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 H100 - but vLLM remains the right default for 80% of teams. TensorRT-LLM posts the fastest raw numbers on NVIDIA hardware if you can absorb a 28-minute engine build per model version.
Key takeaways
Picking an inference engine used to be simple. You ran vLLM. Maybe TGI if you were deep in the HuggingFace stack. That decision is now more complex, and the stakes are higher. SGLang has closed the throughput gap with vLLM and surpassed it on several workload types. TGI is no longer an active project. TensorRT-LLM delivers the highest raw numbers on NVIDIA hardware - if you can build and maintain a compiled engine pipeline. This post cuts through the benchmarks to give you a clear pick-X-when decision for each of the three engines that actually matter in production LLM inference in 2026.
This post belongs to the LLM inference cluster on packet.ai. If you already know which engine you want and need a deploy walkthrough, see the vLLM deployment tutorial or vLLM Docker guide. For cost context, see LLM inference cost in 2026.
HuggingFace placed TGI into maintenance mode on December 11, 2025. Their official README now states: "Going forward, we will accept pull requests for minor bug fixes, documentation improvements and lightweight maintenance tasks." HuggingFace Inference Endpoints - the managed serving product - now defaults to vLLM, with SGLang as the alternative. TGI will not receive new model support, performance improvements, or feature development. The repository is archived read-only.
This is not a deprecation warning. It is a completed deprecation. If your team is running TGI today, it still works for models it already supports. But any new model you want to serve, any performance gain from 2026 onward, and any support from the upstream project - those are gone. The migration path HuggingFace recommends is to vLLM first, SGLang second.
Both engines implement continuous batching, paged KV cache, FP8 quantization, and OpenAI-compatible APIs. The feature gap between them closed in 2024. What separates them in 2026 is one architectural decision: how each engine manages the KV cache.
vLLM's PagedAttention treats the KV cache like OS virtual memory. It splits the cache into fixed-size pages allocated on demand and reclaimed when a request finishes. This eliminates the 60-80% VRAM fragmentation that plagued earlier inference engines, and it works predictably across every workload type. The tradeoff: the scheduler runs in Python, adding 200-500 microseconds of overhead per scheduling cycle at high concurrency.
SGLang's RadixAttention goes one step further. Instead of flat block hashing, it builds a radix tree (trie) across all cached KV pages from all requests. When a new request arrives, the engine finds the longest common prefix across every previously cached sequence and reuses those pages - not just from a shared system prompt, but from any prefix seen by any prior request. For RAG pipelines with the same context document, multi-turn conversations, and agent loops with fixed tool-definition blocks, this eliminates 70-90% of prefill computation. For workloads with completely unique prompts, the radix tree lookup adds overhead with no benefit.
SGLang delivers 16,200 tokens/second versus vLLM's 12,500 tokens/second on Llama 3.1 8B on a single H100 80GB GPU - a 29% throughput advantage measured by PremAI on identical hardware. That number is real and reproducible. It is not the number that will apply to your workload unless your workload has heavy shared-prefix traffic.
On Llama 3.3 70B FP8 at 50 concurrent requests on a single H100, Spheron's benchmark shows TensorRT-LLM at 2,100 tok/s, SGLang at 1,920 tok/s, and vLLM at 1,850 tok/s. The gap between all three is under 14%. At 100 concurrent requests, TensorRT-LLM's p95 TTFT is 1,280 ms versus vLLM's 1,450 ms - a 170 ms difference that is meaningful for interactive applications but invisible in batch jobs.
On Jarvislabs' benchmark running Qwen3-30B-A3B (MoE architecture) on ShareGPT-style traffic, vLLM peaks at ~9,360 tok/s at concurrency 360 while TensorRT-LLM under the same engine configuration stays flat at ~5,550 tok/s because TTFT degrades sharply above 180 concurrent requests. Engine configuration for TRT-LLM matters more than engine choice.
On unique-prompt workloads with zero prefix overlap, SGLang's 29% throughput advantage disappears entirely. RunPod tested this directly with DeepSeek-R1-Distill-Llama-70B on single-turn unique prompts: vLLM 60 tok/s, SGLang 52.7 tok/s - vLLM won. The moment cache hits entered the picture (35 vs 32.8 tok/s with cache), SGLang pulled ahead. Measure your prefix overlap before picking an engine.
