vLLM vs TGI vs SGLang: Inference Engines Compared

CallMissedMay 8, 2026

·5 min readComparison

Inference LLM AI Infrastructure Performance

If you self-host an LLM, the inference engine is the single highest-leverage piece of infrastructure you choose. By 2026 the decision has narrowed: most teams pick vLLM, some pick SGLang for prefix-heavy workloads, and TGI has entered maintenance mode. Here is the picture.

TGI: end of an era

Hugging Face's Text Generation Inference was the default OSS inference server for years. As reported in late-2025 announcements summarized by industry blogs, TGI entered maintenance mode in December 2025, with Hugging Face recommending vLLM or SGLang for new deployments. (premai 2026 comparison) [Unverified — secondhand source]

What this means in practice:

TGI still works for existing deployments

New optimizations (FP8 kernels, latest sampling algorithms) land in vLLM and SGLang first

For new builds in 2026, TGI is no longer the default choice

vLLM: the broad default

vLLM's defining innovation is PagedAttention, which breaks the KV cache into fixed-size blocks (typically 16 tokens) that can be allocated anywhere in GPU memory. The result: under 4% memory waste and much larger effective batch sizes than legacy servers. (premai)

Reported throughput on Llama-2-7B at 100 concurrent requests: vLLM at ~15,243 tokens/sec versus TGI at ~4,156 — a roughly 3.7× gap. ([premai]) [Unverified — single benchmark]

What vLLM is good at:

Broad model coverage — most popular open models work out of the box

Continuous batching — production-grade scheduler

Quantization — first-class support for AWQ, GPTQ, FP8, GGUF

Documentation and community — the largest of the three by a wide margin

OpenAI-compatible server — drop-in for OpenAI client SDKs

What vLLM is less good at:

Shared-prefix workloads at extreme scale — SGLang's RadixAttention pulls ahead here

Custom kernel optimization — TensorRT-LLM beats it for "compile once, serve forever" production [Inference]

SGLang: the prefix-cache specialist

SGLang's defining innovation is RadixAttention, which stores KV cache entries in a radix tree indexed at the token level. The tree automatically discovers shared prefixes across requests, so multi-turn conversations and RAG workloads see massive speedups for the second-and-later request. ([premai])

Reported numbers: ~16,200 tok/s versus vLLM's ~12,500 on the same model — roughly 29% throughput advantage on prefill-heavy workloads. The advantage shrinks at 70B scale (3–5%) because decode dominates the cost there, and grows at 8B scale because prefill is a larger fraction of total cost. ([premai]) [Unverified]

What SGLang is good at:

Multi-turn / agentic workloads — every conversation reuses the prefix tree

RAG with consistent system prompts — same idea

Structured generation — SGLang has strong support for grammar-constrained decoding

What SGLang is less good at:

Smaller model coverage than vLLM (closing fast, but historically narrower)

Smaller community — fewer issues with public answers when something breaks

TensorRT-LLM: the "compile once" specialist

Mentioned for completeness. NVIDIA's TensorRT-LLM compiles the model graph aggressively for a target GPU, producing the highest possible tokens/sec at the cost of compilation time and reduced flexibility.

Use it when you have a model that will not change for months, you need to squeeze every token per second, and you have NVIDIA infrastructure expertise. Otherwise vLLM or SGLang is faster to iterate. [Inference]

Practical decision matrix

Workload pattern	Recommendation
New deployment, mixed workloads, broad model coverage	vLLM
Multi-turn agents, conversation history, shared system prompts	SGLang
Single model, frozen for months, max throughput	TensorRT-LLM
Existing TGI deployment	Keep on TGI; plan migration to vLLM
Mac / CPU / on-device	llama.cpp (different category)

Configuration choices that actually matter

Both vLLM and SGLang expose a similar set of knobs that move performance more than picking between them:

--max-num-seqs — concurrent sequences. Higher = more throughput, more memory. Tune to fill GPU memory.

--max-model-len — context window. Set to the actual ceiling you serve, not the model max — KV cache grows with this.

--gpu-memory-utilization — typically 0.85–0.92 in production. Higher squeezes more in but risks OOM on long inputs.

--enable-prefix-caching (vLLM) or default in SGLang — turn on if you have shared prefixes.

Tensor parallelism vs pipeline parallelism — for multi-GPU, TP is usually better latency, PP is better throughput at very long contexts.

Quantization choices

Both engines support most popular quantization formats in 2026:

AWQ — fast (Marlin kernel), excellent quality, well supported

GPTQ — older, slightly lower quality than AWQ but broad support

FP8 — native on H100/H200/B200, near-zero quality loss

GGUF — not the right format for vLLM/SGLang; use llama.cpp

For most production deployments in 2026: FP8 if your hardware supports it, AWQ otherwise.

Migration cost

Moving from TGI → vLLM is typically a 1–3 day exercise: identical OpenAI-compatible HTTP API, model paths port directly, deployment YAML reshapes. The bigger work is re-tuning batching parameters and re-establishing your throughput baseline.

vLLM ↔ SGLang is similarly portable. Both speak OpenAI-compatible APIs; configuration knobs differ but the model itself does not change.

Bottom line

For most teams in 2026: start with vLLM. It covers the widest model surface, has the best documentation, and delivers throughput that is competitive on most workloads. Move to SGLang when you measure shared-prefix workloads (multi-turn agents, RAG with stable prompts) and confirm the prefix-caching advantage materializes for your traffic. Consider TensorRT-LLM only when latency or throughput is the absolute primary concern and the model will sit still long enough to amortize compilation cost.

Frequently Asked Questions

Should I still use TGI in 2026?

For existing deployments it works. For new builds, vLLM or SGLang is the recommendation, since TGI entered maintenance mode in late 2025 and new optimizations land in the other engines first.

When does SGLang win over vLLM?

On workloads with shared prefixes — multi-turn conversations, RAG with stable system prompts, agent loops with consistent tool definitions. Reported throughput advantage is around 29% on small models with prefill-heavy traffic; it shrinks at 70B+ scale.

What about Triton and TensorRT-LLM?

Triton is a serving framework that can host TensorRT-LLM, vLLM, or other backends; it is orthogonal to the engine choice. TensorRT-LLM gives the highest raw throughput once compiled but trades flexibility — pick it when the model is frozen and absolute performance matters.