The Short Answer: Which Framework Wins in 2026?
For general-purpose serving with fast iteration, vLLM remains the default pick. For structured agentic workloads with heavy prefix reuse, SGLang's cache-aware scheduling pulls ahead. For maximum throughput on fixed model shapes where you can afford build complexity, TensorRT-LLM still takes the crown.
Anyone declaring a single winner without naming batch size, context mix, and SLO is benchmarking their own setup. The right answer is workload-shaped, and this post gives you the decision rules we use with sourcing teams.
Throughput at Fixed Latency: What Changes the Ranking?
At high batch with long outputs, continuous batching efficiency and KV management dominate. vLLM's PagedAttention and SGLang's RadixAttention both attack the same waste (KV duplication), with SGLang winning when many requests share long prefixes: system prompts, few-shot blocks, tool schemas.
TensorRT-LLM wins when you compile for a narrow envelope: fixed model, fixed precision, known sequence distribution. Change the envelope (new context lengths, new modalities) and the build tax returns. Generalist teams feel that tax monthly.
| Framework | Best at | Price of admission |
|---|---|---|
| vLLM | General serving, fast iteration | Tuning chunked prefill + KV limits |
| SGLang | Prefix-heavy agents, structured output | Newer ops surface, smaller ecosystem |
| TensorRT-LLM | Peak throughput, fixed shapes | Build complexity, narrow envelope |
Latency SLOs: Time to First Token vs Tokens Per Second
Interactive products live on TTFT plus steady inter-token pacing. Disaggregated prefill/decode (now standard practice on all three stacks) isolates the two: fat prefill nodes for prompt crunching, lean decode nodes for streaming. If your p99 TTFT regresses, look at prefill queueing before blaming the framework.
Our SLO rule: pin p50/p95/p99 for both TTFT and inter-token delay at your actual prompt distribution, then compare frameworks at equal SLO attainment, not equal batch. A framework that needs 2x the GPUs to hold p99 is not faster.
Cost Per Token: Translating Benchmarks to GPU Bills
Throughput at fixed SLO converts directly to $/M tokens: tokens per second per GPU divided by $/GPU/hr. A 20 percent throughput edge at equal SLO is a 17 percent unit-cost cut. Over millions of requests, framework choice is a pricing decision wearing a technical costume. GPU prices referenced here reflect market conditions at time of writing and can fluctuate with availability.
Do the math on your mix, not the vendor's mix. Short chat prompts and long document prompts produce different winners on the same hardware. Re-run quarterly; kernels move fast.
The Ops Reality Nobody Benchmarks: Upgrades and Debuggability
vLLM upgrades fastest and breaks least across model releases, which is why platform teams default to it. SGLang moves quickly on agent-centric features (speculative decoding variants, constrained decoding) with a smaller support surface. TensorRT-LLM demands build-pipeline ownership: engine versioning, profile matrices, and regression harnesses.
Staff the choice honestly. TensorRT-LLM without an owner for the build matrix becomes the slowest framework in practice, whatever the benchmark says.
Our Production Recommendation by Team Shape
Small platform team serving many models: standardize on vLLM, invest in prefix caching and disaggregation, revisit SGLang for your most prefix-heavy agent endpoint. One team tried to run all three and spent the quarter on version pins instead of latency.
Inference-at-scale team with fixed flagship models: run TensorRT-LLM for the hot path, vLLM for everything else. Price both against current H100/B200 rental supply on ClusterBid inventory before reserving the fleet that holds your p99.