diff --git a/scripts/ci/cleanup_hf_cache.py b/scripts/ci/cleanup_hf_cache.py
new file mode 100755
index 000000000..fd9038a14
--- /dev/null
+++ b/scripts/ci/cleanup_hf_cache.py
@@ -0,0 +1,146 @@
+#!/usr/bin/env python3
+"""
+Clean up stale HuggingFace cache artifacts from previous failed downloads.
+
+This script removes incomplete marker files, temporary files, and lock files
+from the HuggingFace cache directory. These artifacts can accumulate from
+interrupted or failed downloads and may interfere with future downloads.
+"""
+
+import os
+import sys
+from pathlib import Path
+from typing import List
+
+try:
+    from huggingface_hub import constants
+
+    HF_HUB_AVAILABLE = True
+except ImportError:
+    print("Warning: huggingface_hub not available")
+    HF_HUB_AVAILABLE = False
+
+
+def get_hf_cache_dir() -> str:
+    """Get the HuggingFace cache directory."""
+    if HF_HUB_AVAILABLE:
+        return constants.HF_HUB_CACHE
+
+    # Fallback to environment variable or default
+    hf_home = os.environ.get("HF_HOME", os.path.expanduser("~/.cache/huggingface"))
+    return os.path.join(hf_home, "hub")
+
+
+def find_stale_artifacts(cache_dir: str) -> List[Path]:
+    """
+    Find stale artifact files in the HuggingFace cache.
+
+    Args:
+        cache_dir: HuggingFace cache directory
+
+    Returns:
+        List of paths to stale artifact files
+    """
+    cache_path = Path(cache_dir)
+
+    if not cache_path.exists():
+        return []
+
+    # Patterns for stale files to clean up
+    patterns = [
+        "**/*.incomplete",  # Incomplete download markers
+        "**/*.tmp",  # Temporary files
+        "**/*.lock",  # Lock files from interrupted downloads
+    ]
+
+    stale_files = []
+    for pattern in patterns:
+        stale_files.extend(cache_path.glob(pattern))
+
+    return stale_files
+
+
+def cleanup_artifacts(artifacts: List[Path]) -> tuple[int, int]:
+    """
+    Remove stale artifact files.
+
+    Args:
+        artifacts: List of file paths to remove
+
+    Returns:
+        Tuple of (successful_removals, failed_removals)
+    """
+    successful = 0
+    failed = 0
+
+    for file_path in artifacts:
+        try:
+            file_path.unlink()
+            print(f"  Removed: {file_path}")
+            successful += 1
+        except Exception as e:
+            print(f"  Warning: Could not remove {file_path}: {e}")
+            failed += 1
+
+    return successful, failed
+
+
+def main() -> int:
+    """
+    Main cleanup logic.
+
+    Returns:
+        Always returns 0 (cleanup is best-effort and should not fail CI)
+    """
+    print("=" * 70)
+    print("HuggingFace Cache Cleanup")
+    print("=" * 70)
+
+    # Get cache directory
+    cache_dir = get_hf_cache_dir()
+    print(f"Cache directory: {cache_dir}")
+
+    if not os.path.exists(cache_dir):
+        print("Cache directory does not exist - nothing to clean")
+        return 0
+
+    print("-" * 70)
+
+    # Find stale artifacts
+    print("Scanning for stale artifacts...")
+    stale_artifacts = find_stale_artifacts(cache_dir)
+
+    if not stale_artifacts:
+        print("✓ No stale cache artifacts found")
+        return 0
+
+    # Clean up artifacts
+    print(f"Found {len(stale_artifacts)} stale artifact(s) to remove:")
+    successful, failed = cleanup_artifacts(stale_artifacts)
+
+    print("-" * 70)
+
+    # Summary
+    if failed > 0:
+        print(f"⚠ Cleaned up {successful} file(s), {failed} removal(s) failed")
+    else:
+        print(f"✓ Successfully cleaned up {successful} stale file(s)")
+
+    # Always return 0 - cleanup failures should not fail CI
+    return 0
+
+
+if __name__ == "__main__":
+    try:
+        exit_code = main()
+        sys.exit(exit_code)
+    except KeyboardInterrupt:
+        print("\nInterrupted by user")
+        sys.exit(0)
+    except Exception as e:
+        print(f"ERROR: Unexpected error during cleanup: {e}")
+        import traceback
+
+        traceback.print_exc()
+        # Still return 0 - cleanup failures should not fail CI
+        sys.exit(0)
diff --git a/scripts/ci/prepare_runner.sh b/scripts/ci/prepare_runner.sh
index 910c9cb15..4e30d00ac 100755
--- a/scripts/ci/prepare_runner.sh
+++ b/scripts/ci/prepare_runner.sh
@@ -1,47 +1,29 @@
 #!/bin/bash
-# Prepare the CI runner by cleaning up incomplete HuggingFace download files
+# Prepare the CI runner by cleaning up stale HuggingFace cache artifacts and validating models
 set -euo pipefail
 
