s paging reduces on‑device KV‑cache size. TensorRT‑LLM assumes ample on‑GPU memory for its fused kernels.
- Implementation effort: vLLM integrates with popular inference servers out‑of‑the‑box, while TensorRT‑LLM may require custom engine building, and SGLang needs a draft model pipeline.
By mapping your workload characteristics to these criteria, you can select the engine that delivers the best cost‑to‑performance ratio.
Frequently Asked Questions
What is KV‑cache paging?
KV‑cache paging moves inactive key‑value pairs from GPU memory to host memory, freeing space for active tokens and reducing HBM pressure.
How does speculative decoding improve throughput?
Speculative decoding lets a lightweight draft model generate candidate tokens, which the full model then validates, cutting the number of full model passes needed per token.
Can I mix these engines in a single deployment?
Yes. Some architectures route high‑concurrency traffic to vLLM and reserve TensorRT‑LLM for premium low‑latency endpoints, while SGLang can act as a middleware layer for speculative acceleration.
Is FP8 quantization safe for all models?
FP8 works well for many transformer models but may require calibration to avoid accuracy loss, especially on tasks with fine‑grained numerical sensitivity.
Do I need specialized hardware to use these engines?
All three benefit from high‑bandwidth memory GPUs (e.g., NVIDIA H100), but they can run on lower‑tier GPUs with reduced performance.
Neptune Infotech can help you integrate the optimal LLM serving engine into your product stack, ensuring scalable and cost‑effective AI delivery.
