Inference serving in 2026: vLLM, TGI, SGLang, and the choice that matters

For teams self-hosting LLMs, the inference server is one of the largest infrastructure decisions. The choice affects throughput, latency, GPU utilization, and what features (continuous batching, quantization, speculative decoding) you can use.

In 2026 the major options are vLLM, Text Generation Inference (TGI), SGLang, TensorRT-LLM, and a few others. Each has tradeoffs; the right choice depends on your workload and team.

This essay is a practical comparison and the criteria that actually drive the decision.

The frameworks

A quick survey.

vLLM. Open-source, mature, widely adopted. Excellent throughput on common hardware. PagedAttention for memory efficiency. Good support for many model architectures.

Text Generation Inference (TGI). From Hugging Face. Focused on HF-hosted models. Production-grade with telemetry, tracing. Slightly behind vLLM on some throughput metrics; ahead on integration with HF ecosystem.

SGLang. Newer; strong on structured generation, complex generation programs (reasoning chains, structured outputs). Useful when output structure matters as much as throughput.

TensorRT-LLM. From NVIDIA. Highest performance on NVIDIA GPUs but more complex to deploy. Good when you can afford the operational complexity.

Triton Inference Server. General-purpose; supports LLMs via integrations. Worth considering if you’re already running Triton for other ML.

Cloud-managed serving. AWS Bedrock self-hosted, Azure ML, etc. Removes ops burden at the cost of control and per-token cost.

The decision factors

Several factors drive the choice.

Factor 1: throughput vs latency. Higher throughput per GPU lowers cost; lower latency improves UX. The frameworks differ in how they balance these.

Factor 2: model support. Some frameworks support all popular open models; some have narrower support. Confirm yours is supported.

Factor 3: feature support. Continuous batching, quantization, speculative decoding, structured generation, multi-LoRA. Different frameworks have different feature sets.

Factor 4: ops complexity. Some frameworks are easy to deploy (containerized, autoscaling); some require careful tuning.

Factor 5: ecosystem fit. If you’re on Hugging Face’s stack, TGI fits. If you’re on Triton/NVIDIA, TensorRT-LLM. If you want maximum flexibility, vLLM.

When to pick vLLM

Default for most teams self-hosting open-source models.

Pros:

• Strong throughput with good GPU utilization
• Widely deployed; lots of tribal knowledge available
• Active development, frequent releases
• Supports most popular models and quantization formats

Cons:

• Operational complexity is real (tuning batch sizes, memory)
• Some advanced features (speculative decoding) are still maturing

For teams that want a solid middle-ground choice, vLLM is usually right.

When to pick TGI

Best for teams already in the Hugging Face ecosystem.

Pros:

• Tight integration with HF model hub
• Good telemetry / observability built-in
• Easy deployment for HF-hosted models

Cons:

• Throughput slightly behind vLLM on some workloads
• Smaller community than vLLM

If you’re using HF for model selection, training, or inference SaaS, TGI is the natural extension.

When to pick SGLang

Worth considering when output structure matters.

Pros:

• Strong structured generation support
• Allows complex generation programs (multi-step chains, branching) at the inference server layer
• Competitive throughput

Cons:

• Newer; smaller community
• Less battle-tested than vLLM

For products that lean heavily on structured output, agent-like workflows, or multi-step generation, SGLang has unique advantages.

When to pick TensorRT-LLM

For maximum performance on NVIDIA hardware, when you can pay the ops cost.

Pros:

• Highest throughput per GPU on NVIDIA hardware
• Excellent quantization support
• Mature; used in many production deployments

Cons:

• Operational complexity is meaningful
• NVIDIA-specific; reduces flexibility
• Steeper learning curve

For teams running serious volume on NVIDIA fleets, TensorRT-LLM’s performance gains can justify the complexity.

When to use managed serving

For teams that don’t want the ops burden.

Pros:

• No GPU management
• Autoscaling handled
• Patches and updates managed

Cons:

• Per-token cost higher than self-hosting at scale
• Less control over specific model versions and configurations
• Vendor lock-in

For low-to-medium volume, managed serving is often the right call. The savings of self-hosting don’t materialize until you have meaningful sustained throughput.

