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backend: pytorch

# WideEP related settings
moe_config:
  backend: WIDEEP
  load_balancer: /mnt/examples/backends/trtllm/engine_configs/deepseek-r1/disagg/wide_ep/eplb.yaml

# TP/EP/PP/DP
tensor_parallel_size: 16
moe_expert_parallel_size: 16
pipeline_parallel_size: 1
enable_attention_dp: true

max_batch_size: 256
max_num_tokens: 256
# 8448 = 8192 ISL + 256 OSL
max_seq_len: 8448

kv_cache_config:
  # With dp attention disabled: high free_gpu_memory_fraction is fine.
  # free_gpu_memory_fraction: 0.85
  # With dp attention enabled: large ISL at high concurrency may need
  # free_gpu_memory_fraction low to have enough available memory.
  free_gpu_memory_fraction: 0.30
  dtype: fp8


# NOTE: pytorch_backend_config section flattened since: https://github.com/NVIDIA/TensorRT-LLM/pull/4603
# NOTE: overlap_scheduler enabled by default since this commit and changed
# config field from 'enable_overlap_scheduler' to 'disable_overlap_scheduler':
# https://github.com/NVIDIA/TensorRT-LLM/commit/b4e5df0ee0024eda3eeb83a6ba822245a30ab428
disable_overlap_scheduler: false
cuda_graph_config:
  enable_padding: true
  # NOTE: For larger max batch size, you may want to
  # add larger cuda graph batch sizes below to match.
  batch_sizes:
  - 1
  - 2
  - 4
  - 8
  - 16
  - 32
  - 64
  - 128
  - 256


print_iter_log: true

cache_transceiver_config:
  backend: DEFAULT
