vLLM vs TensorRT-LLM vs SGLang 2026 — LLM Inference Serving Engines Compared

Buy the right GPU and you have a fast chip; pick the wrong serving engine and you leave 30–50% of its throughput on the table. In 2026, production LLM serving on data-center GPUs has consolidated around three open engines — vLLM, NVIDIA TensorRT-LLM, and SGLang — each with a distinct philosophy. This guide maps them onto throughput, batching, prefix caching, quantization, and hardware support so you can match the engine to the workload, not the vendor's slide deck.

Why the Serving Engine Matters at All

Token generation is memory-bound, so an inference engine's job is to keep the memory system saturated while serving many requests concurrently. The techniques that do this — and which engines implement them — separate a well-utilized GPU from an idle one:

• Continuous (in-flight) batching — admit new requests and retire finished ones at the token level rather than waiting for a whole batch to complete. This is the single biggest lever on real-world throughput and latency.
• PagedAttention / KV-cache management — allocate the attention key/value cache in fixed-size "pages" to eliminate fragmentation and near-double concurrency.
• Prefix caching — reuse the KV cache across requests that share a prompt prefix (system prompts, tool schemas, document RAG), which can cut prefill cost by an order of magnitude on multi-turn workloads.
• Quantization — FP8, INT8, and 4-bit (AWQ/GPTQ) shrink the model so it fits in less VRAM and streams through bandwidth faster.

The Three Engines

vLLM (UC Berkeley) is the default choice for most teams: easy to deploy, model coverage unmatched (from Llama and Qwen to MoE and multimodal), a stable OpenAI-compatible API, and solid continuous batching via PagedAttention. It prioritizes flexibility and velocity over squeezing out the last percent of NVIDIA-only performance.

TensorRT-LLM (NVIDIA) compiles models into highly optimized engines for NVIDIA hardware. It generally posts the highest raw tokens/sec on a given NVIDIA GPU — especially after FP8 or INT4 AWQ quantization and custom kernel tuning — but the build-compile-tune workflow is steeper and the engine is NVIDIA-only by design.

SGLang (Stanford/Berkeley) pairs continuous batching with RadixAttention, an automatic prefix-cache that makes it the standout for agentic, multi-turn, and long shared-context workloads — think RAG with a fixed document corpus, or tool-calling agents that resend the same schema thousands of times. Its structured-output and fast constrained-decoding support also make it a favorite for JSON-gated applications.

Matching Engine to Workload

• Standard chat / API serving, many different models → vLLM. Broadest model support, minimal engineering, easy horizontal scaling, and hardware portability.
• Peak throughput on a fixed NVIDIA fleet, latency-SLA production → TensorRT-LLM. Accept the compile-and-tune workflow to extract the highest tokens/sec per H100/B200.
• Agentic systems, RAG with a stable corpus, long tool schemas, JSON-heavy output → SGLang. RadixAttention turns repeated prefixes from wasted compute into free tokens, and structured decoding keeps outputs schema-valid.
• Multi-vendor or AMD/Intel fleets → vLLM (or SGLang on ROCm). TensorRT-LLM is not an option off NVIDIA.

What This Means for GPU Purchasing

The engine you standardize on changes the hardware equation. TensorRT-LLM's lead is most pronounced on NVIDIA's latest architectures with FP8 support (H100, H200, B200), so it pairs naturally with a homogeneous NVIDIA fleet. vLLM's portability lets you mix AMD MI300X into the same serving pool without a second stack. And SGLang's prefix caching means an H200 with large HBM can often serve a bigger effective concurrent user base than raw FLOPs would suggest — the KV cache is where the memory budget goes, so capacity and bandwidth, not TFLOPS, determine how many multi-turn users a node holds.