diff --git a/README.md b/README.md
index fa1ff70..a94912d 100644
--- a/README.md
+++ b/README.md
@@ -82,6 +82,13 @@ with GlobalGPUController(gpu_ids=[0, 1], vram_to_keep="750MB", interval=90, busy
 - CLI + API parity: same controllers power both code paths.
 - Continuous docs + CI: mkdocs + mkdocstrings build in CI to keep guidance up to date.
 
+## For developers
+
+- Install dev extras: `pip install -e ".[dev]"` (add `.[rocm]` if you need ROCm SMI).
+- Fast CUDA checks: `pytest tests/cuda_controller tests/global_controller tests/utilities/test_platform_manager.py tests/test_cli_thresholds.py`
+- ROCm-only tests carry `@pytest.mark.rocm`; run with `pytest --run-rocm tests/rocm_controller`.
+- Markers: `rocm` (needs ROCm stack) and `large_memory` (opt-in locally).
+
 ## Contributing
 
 Contributions are welcome—especially around ROCm support, platform fallbacks, and scheduler-specific recipes. Open an issue or PR if you hit edge cases on your cluster.
diff --git a/docs/getting-started.md b/docs/getting-started.md
index aefd689..f3949fe 100644
--- a/docs/getting-started.md
+++ b/docs/getting-started.md
@@ -39,6 +39,12 @@ understand the minimum knobs you need to keep a GPU occupied.
     pip install keep-gpu
     ```
 
+## For contributors
+
+- Install dev extras: `pip install -e ".[dev]"` (append `.[rocm]` if you need ROCm SMI).
+- Fast CUDA checks: `pytest tests/cuda_controller tests/global_controller tests/utilities/test_platform_manager.py tests/test_cli_thresholds.py`
+- ROCm-only tests are marked `rocm`; run with `pytest --run-rocm tests/rocm_controller`.
+
 === "Editable dev install"
     ```bash
     git clone https://github.com/Wangmerlyn/KeepGPU.git
diff --git a/pyproject.toml b/pyproject.toml
index 8acbf93..a69fed6 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -139,3 +139,9 @@ exclude = ["build", "dist", ".venv"]
 known-first-party = ["keep_gpu"]
 combine-as-imports = true
 force-single-line = false
+
+[tool.pytest.ini_options]
+markers = [
+  "rocm: tests that require ROCm stack",
+  "large_memory: tests that use large VRAM",
+]
diff --git a/src/keep_gpu/global_gpu_controller/global_gpu_controller.py b/src/keep_gpu/global_gpu_controller/global_gpu_controller.py
index 89676ae..804f2de 100644
--- a/src/keep_gpu/global_gpu_controller/global_gpu_controller.py
+++ b/src/keep_gpu/global_gpu_controller/global_gpu_controller.py
@@ -30,25 +30,33 @@ def __init__(
                 CudaGPUController,
             )
 
-            if gpu_ids is None:
-                self.gpu_ids = list(range(torch.cuda.device_count()))
-            else:
-                self.gpu_ids = gpu_ids
+            controller_cls = CudaGPUController
+        elif self.computing_platform == ComputingPlatform.ROCM:
+            from keep_gpu.single_gpu_controller.rocm_gpu_controller import (
+                RocmGPUController,
+            )
 
-            self.controllers = [
-                CudaGPUController(
-                    rank=i,
-                    interval=interval,
-                    vram_to_keep=vram_to_keep,
-                    busy_threshold=busy_threshold,
-                )
-                for i in self.gpu_ids
-            ]
+            controller_cls = RocmGPUController
         else:
             raise NotImplementedError(
                 f"GlobalGPUController not implemented for platform {self.computing_platform}"
             )
 
+        if gpu_ids is None:
+            self.gpu_ids = list(range(torch.cuda.device_count()))
+        else:
+            self.gpu_ids = gpu_ids
+
+        self.controllers = [
+            controller_cls(
+                rank=i,
+                interval=interval,
+                vram_to_keep=vram_to_keep,
+                busy_threshold=busy_threshold,
+            )
+            for i in self.gpu_ids
+        ]
+
     def keep(self) -> None:
         for ctrl in self.controllers:
             ctrl.keep()
diff --git a/src/keep_gpu/single_gpu_controller/base_gpu_controller.py b/src/keep_gpu/single_gpu_controller/base_gpu_controller.py
index 87f629e..4adc5ff 100644
--- a/src/keep_gpu/single_gpu_controller/base_gpu_controller.py
+++ b/src/keep_gpu/single_gpu_controller/base_gpu_controller.py
@@ -1,12 +1,25 @@
+from typing import Union
+
+from keep_gpu.utilities.humanized_input import parse_size
+
+
 class BaseGPUController:
-    def __init__(self, vram_to_keep: int, interval: float):
+    def __init__(self, vram_to_keep: Union[int, str], interval: float):
         """
         Base class for GPU controllers.
 
