add as VLLM_RUNPOD prefix and update readme

docs/configuration.md

@@ -156,6 +156,28 @@ The way this works is that the first request will have a batch size of `DEFAULT_
 | `DISABLE_LOGGING_REQUEST`   | False   | `bool`  | Disable logging requests.                                                                                                                              |
 | `MAX_LOG_LEN`               | None    | `int`   | Max number of prompt characters or prompt ID numbers being printed in log.                                                                             |
 
+## VLLM_RUNPOD_ Prefix: Pass Any Engine Arg
+
+Any vLLM `AsyncEngineArgs` field can be set via an environment variable using the `VLLM_RUNPOD_` prefix. The suffix maps directly to the field name (case-insensitive, underscores preserved).
+
+**Format:** `VLLM_RUNPOD_<ARG_NAME>=<value>`
+
+**Examples:**
+
+| Environment Variable                  | vLLM Engine Arg            | Value Example |
+| ------------------------------------- | -------------------------- | ------------- |
+| `VLLM_RUNPOD_MAX_MODEL_LEN`           | `max_model_len`            | `4096`        |
+| `VLLM_RUNPOD_ENFORCE_EAGER`           | `enforce_eager`            | `true`        |
+| `VLLM_RUNPOD_ENABLE_CHUNKED_PREFILL`  | `enable_chunked_prefill`   | `true`        |
+| `VLLM_RUNPOD_NUM_SCHEDULER_STEPS`     | `num_scheduler_steps`      | `8`           |
+| `VLLM_RUNPOD_TOKENIZER_POOL_SIZE`     | `tokenizer_pool_size`      | `4`           |
+
+**Notes:**
+- Only valid `AsyncEngineArgs` fields are applied. Unknown keys are silently ignored.
+- Values are automatically cast to the correct type (`int`, `float`, `bool`, `str`, or JSON for `dict`/`list`).
+- `VLLM_RUNPOD_` overrides are applied **after** all other worker env vars, so they take precedence.
+- For a full list of available engine args, see the [vLLM AsyncEngineArgs documentation](https://docs.vllm.ai/en/latest/serving/engine_args.html).
+
 ## Docker Build Arguments
 
 These variables are used when building custom Docker images with models baked in: