--- # SPDX-FileCopyrightText: Copyright (c) 2026 NVIDIA CORPORATION & AFFILIATES. All rights reserved. # SPDX-License-Identifier: Apache-2.0 sidebar-title: Profile with InstructCoder Dataset --- # Profile with InstructCoder Dataset AIPerf supports benchmarking using the InstructCoder dataset (`likaixin/InstructCoder`), which contains code editing and generation instructions. This dataset is useful for measuring model throughput and latency under code generation workloads. This guide covers profiling OpenAI-compatible chat completions endpoints using the InstructCoder public dataset. --- ## Start a vLLM Server Launch a vLLM server with a chat model: ```bash docker pull vllm/vllm-openai:latest docker run --gpus all -p 8000:8000 vllm/vllm-openai:latest \ --model Qwen/Qwen3-0.6B ``` Verify the server is ready: ```bash curl -s localhost:8000/v1/chat/completions \ -H "Content-Type: application/json" \ -d '{"model":"Qwen/Qwen3-0.6B","messages":[{"role":"user","content":"test"}],"max_tokens":1}' ``` --- ## Profile with InstructCoder Dataset AIPerf loads the InstructCoder dataset from HuggingFace and uses each instruction as a single-turn prompt. ```bash aiperf profile \ --model Qwen/Qwen3-0.6B \ --endpoint-type chat \ --streaming \ --url localhost:8000 \ --public-dataset instruct_coder \ --output-tokens-mean 1024 \ --request-count 10 \ --concurrency 4 ``` **Sample Output (Successful Run):** This sample was captured without `--output-tokens-mean`, so its output sequence lengths can exceed the 1024-token mean set above. ``` NVIDIA AIPerf | LLM Metrics ┏━━━━━━━━━━━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━┳━━━━━━━━━━━┓ ┃ Metric ┃ avg ┃ min ┃ max ┃ p99 ┃ p90 ┃ p50 ┃ std ┃ ┡━━━━━━━━━━━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━╇━━━━━━━━━━━┩ │ Time to First Token │ 467.66 │ 276.26 │ 727.41 │ 727.40 │ 727.36 │ 320.26 │ 202.34 │ │ (ms) │ │ │ │ │ │ │ │ │ Time to Second │ 58.66 │ 51.91 │ 70.72 │ 70.37 │ 67.24 │ 54.80 │ 7.30 │ │ Token (ms) │ │ │ │ │ │ │ │ │ Time to First │ 35,962.41 │ 18,843.60 │ 82,092.30 │ 79,669.08 │ 57,860.07 │ 30,302.17 │ 18,641.22 │ │ Output Token (ms) │ │ │ │ │ │ │ │ │ Request Latency │ 49,546.08 │ 23,752.73 │ 99,184.28 │ 95,994.62 │ 67,287.65 │ 47,369.35 │ 20,292.88 │ │ (ms) │ │ │ │ │ │ │ │ │ Inter Token Latency │ 60.88 │ 43.09 │ 67.81 │ 67.80 │ 67.68 │ 64.09 │ 7.40 │ │ (ms) │ │ │ │ │ │ │ │ │ Output Token │ 16.73 │ 14.75 │ 23.21 │ 22.85 │ 19.64 │ 15.60 │ 2.49 │ │ Throughput Per User │ │ │ │ │ │ │ │ │ (tokens/sec/user) │ │ │ │ │ │ │ │ │ Output Sequence │ 849.60 │ 383.00 │ 2,295.00 │ 2,184.12 │ 1,186.20 │ 709.50 │ 512.81 │ │ Length (tokens) │ │ │ │ │ │ │ │ │ Input Sequence │ 15.20 │ 11.00 │ 21.00 │ 20.82 │ 19.20 │ 14.50 │ 3.31 │ │ Length (tokens) │ │ │ │ │ │ │ │ │ Output Token │ 47.46 │ N/A │ N/A │ N/A │ N/A │ N/A │ N/A │ │ Throughput │ │ │ │ │ │ │ │ │ (tokens/sec) │ │ │ │ │ │ │ │ │ Request Throughput │ 0.06 │ N/A │ N/A │ N/A │ N/A │ N/A │ N/A │ │ (requests/sec) │ │ │ │ │ │ │ │ │ Request Count │ 10.00 │ N/A │ N/A │ N/A │ N/A │ N/A │ N/A │ │ (requests) │ │ │ │ │ │ │ │ └─────────────────────┴───────────┴───────────┴───────────┴───────────┴───────────┴───────────┴───────────┘ ```