         Args:
-            vram_to_keep (int): Amount of VRAM (in MB) to keep free.
-            interval (int): Time interval (in seconds) for checks or actions.
-        """
+            vram_to_keep (int or str): Amount of VRAM to keep busy. Accepts integers
+                (tensor element count) or human strings like "1GiB" (converted to
+                element count for float32 tensors).
+            interval (float): Time interval (in seconds) between keep-alive cycles.
+        """
+        if isinstance(vram_to_keep, str):
+            vram_to_keep = parse_size(vram_to_keep)
+        elif not isinstance(vram_to_keep, int):
+            raise TypeError(
+                f"vram_to_keep must be str or int, got {type(vram_to_keep)}"
+            )
         self.vram_to_keep = vram_to_keep
         self.interval = interval
 
diff --git a/src/keep_gpu/single_gpu_controller/cuda_gpu_controller.py b/src/keep_gpu/single_gpu_controller/cuda_gpu_controller.py
index 7ff794a..baf9579 100644
--- a/src/keep_gpu/single_gpu_controller/cuda_gpu_controller.py
+++ b/src/keep_gpu/single_gpu_controller/cuda_gpu_controller.py
@@ -61,14 +61,6 @@ def __init__(
                 hogging the GPU.
 
         """
-        if isinstance(vram_to_keep, str):
-            vram_to_keep = self.parse_size(vram_to_keep)
-        elif isinstance(vram_to_keep, int):
-            vram_to_keep = vram_to_keep
-        else:
-            raise TypeError(
-                f"vram_to_keep must be str or int, got {type(vram_to_keep)}"
-            )
         super().__init__(vram_to_keep=vram_to_keep, interval=interval)
         self.rank = rank
         self.device = torch.device(f"cuda:{rank}")
@@ -185,7 +177,7 @@ def _run_mat_batch(self, matrix: torch.Tensor) -> None:
         toc = time.time()
 
