diff --git a/megatron/core/transformer/moe/fused_a2a.py b/megatron/core/transformer/moe/fused_a2a.py
index defbe996a72..5e97d7a6786 100644
--- a/megatron/core/transformer/moe/fused_a2a.py
+++ b/megatron/core/transformer/moe/fused_a2a.py
@@ -3,7 +3,6 @@
 # Copyright (c) 2025 DeepSeek
 # Licensed under the MIT License - https://github.com/deepseek-ai/DeepEP/blob/main/LICENSE
 
-import os
 from typing import Optional
 
 from megatron.core.utils import internal_api
@@ -269,7 +268,6 @@ def set_deepep_num_sms(num_sms):
 
 
 try:
-    import hybrid_ep_cpp
     from deep_ep import HybridEPBuffer
 
     HAVE_HYBRIDEP = True
@@ -277,62 +275,6 @@ def set_deepep_num_sms(num_sms):
     HAVE_HYBRIDEP = False
 
 _hybrid_ep_buffer = None
-_HYBRID_EP_TOKEN_ALIGNMENT = 16
-_HYBRID_EP_MIN_BUFFER_TOKENS = 512
-_HYBRID_EP_IB_QP_MAX_DEPTH = 65535
-_HYBRID_EP_IB_DISPATCH_DEPTH_PER_TOKEN = 3
-
-
-def _round_up_to_multiple(value: int, multiple: int) -> int:
-    return ((value + multiple - 1) // multiple) * multiple
-
-
-def _hybrid_ep_num_nodes(group: torch.distributed.ProcessGroup) -> int:
-    """Mirror HybridEP's NVLink-domain detection without constructing the full buffer."""
-    ranks_per_nvlink_domain_env = os.getenv("NUM_OF_HYBRID_EP_RANKS_PER_NVLINK_DOMAIN")
-    if ranks_per_nvlink_domain_env is not None:
-        ranks_per_nvlink_domain = int(ranks_per_nvlink_domain_env)
-    else:
-        allocator = hybrid_ep_cpp.ExtendedMemoryAllocator()
-        ranks_per_nvlink_domain = allocator.detect_accessible_ranks(group)
-
-    assert group.size() % ranks_per_nvlink_domain == 0, (
-        f"The number of ranks {group.size()} should be divisible by the number of ranks per "
-        f"NVLink domain {ranks_per_nvlink_domain}."
-    )
-    return group.size() // ranks_per_nvlink_domain
-
-
-def _hybrid_ep_uses_internode_rdma(group: torch.distributed.ProcessGroup) -> bool:
-    if _hybrid_ep_buffer is not None and hasattr(_hybrid_ep_buffer, "num_of_nodes"):
-        return _hybrid_ep_buffer.num_of_nodes > 1
-    return _hybrid_ep_num_nodes(group) > 1
-
-
-def _validate_hybrid_ep_ib_tx_depth(num_tokens: int, group: torch.distributed.ProcessGroup) -> None:
-    buffer_tokens = max(
-        _round_up_to_multiple(num_tokens, _HYBRID_EP_TOKEN_ALIGNMENT), _HYBRID_EP_MIN_BUFFER_TOKENS
-    )
-    tx_depth = _HYBRID_EP_IB_DISPATCH_DEPTH_PER_TOKEN * buffer_tokens + 1
-    if tx_depth <= _HYBRID_EP_IB_QP_MAX_DEPTH:
-        return
-
-    if not _hybrid_ep_uses_internode_rdma(group):
-        return
-
-    max_supported_tokens = (
-        ((_HYBRID_EP_IB_QP_MAX_DEPTH - 1) // _HYBRID_EP_IB_DISPATCH_DEPTH_PER_TOKEN)
-        // _HYBRID_EP_TOKEN_ALIGNMENT
-        * _HYBRID_EP_TOKEN_ALIGNMENT
-    )
-    raise ValueError(
-        f"HybridEP InfiniBand dispatch queue pair depth ({tx_depth}) exceeds the hardware "
-        f"limit of {_HYBRID_EP_IB_QP_MAX_DEPTH}. DeepEP computes this depth from the "
-        f"tokens per rank rounded up to a {_HYBRID_EP_TOKEN_ALIGNMENT}-token buffer "
-        f"alignment ({buffer_tokens}). Reduce sequence length or micro-batch size, or "
-        f"increase Tensor Parallelism (TP) / Context Parallelism (CP), so tokens per rank "
-        f"are at most {max_supported_tokens} for multi-node HybridEP."
-    )
 
 
 def init_hybrid_ep_buffer(
@@ -450,9 +392,8 @@ def forward(
                 num_blocks_permute = None
                 num_blocks_unpermute = None
 
-        num_tokens, hidden_dim = x.shape[-2:]
-        _validate_hybrid_ep_ib_tx_depth(num_tokens, group)
         if _hybrid_ep_buffer is None:
+            num_tokens, hidden_dim = x.shape[-2:]
             fp8_dispatch = False  # Currently, we do not support fp8 dispatch
             init_hybrid_ep_buffer(
                 group,