vLLM vs SGLang vs TensorRT-LLM: Agentic RAG Benchmark 2026

An agentic RAG system can hit a two-second time-to-first-token target and still feel painfully slow. Retrieval, reranking, tool calls, retries, and repeated prompt prefill often dominate the user’s wait.

That’s why choosing an inference server for agents requires more than a tokens-per-second chart. The useful question is:

Which serving stack delivers the lowest cost per successful task at the required p99 workflow latency?

This is a benchmarking guide, not a claim that vLLM, SGLang, or TensorRT-LLM wins universally. The answer depends on the model, GPU fleet, prompt structure, cache reuse, tool behavior, and quality gates.

Why standard generation benchmarks mislead agent builders

Most LLM serving comparisons use a clean test: send prompts to a server, measure time to first token, count output tokens, and increase concurrency until throughput levels off.

That test is useful for checking a deployment. It’s a poor model of an AI agent.

A production request may:

1. Rewrite the user’s query.
2. Generate an embedding.
3. Search a vector index.
4. Rerank documents.
5. Ask the model whether the evidence is sufficient.
6. Call a database or external API.
7. Generate a follow-up response with a larger context.
8. Validate the answer and retry if necessary.

The relevant equation is closer to:

Agent latency = queueing + embedding + retrieval + reranking + prefill + decode + tool execution + orchestration

Only part of that path belongs to the inference server. A fast average model request can still produce an unacceptable p99 when a vector database stalls, a tool retries, or a dependent model call waits behind a large batch.

MLPerf’s inference results are useful reference points for isolated model serving, but they aren’t end-to-end RAG measurements. For example, interactive and server scenarios report targets such as p99 time to first token and time per output token. Those figures help characterize queueing, prefill, and decode. They don’t include your retriever, tools, retries, or answer evaluation.

Use isolated generation to establish a baseline. Don’t use it as the final migration decision.

vLLM vs. SGLang vs. TensorRT-LLM

These systems overlap, but their strengths and operational costs differ.

vLLM is often the sensible baseline. Its API compatibility, ecosystem, and broad model support make it easy to deploy and compare. Prefix caching and continuous batching can perform well, but the result still depends on prompt stability, scheduler settings, context length, and concurrency.

SGLang is worth testing when agents repeatedly send the same system instructions, tool schemas, conversation prefixes, or structured-generation programs. Its radix-cache design can reduce repeated prefill. That is a documented capability; the size of the improvement is workload-dependent. A cache that looks excellent with identical synthetic prompts may perform poorly when retrieved documents, tool arguments, or serialization order change.

TensorRT-LLM makes the most sense when the model and hardware fleet are stable, particularly in NVIDIA-only environments. Its optimized kernels and quantization paths can improve memory use and execution efficiency. Actual end-to-end gains depend on model architecture, batch shape, quantization, parallelism, and engine configuration. The price is more version-specific maintenance than a general-purpose server usually requires.

Model support is also not a static property. Check the serving stack’s current documentation and release notes for the exact model revision, quantization, multimodal features, speculative-decoding path, and distributed configuration you plan to use. “Supports the model family” isn’t enough.

A practical agentic RAG benchmark

Run the same workload against every server. Keep the model checkpoint, tokenizer, quantization, prompts, retriever, reranker, tool implementations, and quality evaluator constant.

1. Single-turn generation

Start with input lengths of 1K, 8K, and 32K tokens, and output targets of 128, 512, and 2,000 tokens. Test concurrency from one request through saturation.

Record:

• p50, p95, and p99 TTFT
• time per output token
• output tokens per second
• completed requests per second
• GPU memory and utilization
• queueing and prefill time separately

This catches configuration errors and establishes a decoder baseline. It doesn’t tell you whether the server is good for agents.

2. Repeated-prefix chat

Keep the system prompt, tool definitions, and prior conversation stable. Change the final question and retrieved documents.

Measure:

• Prefix or radix-cache hit rate
• Prefill time with and without caching
• p99 TTFT
• Cache memory consumption
• Evictions under concurrency
• Performance as the prefix becomes less stable

Use realistic variation. Change tool arguments, document order, whitespace, and user questions. A synthetic loop with identical prompts will exaggerate cache performance; randomizing every token will hide a benefit that may exist in production.