What to actually benchmark

Don’t just trust framework benchmarks. Benchmark on your data.

Pattern:

1. Pick 2-3 candidate frameworks
2. Set up each with the model you’ll actually use
3. Run a workload representative of your production traffic (similar prompt lengths, similar concurrency)
4. Measure: tokens/sec throughput, p50/p95/p99 latency, GPU utilization
5. Pick based on results

The benchmark might take a week. It’s worth it; the picks based on framework marketing pages often disappoint.

Quantization

Modern inference servers support quantization (running models at lower precision: INT8, INT4, FP8) for better throughput and lower memory.

Tradeoffs:

• INT8 / FP8: small quality loss, significant throughput improvement
• INT4: noticeable quality loss on some tasks, large throughput improvement
• FP16: no quality loss, baseline performance

For high-volume serving, quantization is often essential to fit within GPU budgets. Quality drops are usually acceptable for most tasks; verify on your eval bench.

Continuous batching

Continuous batching lets the server process multiple requests simultaneously by interleaving their generation steps. Modern inference servers do this automatically.

The benefit: higher throughput per GPU. Multiple concurrent requests share the same GPU pass.

The catch: latency for individual requests can be slightly higher than dedicated serving because of batching overhead.

For interactive workloads, the latency cost is small (typically under 10ms added). For high-throughput batch workloads, continuous batching is critical for cost efficiency.

Speculative decoding

Speculative decoding uses a smaller “draft” model to propose tokens, which a larger “verifier” model validates. When the draft is right (often), generation is faster.

In 2026, speculative decoding can lift throughput 2-3x for many models. Most modern inference servers support it.

Worth enabling if your framework supports it. The setup involves picking a compatible draft model; verify quality is maintained on your eval bench.

Model loading and switching

For teams running multiple models or fine-tuned variants:

• Some servers support “model registry” with hot-swapping
• Multi-LoRA serving lets you switch between adapters with minimal overhead
• Some servers require restart to switch base models

If you have many fine-tunes or need to swap models frequently, prioritize this capability when picking.

Observability for inference servers

The serving layer is itself infrastructure that needs observability.

Track:

• Throughput (tokens/sec, requests/sec)
• Latency distribution (TTFT, time-to-first-token; total response time)
• GPU utilization
• Memory usage
• Queue depth
• Error rates

These metrics tell you when you’re nearing capacity, when latency is spiking, when something is misconfigured.

Multi-region and HA

For production at meaningful scale, multi-region:

• Deploy serving in multiple regions
• Route requests to the nearest healthy region
• Failover when a region has issues

This is more infrastructure but necessary for products with users worldwide and reliability requirements.

The cost picture

Self-hosted serving cost is dominated by GPU.

Rough numbers (2026):

• GPU hourly rate: $1-5/hour depending on type and provider
• Tokens per hour at full GPU utilization: tens of millions for mid-size models
• Per-million-token cost: $0.01-0.10 at high utilization

Compare to API: $0.5-30 per million tokens depending on model.

The break-even is typically at hundreds of millions of tokens per month sustained. Below that, API is cheaper. Above that, self-hosted pencils out.

When to self-host vs API

Decision factors:

• Volume: high sustained volume favors self-hosted
• Data residency: required residency favors self-hosted
• Latency: tightly co-located self-hosted can be lower latency
• Customization: fine-tuned models often require self-hosted
• Operational maturity: if you don’t have GPU ops experience, API is safer

For most teams under 100M tokens/month, API is the right call. Above that, self-hosted starts to win on cost.

The take

Inference server choice matters when you’re self-hosting. vLLM is the strong default. TGI fits HF-centric stacks. SGLang for structured generation. TensorRT-LLM for max performance with the ops to support it.

Benchmark on your actual workload. Use quantization and continuous batching. Plan for multi-region if you need reliability.

The teams running large self-hosted LLM infrastructure picked their inference server deliberately and benchmarked. The teams that struggle often picked by hype and didn’t measure.