+SCRIPT_DIR="$(cd "$(dirname "${BASH_SOURCE[0]}")" && pwd)"
+
 echo "Preparing CI runner..."
+echo ""
 
-# Clean up incomplete HuggingFace download files
-echo "Cleaning up incomplete HuggingFace download files..."
-python3 << 'EOF'
-import os
-import glob
+# Clean up stale HuggingFace cache artifacts from previous failed downloads
+python3 "${SCRIPT_DIR}/cleanup_hf_cache.py"
+echo ""
 
-try:
-    from huggingface_hub import constants
-    hf_cache_dir = constants.HF_HUB_CACHE
-except Exception as e:
-    print(f"Warning: Could not import huggingface_hub constants: {e}")
-    # Fallback to checking HF_HOME env var or default location
-    hf_home = os.environ.get('HF_HOME', os.path.expanduser("~/.cache/huggingface"))
-    hf_cache_dir = os.path.join(hf_home, "hub")
+# Validate model integrity for configured runners
+echo "Validating model integrity..."
 
-if os.path.exists(hf_cache_dir):
-    print(f"Checking HuggingFace cache directory: {hf_cache_dir}")
+# Enable accelerated HuggingFace downloads (10x faster on high-bandwidth networks)
+export HF_HUB_ENABLE_HF_TRANSFER=1
 
-    # Clean up incomplete marker files, temporary files, and lock files
-    patterns = ['**/*.incomplete', '**/*.tmp', '**/*.lock']
-    cleaned_count = 0
+python3 "${SCRIPT_DIR}/validate_and_download_models.py"
+VALIDATION_EXIT_CODE=$?
 
-    for pattern in patterns:
-        files = glob.glob(os.path.join(hf_cache_dir, pattern), recursive=True)
-        for file_path in files:
-            try:
-                os.remove(file_path)
-                print(f"Removed: {file_path}")
-                cleaned_count += 1
-            except Exception as e:
-                print(f"Warning: Could not remove {file_path}: {e}")
-
-    if cleaned_count > 0:
-        print(f"Cleaned up {cleaned_count} incomplete HuggingFace download file(s)")
-    else:
-        print("No incomplete HuggingFace download files found")
-else:
-    print(f"HuggingFace cache directory does not exist: {hf_cache_dir}")
-EOF
+if [ $VALIDATION_EXIT_CODE -ne 0 ]; then
+    echo "Model validation failed with exit code: $VALIDATION_EXIT_CODE"
+    exit $VALIDATION_EXIT_CODE
+fi
 