As a practical gate, you might require at least a 70% cache-hit rate on the production-like workload before treating caching as a major architectural advantage. That threshold isn’t universal, but it forces the team to define what “enough reuse” means.

3. Multi-turn tool use

A representative workflow might be:

1. The user asks a question.
2. The model calls search.
3. The retriever returns documents.
4. The model calls a database or calculator.
5. The model produces and validates the answer.

Measure time to successful completion, not just the first model token. Track model turns, tool failures, retries, input and output tokens, invalid tool arguments, and quality-gate failures.

Define the retry policy before testing. For example, allow one retry for a timeout or malformed tool call, but no automatic retry after a failed quality check unless the production system actually does that. Otherwise, the benchmark will hide reliability costs.

4. End-to-end RAG

Run the complete controlled pipeline:

• Embedding
• Retrieval
• Reranking
• Query rewriting
• Document grading
• Answer generation
• Answer evaluation

Use the same corpus and relevance judgments for every server. The quality gate might require citation support, a correct tool result, and a task-specific answer score. A faster server that produces invalid tool arguments or unsupported answers isn’t cheaper.

A compact test matrix could look like this:

For a concrete capacity test, replay an arrival rate of two workflows per second against an eight-GPU deployment for 30 minutes, with a five-minute warm-up. Use a fixed random seed, record every request, and report results separately for warm and cold cache conditions.

Publish the GPU model and count, driver and CUDA versions, model revision, quantization, tensor and pipeline parallelism, context limit, scheduler settings, cache configuration, tokenizer version, and arrival distribution. Without those details, the result is difficult to reproduce.

Measure the waterfall before changing servers

A trace should divide each workflow into:

• Queue time
• Prompt prefill
• Token decode
• Embedding
• Retrieval
• Reranking
• Tool execution
• Retries
• Client and orchestration overhead

Suppose generation accounts for 35% of workflow time. Even a 25% improvement in generation reduces total latency by only about 9%, assuming everything else remains unchanged. That’s the sort of calculation a waterfall makes obvious.

Prompt layout often matters more than a server switch. Put stable content first and volatile content last. Avoid nondeterministic JSON ordering, timestamps in system prompts, changing tool descriptions, inconsistent whitespace, and request-specific metadata before the reusable prefix.

Tool orchestration deserves the same attention. Independent retrieval and metadata calls should run concurrently where correctness allows. A serial workflow turns every network tail into part of the model’s critical path.

Cost per successful task

Report infrastructure cost alongside latency and quality.

Cost per successful task = infrastructure cost ÷ tasks that pass latency, correctness, and quality thresholds

Include failed tool calls, retries, discarded answers, and review work. A raw request cost hides all of them.

For example, if an eight-GPU cluster costs $24 per hour and completes 5,000 quality-passing tasks during that hour, the infrastructure cost is $0.0048 per successful task. If 12% of requests fail a quality gate and require another attempt, the denominator must be the number of tasks that ultimately pass—not the number of initial requests.

The final decision usually follows the waterfall:

• Choose vLLM when compatibility, model flexibility, and deployment speed matter most.
• Test SGLang when repeated structures and prefix reuse dominate, provided the exact model path is supported.
• Choose TensorRT-LLM when stable NVIDIA infrastructure can justify engine-building and maintenance work.
• Keep the current server when retrieval, reranking, tools, or orchestration dominate p99.

Benchmark the production-shaped workflow first. The leaderboard can come later.

Frequently Asked Questions

What is the best inference server for AI agents?

There isn’t one universal winner. vLLM is a strong general-purpose baseline, SGLang is attractive for structured workloads with substantial prefix reuse, and TensorRT-LLM suits stable NVIDIA deployments that can absorb more integration work. Compare successful-task latency, quality, and cost.

How should I benchmark end-to-end RAG latency?

Measure from request arrival through the evaluated final answer. Include embeddings, retrieval, reranking, every model turn, tool calls, retries, and orchestration. Report p50, p95, and p99 latency, cache-hit rate, task-success rate, quality, and GPU cost.

Does prefix caching improve multi-turn agent latency?

It can, when reusable prefixes are genuinely stable. Measure cache hits and eviction behavior under realistic traffic; identical synthetic prompts can make the benefit look much larger than it will be in production.

Is p99 TTFT enough for production agents?

No. TTFT covers the initial model response. Production agents need p99 time to successful task completion, including retrieval, tools, dependent model calls, retries, and final validation.