Compare LLM serving frameworks against the model, hardware, prompt distribution, and latency requirements of the intended service. This source-based guide covers engine selection and orchestration, with vendor benchmark claims attributed to their published workloads. Our vLLM vs TGI comparison covers documented capabilities, maintenance policy, and migration checks.
The frameworks covered here are the ones teams are actually deploying on H100 clusters in 2026: vLLM, SGLang, TensorRT-LLM, Ray Serve (as orchestration), and a brief accounting of where TGI now fits.
The metrics that separate them
Before comparing frameworks, be precise about what you’re measuring. Three numbers define production serving health:
Time-to-first-token (TTFT) — the wall-clock milliseconds from request receipt to the first output token. Users perceive latency as “the pause before it starts,” not the total generation time. A 400ms TTFT on a 2,000-token generation feels fine; a 2,000ms TTFT on a 200-token generation feels broken.
Output token throughput (tokens/sec) — how many decode-phase tokens the engine can emit per second across all concurrent requests. This is what your GPU bill reflects. Higher throughput at a given concurrency level means more requests served per dollar.
KV cache hit rate — what fraction of attention key-value pairs are served from cache rather than recomputed. For RAG workloads with a shared system prompt, a high cache hit rate collapses TTFT by 40–70%; frameworks differ in how automatically and aggressively they exploit this.
The secondary signal is p99 latency under load — not the mean, but the tail. A framework that looks identical at p50 can diverge by 3x at p99 when the request queue builds under a concurrency spike.
The main contenders in 2026
vLLM remains the safest default for new production deployments. Its PagedAttention mechanism manages KV cache memory as paged virtual memory — eliminating fragmentation and enabling continuous batching that dynamically absorbs new requests mid-generation. The result: up to 24x higher throughput than TGI under high-concurrency workloads, per independent benchmarking published on arXiv in November 2025, with 19–27% lower GPU memory utilization compared to TGI. vLLM ships an OpenAI-compatible API server out of the box, supports 200+ model architectures (Llama, Qwen, Mixtral, DeepSeek, Mamba, multimodal), and handles quantization via FP8, INT4, AWQ, and GPTQ. Multi-LoRA serving — routing different adapter weights to different request streams — works without reloading the base model. Squeezing more out of the same card is the subject of our LLM GPU cost optimization guide. Apache 2.0 licensed.
SGLang has closed the gap on vLLM in many workloads and, in prefix-heavy scenarios, surpassed it. The core mechanism is RadixAttention: the KV cache is indexed as a radix tree over token sequences, with LRU eviction. This means shared prefixes — a RAG system prompt, a few-shot block, an agent preamble — are detected and reused automatically, even when requests don’t arrive with identical prefixes. SGLang’s original NeurIPS 2024 paper (arXiv 2312.07104) reports up to 6.4x higher throughput versus state-of-the-art systems on tasks involving repeated-prefix patterns. It’s now the default inference engine for xAI and the production choice for large AMD deployments. If your workload involves RAG, multi-turn agents, or few-shot prompting with shared prefix blocks, SGLang is worth benchmarking against vLLM directly.
TensorRT-LLM needs a release-specific review before selection. The sources cited in this comparison do not establish a universal compilation time or a throughput winner. Make backend, model-loading workflow, supported hardware, and update procedure explicit acceptance criteria for the version being considered; do not assume every release requires the same preparation steps.
Ray Serve operates at a different layer: it’s an orchestration plane that sits above the inference engines, not a replacement for them. The typical production pattern is Ray Serve routing to vLLM or SGLang replicas, handling autoscaling, multi-model routing, and load balancing. Anyscale’s benchmark data shows Ray Serve LLM achieves 4.4x higher throughput on prefill-heavy workloads and up to 24.8x improvement on decode-heavy workloads over its previous architecture, via a combination of direct streaming (decoupling routing from token forwarding) and the vLLM Ray Executor Backend V2. At concurrency 256 on 8× H100s, mean TTFT on prefill-heavy workloads hit 355ms versus vllm-router’s 389ms. If you’re operating multiple models, need per-model autoscaling, or want GPU-aware request routing, Ray Serve is the right abstraction — but you’re still running vLLM or SGLang at the bottom of the stack.