+echo ""
 echo "CI runner preparation complete!"
diff --git a/scripts/ci/validate_and_download_models.py b/scripts/ci/validate_and_download_models.py
new file mode 100755
index 000000000..4e678114c
--- /dev/null
+++ b/scripts/ci/validate_and_download_models.py
@@ -0,0 +1,458 @@
+#!/usr/bin/env python3
+"""
+Validate model integrity for CI runners and download if needed.
+
+This script checks HuggingFace cache for model completeness and downloads
+missing models. It exits with code 1 if download was required (indicating
+cache corruption), which causes the CI job to fail and surface cache issues.
+"""
+
+import os
+import re
+import sys
+from pathlib import Path
+from typing import Dict, List, Optional, Tuple
+
+try:
+    from huggingface_hub import constants, snapshot_download
+
+    HF_HUB_AVAILABLE = True
+except ImportError:
+    print(
+        "Warning: huggingface_hub not available. Install with: pip install huggingface_hub"
+    )
+    HF_HUB_AVAILABLE = False
+
+try:
+    from safetensors import safe_open
+
+    SAFETENSORS_AVAILABLE = True
+except ImportError:
+    print("Warning: safetensors not available. Install with: pip install safetensors")
+    SAFETENSORS_AVAILABLE = False
+
+
+# Mapping of runner labels to their required models
+# Add new runner labels and models here as needed
+RUNNER_LABEL_MODEL_MAP: Dict[str, List[str]] = {
+    "8-gpu-h200": ["deepseek-ai/DeepSeek-V3-0324", "moonshotai/Kimi-K2-Thinking"],
+}
+
+
+def get_hf_cache_dir() -> str:
+    """Get the HuggingFace cache directory."""
+    if HF_HUB_AVAILABLE:
+        return constants.HF_HUB_CACHE
+
+    # Fallback to environment variable or default
+    hf_home = os.environ.get("HF_HOME", os.path.expanduser("~/.cache/huggingface"))
+    return os.path.join(hf_home, "hub")
+
+
+def get_model_cache_path(model_id: str, cache_dir: str) -> Optional[Path]:
+    """
+    Find the model's cache directory in HuggingFace hub cache.
+
+    Args:
+        model_id: Model identifier (e.g., "deepseek-ai/DeepSeek-V3-0324")
+        cache_dir: HuggingFace cache directory
+
+    Returns:
+        Path to model's snapshot directory, or None if not found
+    """
+    # Convert model_id to cache directory name format
+    # "deepseek-ai/DeepSeek-V3-0324" -> "models--deepseek-ai--DeepSeek-V3-0324"
+    cache_model_name = "models--" + model_id.replace("/", "--")
+    model_path = Path(cache_dir) / cache_model_name
+
+    if not model_path.exists():
+        return None
+
+    # Find the most recent snapshot directory
+    snapshots_dir = model_path / "snapshots"
+    if not snapshots_dir.exists():
+        return None
+
+    # Get all snapshot directories (sorted by modification time, most recent first)
+    snapshot_dirs = sorted(
+        [d for d in snapshots_dir.iterdir() if d.is_dir()],
+        key=lambda x: x.stat().st_mtime,
+        reverse=True,
+    )
+
+    if not snapshot_dirs:
+        return None
+
+    return snapshot_dirs[0]
+
+
+def check_incomplete_files(model_path: Path, cache_dir: str) -> List[str]:
+    """
+    Check for incomplete download marker files specific to this model.
+
+    Args:
+        model_path: Path to model's snapshot directory
+        cache_dir: HuggingFace cache directory
+
+    Returns:
+        List of incomplete files found for this specific model
+    """
+    incomplete_in_snapshot = []
+
+    # Check if any files in the snapshot are symlinks to .incomplete blobs
+    # This ensures we only flag incomplete files for THIS specific model,
+    # not other models that might be downloading concurrently
+    for file_path in model_path.glob("*"):
+        if file_path.is_symlink():
+            try:
+                target = file_path.resolve()
+                # Check if the symlink target has .incomplete suffix
+                if str(target).endswith(".incomplete"):
+                    incomplete_in_snapshot.append(str(target))
+            except (OSError, RuntimeError):
+                # Broken symlink - also indicates incomplete download
+                incomplete_in_snapshot.append(str(file_path))
+
+    return incomplete_in_snapshot
+
+
+def validate_safetensors_file(file_path: Path) -> Tuple[bool, Optional[str]]:
+    """
+    Validate that a safetensors file is readable and not corrupted.
+
+    Args:
+        file_path: Path to the safetensors file
+
+    Returns:
+        Tuple of (is_valid, error_message)
+    """
+    if not SAFETENSORS_AVAILABLE:
+        # Skip validation if safetensors library is not available
+        return True, None
+
+    try:
+        # Attempt to open and read the header
+        # This will fail if the file is corrupted or incomplete
+        with safe_open(file_path, framework="pt", device="cpu") as f:
+            # Just accessing the keys validates the header is readable
