diff --git a/vllm/envs.py b/vllm/envs.py
index 0445447dd9df..c40f7e47097c 100644
--- a/vllm/envs.py
+++ b/vllm/envs.py
@@ -92,6 +92,10 @@
     V_SCALE_CONSTANT: int = 10
     VLLM_SERVER_DEV_MODE: bool = False
     VLLM_V1_OUTPUT_PROC_CHUNK_SIZE: int = 128
+    VLLM_MLA_DISABLE: bool = False
+    VLLM_MLA_PERFORM_MATRIX_ABSORPTION: bool = True
+    VLLM_MLA_DISABLE_REQUANTIZATION: bool = False
+    VLLM_ENABLE_MOE_ALIGN_BLOCK_SIZE_TRITON: bool = False
 
 
 def get_default_cache_root():
@@ -580,6 +584,49 @@ def maybe_convert_int(value: Optional[str]) -> Optional[int]:
     lambda: float(os.getenv("VLLM_LOG_BATCHSIZE_INTERVAL", "-1")),
     "VLLM_DISABLE_COMPILE_CACHE":
     lambda: bool(int(os.getenv("VLLM_DISABLE_COMPILE_CACHE", "0"))),
+
+    # If set, vllm will run in development mode, which will enable
+    # some additional endpoints for developing and debugging,
+    # e.g. `/reset_prefix_cache`
+    "VLLM_SERVER_DEV_MODE":
+    lambda: bool(int(os.getenv("VLLM_SERVER_DEV_MODE", "0"))),
+
+    # Controls the maximum number of requests to handle in a
+    # single asyncio task when processing per-token outputs in the
+    # V1 AsyncLLM interface. It is applicable when handling a high
+    # concurrency of streaming requests.
+    # Setting this too high can result in a higher variance of
+    # inter-message latencies. Setting it too low can negatively impact
+    # TTFT and overall throughput.
+    "VLLM_V1_OUTPUT_PROC_CHUNK_SIZE":
+    lambda: int(os.getenv("VLLM_V1_OUTPUT_PROC_CHUNK_SIZE", "128")),
+
+    # If set, vLLM will disable the MLA attention optimizations.
+    "VLLM_MLA_DISABLE":
+    lambda: bool(int(os.getenv("VLLM_MLA_DISABLE", "0"))),
+
+    # Flag that can control whether or not we perform matrix-absorption for MLA
+    # decode, i.e. absorb W_UK into W_Q/W_UK and W_UV into W_O, absorbing the
+    # matrices reduces the runtime FLOPs needed to compute MLA but requires
+    # storing more weights, W_Q_UK and W_UV_O, so can increase memory usage,
+    # the is enabled by default
+    "VLLM_MLA_PERFORM_MATRIX_ABSORPTION":
+    lambda: bool(int(os.getenv("VLLM_MLA_PERFORM_MATRIX_ABSORPTION", "1"))),
+
+    # When running MLA with matrix-absorption enabled and fp8 quantized weights
+    # we perform the matrix-absorption in float32 precision, after the matrices
+    # are absorbed we requantize the weights back to fp8, this flag can be used
+    # to disable the requantization step, and instead convert the absorbed
+    # matrices to match the activation type. This can lead to higher memory and
+    # compute usage but better preserves the accuracy of the original model.
+    "VLLM_MLA_DISABLE_REQUANTIZATION":
+    lambda: bool(int(os.getenv("VLLM_MLA_DISABLE_REQUANTIZATION", "0"))),
+
+    # If set, vLLM will use the Triton implementation of moe_align_block_size,
+    # i.e. moe_align_block_size_triton in fused_moe.py.
+    "VLLM_ENABLE_MOE_ALIGN_BLOCK_SIZE_TRITON":
+    lambda: bool(int(os.getenv("VLLM_ENABLE_MOE_ALIGN_BLOCK_SIZE_TRITON", "0"))
+                 ),
 }
 
 # end-env-vars-definition