TGI is dead. SGLang beats vLLM by 29% on prefix-heavy workloads. TensorRT-LLM needs 28 minutes before its first request. Here is the decision framework that tells you which one to actually run.
In the SGLang vs vLLM debate, SGLang delivers 29% higher throughput on prefix-heavy workloads (16,200 vs 12,500 tokens/second on Llama 3.1 8B on an A100 80GB). TensorRT-LLM needs 28 minutes of compilation before its first request. The right engine depends on what you are actually running, not on which benchmark headline you read first.
Key takeaways
The LLM inference engine landscape consolidated fast. In 2023, the choice was vLLM or custom code. By mid-2026, SGLang, vLLM, and TensorRT-LLM cover the practical decision space for most teams. Each engine makes different trade-offs across cold start time, throughput ceiling, prefix caching efficiency, and operator tooling. This guide compares them on the dimensions that actually affect your GPU bill and deployment complexity.
All throughput figures below are on Llama 3.1 8B at FP16 on an NVIDIA A100 80GB SXM unless otherwise noted. Sources are cited per row.
*TensorRT-LLM figures after compilation. Cold start time not included. Sources: SGLang paper (Zheng et al., 2024), vLLM LMSys benchmarks (2025), NVIDIA TensorRT-LLM documentation and community benchmarks (2025-2026).
SGLang (Structured Generation Language) was released by the LMSys team in late 2024 and has become the throughput leader for workloads with shared prompt prefixes. Its core innovation is RadixAttention: a radix tree structure for KV cache that automatically identifies and reuses shared prefixes across requests.
In practice, this matters most for:
When prefixes are shared, SGLang avoids recomputing KV cache for the shared portion on every request. On RAG workloads where 60-80% of the context is shared, this directly translates to fewer GPU cycles per request and higher effective throughput.
SGLang also has efficient JSON-mode structured output generation, which matters for agent workloads that parse model output into typed objects. It compiles structured output grammars once and reuses them across requests, avoiding the per-request grammar parsing overhead that other engines incur.
SGLang has a smaller operator ecosystem than vLLM. Prometheus metrics, distributed tracing, and Kubernetes-native health checks are available but less mature. If you are running a team that relies on existing vLLM operator tooling, the migration overhead can outweigh the throughput gains for workloads that are not prefix-heavy.
vLLM introduced PagedAttention in 2023, solving KV cache fragmentation that limited GPU utilisation under variable request lengths. It remains the most widely deployed open-source inference engine in 2026, and the default choice for teams that need production stability, ecosystem integration, and operator tooling over maximum throughput.
vLLM’s strengths:
vLLM is the correct default when you cannot predict the prefix distribution of your traffic, when you are switching between multiple models or LoRA adapters, or when your team’s existing tooling is built around vLLM and the migration cost to SGLang is not justified by the expected throughput gain.
vLLM’s prefix caching (added in v0.4) is less aggressive than SGLang’s RadixAttention. On prefix-heavy workloads, SGLang consistently outperforms it. vLLM also has a higher per-request scheduling overhead than TensorRT-LLM on sustained single-model batch workloads.
TensorRT-LLM compiles a model into a TensorRT engine at deployment time. That compilation optimises the model specifically for the GPU, precision, and sequence length configuration you specify. The result is the highest sustained throughput of the three engines on fixed-configuration workloads, roughly 32-40% above vLLM on sustained single-model batch serving.
The constraint is the compilation step. Building a TensorRT engine for Llama 3.1 8B on an A100 takes approximately 28 minutes. You cannot switch models without recompiling. You cannot change sequence length limits without recompiling. This makes TensorRT-LLM practical only for:
For teams running on packet.ai on-demand instances, the 28-minute compilation time means TensorRT-LLM is only economical if the instance runs for many hours. At $1.43/hr for an A100, 28 minutes of idle compilation costs $0.67 before you serve a single request. For burst workloads or multi-model serving, TensorRT-LLM is the wrong choice.
Use SGLang if:
Use vLLM if:
Use TensorRT-LLM if:
Engine choice and GPU choice are separate decisions, but they interact. SGLang’s RadixAttention benefit scales with VRAM: more VRAM means larger prefix caches and higher hit rates on long-context workloads. B200’s 192 GB gives SGLang significantly more cache headroom than H100’s 80 GB.
For inference clusters, see our guide to GPU cluster configurations on packet.ai, which covers multi-node SGLang and vLLM deployments with InfiniBand fabric.
pip install sglang[all]
python -m sglang.launch_server \
--model-path meta-llama/Llama-3.1-8B-Instruct \
--port 30000 \
--enable-prefix-cachingpip install vllm
vllm serve meta-llama/Llama-3.1-8B-Instruct \
--port 8000 \
--enable-prefix-caching# Requires NVIDIA container toolkit
docker pull nvcr.io/nvidia/tritonserver:24.12-trtllm-python-py3
# Convert model (28-minute compilation on A100)
trtllm-build --checkpoint-dir ./llama-3.1-8b \
--output-dir ./engine \
--gemm-plugin float16Last reviewed: July 2026. Deploy SGLang, vLLM, or TensorRT-LLM on packet.ai: A100 80GB from $1.43/hr or B200 SXM from $3.75/hr. For inference clusters, see GPU cluster configurations on packet.ai.
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