         logger.debug(
-            "rank %s: mat ops batch done – avg %.2f ms",
+            "rank %s: mat ops batch done - avg %.2f ms",
             self.rank,
             (toc - tic) * 1000 / self.matmul_iterations,
         )
diff --git a/src/keep_gpu/single_gpu_controller/rocm_gpu_controller.py b/src/keep_gpu/single_gpu_controller/rocm_gpu_controller.py
index e69de29..2f65b80 100644
--- a/src/keep_gpu/single_gpu_controller/rocm_gpu_controller.py
+++ b/src/keep_gpu/single_gpu_controller/rocm_gpu_controller.py
@@ -0,0 +1,143 @@
+import threading
+import time
+from typing import Optional
+
+import torch
+
+from keep_gpu.single_gpu_controller.base_gpu_controller import BaseGPUController
+from keep_gpu.utilities.logger import setup_logger
+
+logger = setup_logger(__name__)
+
+
+class RocmGPUController(BaseGPUController):
+    """
+    Keep a single ROCm GPU busy by running lightweight elementwise ops
+    in a background thread. Requires a ROCm-enabled torch build.
+    """
+
+    def __init__(
+        self,
+        *,
+        rank: int,
+        interval: float = 1.0,
+        vram_to_keep: str | int = "1000 MB",
+        busy_threshold: int = 10,
+        iterations: int = 5000,
+    ):
+        super().__init__(vram_to_keep=vram_to_keep, interval=interval)
+        self.rank = rank
+        self.device = torch.device(f"cuda:{rank}")
+        self.busy_threshold = busy_threshold
+        self.iterations = iterations
+        self._stop_evt: Optional[threading.Event] = None
+        self._thread: Optional[threading.Thread] = None
+
+        # Lazy rocm_smi import; keep handle for reuse
+        try:
+            import rocm_smi  # type: ignore
+
+            self._rocm_smi = rocm_smi
+        except Exception as exc:  # pragma: no cover - env-specific
+            logger.debug("rocm_smi not available: %s", exc)
+            self._rocm_smi = None
+
+    def keep(self) -> None:
+        if self._thread and self._thread.is_alive():
+            logger.warning("rank %s: keep thread already running", self.rank)
+            return
+        if self._rocm_smi:
+            try:
+                self._rocm_smi.rsmi_init()
+            except Exception as exc:  # pragma: no cover - env-specific
+                logger.debug("rsmi_init failed: %s", exc)
+
+        self._stop_evt = threading.Event()
+        self._thread = threading.Thread(
+            target=self._keep_loop,
+            name=f"gpu-keeper-rocm-{self.rank}",
+            daemon=True,
+        )
+        self._thread.start()
+        logger.info("rank %s: ROCm keep thread started", self.rank)
+
+    def release(self) -> None:
+        if not (self._thread and self._thread.is_alive()):
+            logger.warning("rank %s: keep thread not running", self.rank)
+            return
+        self._stop_evt.set()
+        self._thread.join()
+        torch.cuda.empty_cache()
+        if self._rocm_smi:
+            try:
+                self._rocm_smi.rsmi_shut_down()
+            except Exception as exc:  # pragma: no cover - best effort
+                logger.debug("rsmi_shut_down failed: %s", exc)
+        logger.info("rank %s: keep thread stopped & cache cleared", self.rank)
+
+    def __enter__(self):
+        self.keep()
+        return self
+
+    def __exit__(self, exc_type, exc, tb):
+        self.release()
+
+    def _query_utilization(self) -> Optional[int]:
+        if not self._rocm_smi:
+            return None
+        try:
+            util = self._rocm_smi.rsmi_dev_busy_percent_get(self.rank)
+            return int(util)
+        except Exception as exc:  # pragma: no cover - env-specific
+            logger.debug("ROCm utilization query failed: %s", exc)
+            return None
+
+    def _keep_loop(self) -> None:
+        torch.cuda.set_device(self.rank)
+        tensor = None
+        while not self._stop_evt.is_set():
+            try:
+                tensor = torch.rand(
+                    self.vram_to_keep,
+                    device=self.device,
+                    dtype=torch.float32,
+                    requires_grad=False,
+                )
+                break
+            except RuntimeError:
+                logger.exception("rank %s: failed to allocate tensor", self.rank)
+                time.sleep(self.interval)
+        if tensor is None:
+            logger.error("rank %s: failed to allocate tensor, exiting loop", self.rank)
+            raise RuntimeError("Failed to allocate tensor for ROCm GPU keeping")
+
+        while not self._stop_evt.is_set():
+            try:
+                util = self._query_utilization()
+                if util is not None and util > self.busy_threshold:
+                    logger.debug("rank %s: GPU busy (%d%%), sleeping", self.rank, util)
+                else:
+                    self._run_batch(tensor)
+                time.sleep(self.interval)
+            except RuntimeError as exc:
+                if "out of memory" in str(exc).lower():
+                    torch.cuda.empty_cache()
+                time.sleep(self.interval)
+            except Exception:
+                logger.exception("rank %s: unexpected error", self.rank)
+                time.sleep(self.interval)
+
+    @torch.no_grad()
+    def _run_batch(self, tensor: torch.Tensor) -> None:
+        tic = time.time()
+        for _ in range(self.iterations):
+            torch.relu_(tensor)
+            if self._stop_evt.is_set():
+                break
+        torch.cuda.synchronize()
+        toc = time.time()
+        logger.debug(
+            "rank %s: elementwise batch done - avg %.2f ms",
+            self.rank,
+            (toc - tic) * 1000 / max(1, self.iterations),
+        )
diff --git a/tests/conftest.py b/tests/conftest.py
new file mode 100644
index 0000000..22d1680
--- /dev/null
+++ b/tests/conftest.py
@@ -0,0 +1,31 @@
+import pytest
+import torch
+
+
+def pytest_addoption(parser):
+    parser.addoption(
+        "--run-rocm",
+        action="store_true",
+        default=False,
+        help="run tests marked as rocm (require ROCm stack)",
+    )
+
+
+def pytest_configure(config):
+    config.addinivalue_line("markers", "rocm: tests that require ROCm stack")
+    config.addinivalue_line("markers", "large_memory: tests that use large VRAM")
+
+
+def pytest_collection_modifyitems(config, items):
+    if config.getoption("--run-rocm"):
+        return
+
+    skip_rocm = pytest.mark.skip(reason="need --run-rocm option to run")
+    for item in items:
+        if "rocm" in item.keywords:
+            item.add_marker(skip_rocm)
+
+
+@pytest.fixture
+def rocm_available():
+    return bool(torch.cuda.is_available() and getattr(torch.version, "hip", None))
diff --git a/tests/rocm_controller/test_rocm_utilization.py b/tests/rocm_controller/test_rocm_utilization.py
new file mode 100644
index 0000000..4618398
--- /dev/null
+++ b/tests/rocm_controller/test_rocm_utilization.py
@@ -0,0 +1,36 @@
+import sys
+
+import pytest
+
+from keep_gpu.single_gpu_controller import rocm_gpu_controller as rgc
+
+
+@pytest.mark.rocm
+def test_query_rocm_utilization_with_mock(monkeypatch, rocm_available):
+    if not rocm_available:
+        pytest.skip("ROCm stack not available")
+
+    class DummyRocmSMI:
+        calls = 0
+
+        @classmethod
+        def rsmi_init(cls):
+            cls.calls += 1
+
+        @staticmethod
+        def rsmi_dev_busy_percent_get(index):
+            assert index == 1
+            return 42
+
+        @classmethod
+        def rsmi_shut_down(cls):
+            cls.calls += 1
+
+    # Ensure the counter is reset to avoid leaking state between tests
+    DummyRocmSMI.calls = 0
+    monkeypatch.setitem(sys.modules, "rocm_smi", DummyRocmSMI)
+    util = rgc._query_rocm_utilization(1)
+    assert util == 42
+    assert DummyRocmSMI.calls == 2  # init + shutdown
+    # Reset after test for cleanliness
+    DummyRocmSMI.calls = 0