HuggingFace TGI entered maintenance mode on 2025-12-11, and Hugging Face archived the GitHub repository (read-only) on 2026-03-21, so no further upstream fixes, security fixes included, can be merged; plan existing deployments around a pinned, unpatched release. Existing TGI deployments continue to function, but teams should plan migrations to vLLM or SGLang for any workload that needs patches or features shipped after late 2025. TGI’s historical advantage — lower TTFT at low concurrency — has been matched by the continuous batching improvements in vLLM and SGLang.
Choosing by workload class
The framework choice follows directly from what you’re serving:
| Workload | Recommendation |
|---|---|
| Interactive chat, low TTFT required | vLLM or SGLang |
| RAG with shared system prompts | SGLang (RadixAttention) |
| Multi-model routing + autoscaling | Ray Serve + vLLM |
| Single model, fixed NVIDIA fleet | Evaluate TensorRT-LLM’s selected release alongside the other candidates |
| Multi-LoRA adapter serving | vLLM |
| AMD GPU fleet | SGLang |
Deployment acceptance checks
The Anyscale Ray Serve LLM article explains the orchestration architecture and its benchmark setup. It is not a version-pinned deployment specification for this guide. Before deploying, record and validate:
- The exact Ray and inference-engine versions, with the application builder and argument schema documented for that release.
- The model identifier, checkpoint source and revision, tokenizer, supported precision, and available GPU capacity.
- The assembled HTTP route and a successful model-list and inference request through that route.
- One deliberate scaling mode, fixed replicas or autoscaling, with compatible settings for the pinned schema.
- An explicitly configured metrics export port and a successful scrape from that exporter. Do not derive the metrics address from the dashboard URL.
Treat replica-level TTFT, output throughput, queueing, and cache usage as acceptance evidence alongside aggregate request metrics. Keep the validated configuration with the release bundle. For production observability on your inference stack, track KV cache utilization as a first-class metric alongside p99 TTFT; cache pressure appears there before it shows up in latency histograms.
Caveats
Vendor benchmarks require source-checking. The throughput numbers cited in vendor engineering blogs (Anyscale, LMSYS) reflect specific hardware configurations and workload distributions. Run your own benchmark against your actual request distribution before committing to a framework for production. The arXiv paper comparing vLLM and TGI (2511.17593) is an independent study; treat vendor-published numbers as directional.
Quantization changes the benchmark. FP8 throughput numbers don’t translate directly to BF16 or AWQ deployments. Match the quantization config in your benchmark to what you’ll actually run.
Prefix caching only helps if prefixes are long and shared. For workloads with unique-per-request prompts — single-turn queries with no shared preamble — RadixAttention provides little benefit and SGLang’s throughput advantage over vLLM narrows.
Version the preparation workflow. For any candidate, record the selected backend, checkpoint format, target hardware, and preparation steps. Budget those steps from the documented workflow for that release rather than assigning a universal compilation duration.
The framework choice precedes the hosting choice. How much batching an engine can extract from one GPU sets the ceiling on what any hosting shape can achieve, which is why serverless vs dedicated GPU for LLM hosting should be decided after this comparison rather than before it.
Security posture on the serving path. An OpenAI-compatible serving endpoint with no auth layer is a credential-free inference endpoint. Apply token-based authentication (Bearer tokens, API keys) at the gateway layer before any vLLM or SGLang server is reachable from the network. The gateway projects that provide that layer, along with the rest of the open-source stack, are mapped in LLMOps tools on GitHub. The content-level controls that sit behind that gateway are covered in guardrails in the serving path. For a deeper look at how model serving endpoints get attacked, aisec.blog covers prompt injection and agent exploitation vectors that apply directly to production serving configurations.
Related across the network
- CVE-2026-7669: Deserialization Flaw in SGLang’s Tokenizer Loader — ai-alert.org
- CVE Roundup: AI/ML Infrastructure Vulnerabilities — Q1 2026 — ai-alert.org
- AI/ML CVE Roundup: May 2026 — What Got Patched — ai-alert.org
- AI Security Week: May 18, 2026 — aisecdigest.com
- Inference Cost Optimization: Autoscaling, Batching, Spot — mlopsplatforms.com
