--- title: "Benchmarking Enterprise AI: vLLM vs SGLang vs TensorRT-LLM" author: "Acadify Engineering Team" author_role: "AI & Software Engineering Team" date: "September 25, 2026" categories: [Enterprise AI] description: "Compare vLLM, SGLang, and TensorRT-LLM on H100 workloads using latency, throughput, memory, batching, and production serving considerations. For production AI." --- # Benchmarking Enterprise AI: vLLM vs SGLang vs TensorRT-LLM By **Acadify Engineering Team** (AI & Software Engineering Team) on September 25, 2026 ## Benchmark Test Environment Specifications A reproducible H100 benchmark must document the exact GPU configuration, host CPU and memory, driver and CUDA versions, serving-engine versions, model checkpoint, precision or quantization, context length, output length, concurrency, batching configuration, and workload mix. The original article did not provide enough methodology or raw benchmark evidence to validate its numerical results, so those figures should not be treated as independently reproduced measurements. ## Benchmark Test Environment Specifications A reproducible H100 benchmark must document the exact GPU configuration, host CPU and memory, driver and CUDA versions, serving-engine versions, model checkpoint, precision or quantization, context length, output length, concurrency, batching configuration, and workload mix. The original article listed environment values but did not provide enough methodology or raw benchmark evidence to validate its numerical results, so those figures should not be treated as independently reproduced measurements. ## Latency Breakdown Report time to first token (TTFT), inter-token latency, end-to-end latency, and tail percentiles such as p95 and p99. The original numerical table was not accompanied by a reproducible benchmark artifact, so use this measurement framework instead of presenting those values as verified comparative data. MetricRecommended reportingWhy it mattersTTFTp50, p95, p99Shows how quickly generation begins.Inter-token latencyp50, p95, p99Shows streaming generation behavior.End-to-end latencyp50, p95, p99Captures total request experience.Throughputtokens/sec and requests/secShows capacity under a defined workload. ## Latency Breakdown Report time to first token (TTFT), inter-token latency, end-to-end latency, and tail percentiles such as p95 and p99. The numerical table in the original version was not accompanied by a reproducible benchmark artifact, so it has been replaced with a measurement framework rather than presented as verified comparative data. MetricRecommended reportingWhy it mattersTTFTp50, p95, p99Shows how quickly generation begins.Inter-token latencyp50, p95, p99Shows streaming generation behavior.End-to-end latencyp50, p95, p99Captures total request experience.Throughputtokens/sec and requests/secShows capacity under a defined workload. ## Full HTML Benchmark Comparison Table Remove any comparative numbers unless they are backed by a reproducible benchmark artifact containing the exact workload, versions, configuration, and raw measurements. A methodology-first article is more reliable than an unsupported static ranking. ## Memory Footprint Report peak and steady-state GPU memory under the same model, precision, context, concurrency, and serving configuration. Do not reuse the previous VRAM or KV-cache figures without a reproducible measurement record. ## Final Verdict & Production Recommendations Do not convert one benchmark into a universal production recommendation. The appropriate serving engine depends on workload, model, concurrency, latency target, throughput target, memory constraints, supported features, observability, and operational requirements. ### Can one H100 benchmark be generalized? No. Hardware, software versions, model configuration, workload, concurrency, and serving settings can materially change results. ### Should average latency be the primary metric? No. Report p50, p95, and p99 alongside throughput and TTFT. ### Is the fastest engine always the best production choice? No. Operational requirements, supported features, memory behavior, observability, and workload-specific results also matter. ## Key Takeaways - Benchmark engines under identical documented conditions. - Report TTFT, inter-token latency, throughput, memory, and tail latency. - Use multiple concurrency levels and repeated trials. - Treat results as workload-specific evidence. **Technical references:** [vLLM documentation](https://docs.vllm.ai/), [SGLang documentation](https://docs.sglang.ai/), and [NVIDIA TensorRT-LLM documentation](https://nvidia.github.io/TensorRT-LLM/). ## How Should You Interpret the Results? Do not convert one benchmark into a universal production recommendation. The appropriate serving engine depends on the workload, model, concurrency, latency target, throughput target, memory constraints, supported features, observability, and operational requirements. ## Frequently Asked Questions ### Can one H100 benchmark be generalized? No. Hardware, software versions, model configuration, workload, concurrency, and serving settings can materially change results. ### Should average latency be the primary metric? No. Report p50, p95, and p99 alongside throughput and TTFT. ### Is the fastest engine always the best production choice? No. Operational requirements, supported features, memory behavior, observability, and workload-specific results also matter. ## Key Takeaways - Benchmark engines under identical documented conditions. - Report TTFT, inter-token latency, throughput, memory, and tail latency. - Use multiple concurrency levels and repeated trials. - Treat results as workload-specific evidence. **Technical references:** [vLLM documentation](https://docs.vllm.ai/), [SGLang documentation](https://docs.sglang.ai/), and [NVIDIA TensorRT-LLM documentation](https://nvidia.github.io/TensorRT-LLM/). --- ### About the Author **Acadify Engineering Team** Acadify Engineering Team is the technical team behind Acadify Solution’s AI, software engineering, cloud, automation, and product development work. We publish practical, research-informed insights based on our engineering experience across AI systems, LLM applications, software development, cloud infrastructure, automation, AI testing and evaluation, and digital product engineering. Our content is designed to help founders, engineering teams, technology leaders, and businesses understand complex technical topics and make informed decisions about building, deploying, and improving software and AI systems.