Pick SGLang if your workload has shared prefixes. This means: RAG pipelines where the same document or few-shot examples appear in every request; multi-turn chat applications where conversation history accumulates into a long shared prefix; agent loops that re-send the same tool-definition block on every call; coding assistants that prefix every completion with the same file context. For DeepSeek V3 and DeepSeek R1 specifically, SGLang is the official inference engine - the DeepSeek team co-developed FP8 support with SGLang and published benchmarks showing 3.1x faster inference than vLLM on V3 due to optimized MLA attention backends.
Pick vLLM if: your prompts are mostly unique (batch jobs, diverse API traffic, varied document summarization); you need to support non-NVIDIA hardware (AMD MI300X, TPU, Intel Gaudi); you want the broadest model coverage without waiting for SGLang to add support; you want the fastest path to a working endpoint with the least operational risk. vLLM's community is three times larger than SGLang's, which means more Stack Overflow answers, more integrations, and faster bug resolution when something breaks in production.
SGLang wins when
vLLM wins when
Both engines expose an OpenAI-compatible API at /v1/chat/completions. Switching between them requires changing the Docker run command, not the application code. If you are unsure, start with vLLM and benchmark SGLang against your actual production traffic before committing.
TensorRT-LLM takes a fundamentally different approach. Instead of interpreting model weights at runtime through a Python scheduler, it compiles the model into a TensorRT engine - a serialized, hardware-specific CUDA execution plan with fused operations and auto-tuned kernel selection. The compiled engine runs in C++ with minimal Python overhead, which is where the throughput gains come from.
The build pipeline for Llama 3.3 70B FP8 on a single H100: quantize the HuggingFace checkpoint (~5 minutes) then compile with trtllm-build (~23 minutes). Every time you change the model, the quantization level, the maximum batch size, or the maximum sequence length, you rebuild. Teams using TRT-LLM build CI pipelines around engine builds and version the .engine files alongside model artifacts.
The PyTorch backend (stable since TRT-LLM v1.0.0, default since v1.2.1) removes the compilation requirement at the cost of roughly 15-20% peak throughput. If you want TRT-LLM's OpenAI-compatible API without the compile step, trtllm-serve with the PyTorch backend starts in ~90 seconds - comparable to vLLM and SGLang - and gives you most of the performance benefit.
Use TensorRT-LLM when: you are committed to NVIDIA hardware (H100, H200, B200, GB200) for the foreseeable future; you are serving a model that will not change frequently; you have engineering bandwidth to maintain a compiled engine CI pipeline; and you need the last 13-15% of throughput that vLLM and SGLang cannot reach on dense models at high concurrency.
Read top to bottom. Stop at the first question that applies to your situation.
Every inference engine benchmarked in this post ran on H100 SXM GPUs. The engine choice interacts with the GPU tier you pick.
packet.ai H100 SXM and H200 SXM clusters are coming soon - join the H100 waitlist or H200 waitlist to get early access. B200 SXM clusters are available now at $3.75/hr per GPU - deploy on-demand in under 5 minutes with packet.ai B200 pricing. For multi-node inference clusters, see packet.ai cluster options.
All three engines install the same way regardless of GPU tier:
# vLLM - one command to a working OpenAI-compatible endpoint
pip install vllm
vllm serve meta-llama/Llama-3.1-8B-Instruct --port 8000
# SGLang - same shape, RadixAttention prefix caching on by default
pip install "sglang[all]"
python -m sglang.launch_server \
--model-path meta-llama/Llama-3.1-8B-Instruct --port 30000
# TensorRT-LLM - PyTorch backend (no compile step, ~90s cold start)
docker run --gpus all -it nvcr.io/nvidia/tensorrt-llm/release:1.2.1
trtllm-serve meta-llama/Llama-3.1-8B-Instruct --port 8000All three answer the same OpenAI client call at /v1/chat/completions. Switching engines is a server-side change - your application code stays identical.
Last reviewed: July 31, 2026. Engine versions: vLLM 0.23.0, SGLang 0.5.13, TensorRT-LLM 1.2.1. Run any of these engines on packet.ai GPU clusters - B200 SXM available now from $3.75/hr, H100 and H200 coming soon. Browse packet.ai cluster options for multi-node inference configurations.
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