diff --git a/examples/deepseek/README.md b/examples/deepseek/README.md
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+# Example Analyses with DeepSeek-V3
+
+[DeepSeek-V3 Technical Report](https://arxiv.org/abs/2412.19437)
+
+```sh
+./run_pretrain.sh
+```
diff --git a/examples/deepseek/run_pretrain.sh b/examples/deepseek/run_pretrain.sh
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+# https://huggingface.co/deepseek-ai/DeepSeek-V3/blob/main/configuration_deepseek.py
+
+model_name=deepseek-ai/DeepSeek-V3 # model name (hf model name) or model config json file path
+seq_len=4096
+gpu_name='h800-sxm-80gb'    # python -m llm_analysis.config list_gpu_configs
+dtype_name="w16a16e16l8"      # 16-bit weights, activations for non-linear ops and embedding, 8-bit linear ops
+flops_efficiency=0.25       # mfu
+batch_size_per_gpu=1        # device_train_microbatch_size
+num_gpus=2048               # num_gpus
+activation_recomputation=2  # 0: no activation recomputation; 1: checkpoint attention compute; 2: checkpoint attention ; 3: checkpoint layernorm-attention-layernorm; 4: checkpoint attention the entire transformer layer
+dp_size=128                 # data parallelization size for sharding
+ep_size=64                   # expert parallelization size, moe_dp_sharding_size = dp_size / ep_size
+tp_size=1                   # tensor parallelization size
+pp_size=16                   # pipeline parallelization size
+ds_zero=1                   # dp sharding strategy, https://github.com/cli99/llm-analysis#parallelism-scheme
+mlp_activation_quant_bits=8 # number of bits used for mlp activation
+mlp_recompute_act=True     # whether to recompute the gelu in mlp backward
+hbm_memory_efficiency=0.8     # gpu memory efficiency
+intra_node_memory_efficiency=0.8
+inter_node_memory_efficiency=0.8
+total_num_tokens=14.8e12       # number of tokens to train on
+master_weights_dtype_bytes=4 # FP32 master weights
+other_op_bytes=4             # BF16 first and second moments AdamW optimizer
+output_dir=output_deepseek
+output_file_prefix="bs${batch_size_per_gpu}-ar${activation_recomputation}-zero${ds_zero}-"
+layernorm_dtype_bytes=2
+
+python -m llm_analysis.analysis train --model_name=${model_name} --seq_len=${seq_len} --gpu_name=${gpu_name} --dtype_name=${dtype_name} --output_dir=${output_dir} --output-file-prefix=${output_file_prefix} --activation_recomputation ${activation_recomputation} --ds_zero ${ds_zero} --batch_size_per_gpu=${batch_size_per_gpu} --total_num_gpus=${num_gpus} --dp_size=${dp_size} --tp_size=${tp_size} --pp_size=${pp_size} --ep_size=${ep_size} --flops_efficiency=${flops_efficiency} --hbm_memory_efficiency=${hbm_memory_efficiency} --total_num_tokens ${total_num_tokens} --mlp_activation_quant_bits ${mlp_activation_quant_bits} --layernorm_dtype_bytes=${layernorm_dtype_bytes} --mlp_recompute_act ${mlp_recompute_act} --master_weights_dtype_bytes ${master_weights_dtype_bytes} --other_op_bytes ${other_op_bytes} --intra_node_memory_efficiency ${intra_node_memory_efficiency} --inter_node_memory_efficiency ${inter_node_memory_efficiency}