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Aug 19, 2026
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Update inference_rules.adoc #338

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2 changes: 1 addition & 1 deletion inference_rules.adoc
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Expand Up @@ -264,7 +264,7 @@ The Datacenter suite includes the following benchmarks:
|Area |Task |Model |Dataset |QSL Size |Quality |Server latency constraint
|Vision |Medical image segmentation |3D UNET |KiTS 2019 | 42 | 99% of FP32 and 99.9% of FP32 (0.86330 mean DICE score) | N/A
|Language |Summarization |Llama3.1-8B |CNN Dailymail (v3.0.0, max_seq_len=2048) | 13368 | 99% of FP32 and 99.9% of FP32 (rouge1=42.9865, rouge2=20.1235, rougeL=29.9881). Additionally, for both cases the total generation length of the texts should be more than 90% of the reference (gen_len=8167644)| Conversational category: TTFT/TPOT: 2000 ms/100 ms. Interactive category: TTFT/TPOT: 500 ms/30 ms.footnote:llm_ttft_tpot[]
|Language |Question Answering |Llama2-70b |OpenOrca (max_seq_len=1024) | 24576 | 99% of FP32 and 99.9% of FP32 (rouge1=44.4312, rouge2=22.0352, rougeL=28.6162). Additionally, for both cases the generation length of the tokens per sample should be more than 90% of the reference (tokens_per_sample=294.45)| Conversational category: TTFT/TPOT: 2000 ms/200 ms. Interactive category: TTFT/TPOT: 450 ms/40 ms.footnote:llm_ttft_tpot[]
|Language |Question Answering |Llama2-70b |OpenOrca (max_seq_len=1024) | 24576 | 99.9% of FP32 (rouge1=44.4312, rouge2=22.0352, rougeL=28.6162). Additionally, the generation length of the tokens per sample should be more than 90% of the reference (tokens_per_sample=294.45)| Conversational category: TTFT/TPOT: 2000 ms/200 ms. Interactive category: TTFT/TPOT: 450 ms/40 ms.footnote:llm_ttft_tpot[]
|Language |Text Generation |Llama3.1-405B |Subset of LongBench, LongDataCollections, Ruler, GovReport | 8313 | 99% of FP16 ((GovReport + LongDataCollections + 65 Sample from LongBench)rougeL=21.6666, (Remaining samples of the dataset)exact_match=90.1335). Additionally, for both cases tokens per sample should be between than 90% and 110% of the reference (tokens_per_sample=684.68)| Server: TTFT/TPOT: 6000 ms/175 ms. Interactive: TTFT/TPOT: 4500 ms/80 ms.footnote:llm_ttft_tpot[]
|Language |Text Generation (Question Answering, Math and Code Generation) |Mixtral-8x7B |OpenOrca (5k samples, max_seq_len=2048), GSM8K (5k samples of the train split, max_seq_len=2048), MBXP (5k samples, max_seq_len=2048) | 15000 | 99% of FP16 ((OpenOrca)rouge1=45.5989, (OpenOrca)rouge2=23.3526, (OpenOrca)rougeL=30.4608, (gsm8k)Accuracy=73.66, (mbxp)Accuracy=60.16). Additionally, for both cases the tokens per sample should be between than 90% and 110% of the reference (tokens_per_sample=144.84)| TTFT/TPOT: 2000 ms/200 ms.footnote:llm_ttft_tpot[]
|Language |Reasoning |DeepSeek-r1 |mlperf_deepseek_r1 | 4388 | 99% of FP16 (exact match 81.9132%).| Server: TTFT/TPOT: 2000 ms/80 ms. Interactive: TTFT/TPOT: 1500 ms/15 ms.footnote:llm_ttft_tpot[For LLM benchmarks, 2 latency metrics are collected - time to first token (TTFT) which measures the latency of the first token, and time per output token (TPOT) which measures the average interval between all the tokens generated.]
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