diff --git a/inference_rules.adoc b/inference_rules.adoc
index b1e6991..35b1df6 100644
--- a/inference_rules.adoc
+++ b/inference_rules.adoc
@@ -253,8 +253,8 @@ The Datacenter suite includes the following benchmarks:
 |Vision |Object detection |Retinanet |OpenImages (800x800) | 64 | 99% of FP32 (0.3755 mAP) | 100 ms
 |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 |GPT-J |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=4016878)| 20 s
-|Language |Question Answering |Llama2 |OpenOrca (GPT-4 split, 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)| TTFT/TPOTfootnote:[For Llama2, 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.]: 2000 ms/200 ms
-|Language |Text Generation (Question Answering, Math and Code Generation) |Mixtral-8x7B |OpenOrca (5k samples of the GPT-4 split, max_seq_len=2048), GSM8K (5k samples of the validation split, max_seq_len=2048), MBXP (5k samples of the validation split, max_seq_len=2048) | 15000 | 99% of FP32 and 99.9% of FP32 (rouge1=45.5989, rouge2=23.3526, 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/TPOTfootnote:[For Mixtral-8x7B, 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.]: 2000 ms/200 ms
+|Language |Question Answering |Llama2 |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)| TTFT/TPOTfootnote:[For Llama2, 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.]: 2000 ms/200 ms
+|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/TPOTfootnote:[For Mixtral-8x7B, 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.]: 2000 ms/200 ms
 |Commerce |Recommendation |DLRMv2 |Synthetic Multihot Criteo Dataset | 204800 |99% of FP32 and 99.9% of FP32 (AUC=80.31%) | 60 ms
 |Generative |Text to image |SDXL |Subset of coco-2014 val | 5000 |FID range: [23.01085758, 23.95007626] and CLIP range: [31.68631873, 31.81331801] | 20 s
 |===
@@ -1026,8 +1026,8 @@ Datacenter systems must provide at least the following bandwidths from the netwo
 |Vision |3D UNET | KiTS 2019 | __avg(C*D*H*W)*dtype_size__footnote:3d_unet_bw[The average image size above is the average image size of the inference cases specified in https://github.com/mlcommons/inference/blob/master/vision/medical_imaging/3d-unet-kits19/meta/inference_cases.json[inference_cases.json].] | __32944795*dtype_size__ | __throughput*32944795*dtype_size__
 |Language |BERT |SQuAD v1.1 (max_seq_len=384) | __num_inputs*max_seq_len*dtype_size__ | __3*384*dtype_size__ | __throughput*1152*dtype_size__
 |Language |GPT-J |CNN Dailymail (v3.0.0, max_seq_len=2048) | __num_inputs*max_seq_len*dtype_size__ | __2048*dtype_size__ | __throughput*2048*dtype_size__
-|Language |Llama2 |OpenOrca (GPT-4 split, max_seq_len=1024) | __num_inputs*max_seq_len*dtype_size__ | __1024*dtype_size__ | __throughput*1024*dtype_size__
-|Language |Mixtral-8x7B |OpenOrca (5k samples of the GPT-4 split, max_seq_len=2048), GSM8K (5k samples of the validation split, max_seq_len=2048), MBXP (5k samples of the validation split, max_seq_len=2048) | __num_inputs*max_seq_len*dtype_size__ | __2048*dtype_size__ | __throughput*2048*dtype_size__
+|Language |Llama2 |OpenOrca (max_seq_len=1024) | __num_inputs*max_seq_len*dtype_size__ | __1024*dtype_size__ | __throughput*1024*dtype_size__
+|Language |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) | __num_inputs*max_seq_len*dtype_size__ | __2048*dtype_size__ | __throughput*2048*dtype_size__
 |Commerce |DLRMv2 | 1TB Click Logs |__avg(num_pairs_per_sample)*(num_numerical_inputs*dtype_size~1~ +num_categorical_inputs*dtype_size~2~))__footnote:[Each DLRMv2 sample consists of up to 700 user-item pairs draw from the distribution specified in https://github.com/mlcommons/inference/blob/master/recommendation/dlrm/pytorch/tools/dist_quantile.txt[dist_quantile.txt].] |__270*(13*dtype_size~1~+26*dtype_size~2~)__ | __throughput*270*(13*dtype_size~1~+26*dtype_size~2~)__
 |Generative |SDXL |Subset of coco-2014 val captions (max_prompt_len=77) | __num_inputs*max_prompt_len*dtype_size__ | __77*dtype_size__ |  __throughput*77*dtype_size__
 |===