Online Inference at Sub-100ms: vLLM vs Triton vs TGI in 2026

LLM inference at scale is engineering work. A naive deployment can produce time-to-first-token in seconds and total response time in tens of seconds; an optimized deployment of the same model produces TTFT in tens of milliseconds and substantial throughput. The difference comes from inference server choice and configuration. Three servers dominate production deployments in 2026: vLLM (the open-source leader), NVIDIA Triton (the enterprise option), and TGI (Hugging Face’s offering). This post walks through trade-offs and where each fits.

What “sub-100ms inference” actually means#

A few definitional points.

Time to first token (TTFT) — time from request to first generated token. For interactive applications, this is the dominant latency component because it determines when the user starts seeing output.

Inter-token latency — time between successive tokens once generation has started. Determines the perceived “streaming speed.”

Total response time — TTFT plus inter-token-latency × generated tokens. Determines when the response is complete.

Throughput — tokens per second the server can produce across all requests.

Sub-100ms typically refers to TTFT for interactive applications. Inter-token latency targets are usually 20-50ms; total response time depends on output length.

The same hardware can produce dramatically different metrics depending on inference server configuration. The choice matters.

vLLM#

vLLM is the open-source inference server developed at UC Berkeley, now widely deployed. Pioneered PagedAttention — efficient KV-cache management that produces substantial throughput gains over naive implementations.

Strengths in 2026:

• PagedAttention — substantial throughput improvement, particularly for variable-length workloads.
• Continuous batching — substantial throughput improvement over naive batching.
• Substantial model coverage — most popular open-weights models supported.
• Active development — substantial ongoing improvements.
• Cost — open-source.

Trade-offs:

• Self-managed operations — you operate the deployment.
• Tuning complexity — getting optimal performance requires understanding the parameters.

Best for: most production open-weights LLM deployments. The default choice for self-hosted inference.

NVIDIA Triton#

Triton is NVIDIA’s enterprise inference server. Broader than LLM-specific — handles any model type with substantial integration with NVIDIA’s broader stack.

Strengths in 2026:

• TensorRT-LLM integration — substantial performance with NVIDIA-optimized kernels.
• Multi-framework support — beyond LLMs to other model types.
• Enterprise tooling — Triton Inference Server has substantial enterprise tooling.
• NVIDIA ecosystem integration — TensorRT, Triton, NeMo, NIM all integrated.
• Production reliability — Triton has substantial production deployment history.

Trade-offs:

• NVIDIA-specific — locks you to NVIDIA GPU infrastructure.
• Setup complexity — more substantial than vLLM for LLM-specific deployments.
• TensorRT-LLM compilation — produces excellent performance but adds workflow complexity.

Best for: enterprises with substantial NVIDIA infrastructure who want NVIDIA-optimized performance and don’t mind the tighter coupling.

TGI (Text Generation Inference)#

TGI is Hugging Face’s inference server, designed specifically for LLM serving.

Strengths in 2026:

• Hugging Face integration — works seamlessly with HF Hub models.
• Modern architecture with continuous batching and other optimizations.
• Reasonable defaults — works well out of the box.
• HF Endpoints — TGI underlies HF’s hosted endpoint product.

Trade-offs:

• Less performance ceiling than vLLM or Triton+TensorRT-LLM in many benchmarks.
• Smaller community than vLLM.

Best for: teams heavily in the Hugging Face ecosystem; smaller-scale deployments where vLLM tuning overhead isn’t justified.

The TensorRT-LLM dimension#

A specific NVIDIA technology worth understanding: TensorRT-LLM. NVIDIA’s compilation framework that produces highly-optimized inference kernels for specific models on specific GPUs.

The trade-off: substantial performance gains (often 30-50% over vLLM on equivalent hardware) at the cost of compilation step and workflow integration.

For deployments at substantial scale where every percentage of performance matters, TensorRT-LLM through Triton is the highest-performance option. For most deployments, vLLM’s simpler workflow plus competitive performance is the right balance.

The hardware story#

Inference server choice interacts with hardware.

NVIDIA H100/H200/B100/B200 — all three servers run; NVIDIA-optimized servers (Triton+TensorRT-LLM) can extract maximum performance.

NVIDIA A100, A10, L40S — all three servers run, with somewhat different optimization profiles.

AMD MI300 — vLLM and TGI support; Triton support exists but is less mature.

Inference-specialized hardware — Groq, Cerebras, plus various — typically have their own inference stacks rather than vLLM/Triton/TGI.

What we typically see at clients#

Common patterns:

No optimization. Default Hugging Face Transformers deployment with no batching. Produces TTFT of seconds and substantial under-utilization.

vLLM with default config. Better than nothing; significantly worse than tuned. Common at teams that adopted vLLM without learning the tuning.

vLLM with tuning. This is where most production deployments land. The configuration work pays off substantially.

Triton + TensorRT-LLM at large-scale deployments where maximum performance matters.

Multi-server architectures — some teams use different servers for different workloads (vLLM for general, TensorRT-LLM for specific high-volume models).

The decision framework#

For most production teams in 2026:

Pick vLLM for the default. Open-source, broadly supported, competitive performance.

Pick Triton + TensorRT-LLM for largest-scale NVIDIA-anchored deployments where maximum performance matters.

Pick TGI for Hugging Face-ecosystem-anchored deployments at smaller scale.

Pick cloud-managed (Anthropic Bedrock, OpenAI, Together, Modal, plus the various) when self-hosting isn’t justified.

Where pdpspectra fits#

Our MLOps practice builds production inference platforms with appropriate inference server selection and tuning.

Related reading: the quantization post, the GPU cost post, and the LLM cost optimization post.

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Inference server choice determines latency. Talk to our team about your AI infrastructure.