docs: update doc (#713)
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@@ -29,6 +29,9 @@ python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-8B-Instruc
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# Meta-Llama-3.1-70B-Instruct
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python -m sglang.launch_server --model-path meta-llama/Meta-Llama-3.1-70B-Instruct --disable-radix-cache --tp 8
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# Meta-Llama-3-70B-Instruct-FP8
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python -m sglang.launch_server --model-path neuralmagic/Meta-Llama-3-70B-Instruct-FP8 --disable-radix-cache --tp 8
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```
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## Benchmark
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@@ -59,19 +62,19 @@ cat sglang_offline_benchmark.jsonl | cut -d':' -f12 | cut -d',' -f1
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#### Online benchmark
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```bash
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# Random dataset, Input [1024, 4096], Output [256, 1024], request rate 1, num prompts 300
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# Random dataset, Input [512, 4096], Output [128, 1024], request rate 1, num prompts 300
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python3 -m sglang.bench_serving --backend sglang --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 300 --request-rate 1 --output-file sglang_online_benchmark.jsonl
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# Random dataset, Input [1024, 4096], Output [256, 1024], request rate 2, num prompts 600
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# Random dataset, Input [512, 4096], Output [128, 1024], request rate 2, num prompts 600
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python3 -m sglang.bench_serving --backend sglang --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 600 --request-rate 2 --output-file sglang_online_benchmark.jsonl
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# Random dataset, Input [1024, 4096], Output [256, 1024], request rate 4, num prompts 1200
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# Random dataset, Input [512, 4096], Output [128, 1024], request rate 4, num prompts 1200
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python3 -m sglang.bench_serving --backend sglang --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 1200 --request-rate 4 --output-file sglang_online_benchmark.jsonl
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# Random dataset, Input [1024, 4096], Output [256, 1024], request rate 8, num prompts 2400
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# Random dataset, Input [512, 4096], Output [128, 1024], request rate 8, num prompts 2400
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python3 -m sglang.bench_serving --backend sglang --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 2400 --request-rate 8 --output-file sglang_online_benchmark.jsonl
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# Random dataset, Input [1024, 4096], Output [256, 1024], request rate 16, num prompts 3200
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# Random dataset, Input [512, 4096], Output [128, 1024], request rate 16, num prompts 3200
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python3 -m sglang.bench_serving --backend sglang --dataset-name random --random-input 4096 --random-output 1024 --random-range-ratio 0.125 --num-prompts 3200 --request-rate 16 --output-file sglang_online_benchmark.jsonl
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# get median e2e latency
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