+            _ = f.keys()
+        return True, None
+    except Exception as e:
+        error_type = type(e).__name__
+        error_msg = str(e)
+        # Return detailed error for debugging
+        return False, f"{error_type}: {error_msg}"
+
+
+def validate_model_shards(model_path: Path) -> Tuple[bool, Optional[str]]:
+    """
+    Validate that all model shards are present and complete.
+
+    Args:
+        model_path: Path to model's snapshot directory
+
+    Returns:
+        Tuple of (is_valid, error_message)
+    """
+    # Pattern for sharded files: model-00001-of-00009.safetensors or pytorch_model-00001-of-00009.bin
+    shard_pattern = re.compile(
+        r"(?:model|pytorch_model)-(\d+)-of-(\d+)\.(safetensors|bin)"
+    )
+
+    # Find all shard files (both .safetensors and .bin)
+    shard_files = (
+        list(model_path.glob("model-*-of-*.safetensors"))
+        + list(model_path.glob("model-*-of-*.bin"))
+        + list(model_path.glob("pytorch_model-*-of-*.bin"))
+    )
+
+    if not shard_files:
+        # No sharded files - check for single model file
+        single_files = list(model_path.glob("model.safetensors")) or list(
+            model_path.glob("pytorch_model.bin")
+        )
+        if single_files:
+            # Validate the single safetensors file if it exists
+            if single_files[0].suffix == ".safetensors":
+                is_valid, error_msg = validate_safetensors_file(single_files[0])
+                if not is_valid:
+                    return False, f"Corrupted file {single_files[0].name}: {error_msg}"
+            return True, None
+        return False, "No model files found (safetensors or bin)"
+
+    # Extract total shard count from any shard filename
+    total_shards = None
+    for shard_file in shard_files:
+        match = shard_pattern.search(shard_file.name)
+        if match:
+            total_shards = int(match.group(2))
+            break
+
+    if total_shards is None:
+        return False, "Could not determine total shard count from filenames"
+
+    # Check that all shards exist
+    expected_shards = set(range(1, total_shards + 1))
+    found_shards = set()
+
+    for shard_file in shard_files:
+        match = shard_pattern.search(shard_file.name)
+        if match:
+            shard_num = int(match.group(1))
+            found_shards.add(shard_num)
+
+    missing_shards = expected_shards - found_shards
+
+    if missing_shards:
+        missing_list = sorted(missing_shards)
+        return False, f"Missing shards: {missing_list} (expected {total_shards} total)"
+
+    # Check for index file
+    index_file = model_path / "model.safetensors.index.json"
+    if not index_file.exists():
+        return False, "Missing model.safetensors.index.json"
+
+    # Validate each safetensors shard file for corruption
+    print(f"  Validating {len(shard_files)} shard file(s) for corruption...")
+    for shard_file in shard_files:
+        if shard_file.suffix == ".safetensors":
+            is_valid, error_msg = validate_safetensors_file(shard_file)
+            if not is_valid:
+                return False, f"Corrupted shard {shard_file.name}: {error_msg}"
+
+    return True, None
+
+
+def validate_model(model_id: str, cache_dir: str) -> Tuple[bool, Optional[str]]:
+    """
+    Validate a model's cache integrity.
+
+    Args:
+        model_id: Model identifier
+        cache_dir: HuggingFace cache directory
+
+    Returns:
+        Tuple of (is_valid, error_message)
+    """
+    print(f"Validating model: {model_id}")
+
+    # Find model in cache
+    model_path = get_model_cache_path(model_id, cache_dir)
+    if model_path is None:
+        return False, "Model not found in cache"
+
+    print(f"  Found in cache: {model_path}")
+
+    # Check for incomplete files
+    incomplete_files = check_incomplete_files(model_path, cache_dir)
+    if incomplete_files:
+        return False, f"Found incomplete download files: {len(incomplete_files)} files"
+
+    # Validate shards
+    is_valid, error_msg = validate_model_shards(model_path)
+    if not is_valid:
+        return False, error_msg
+
+    print(f"  ✓ Model validated successfully")
+    return True, None
+
+
+def download_model(model_id: str) -> bool:
+    """
+    Download a model from HuggingFace.
+
+    Args:
+        model_id: Model identifier
+
+    Returns:
+        True if download succeeded, False otherwise
+    """
+    if not HF_HUB_AVAILABLE:
+        print(f"ERROR: Cannot download model - huggingface_hub not available")
+        return False
+
+    print(f"Downloading model: {model_id}")
+    print(f"  This may take a while for large models...")
+
+    try:
+        snapshot_download(
+            repo_id=model_id,
+            allow_patterns=["*.safetensors", "*.bin", "*.json", "*.txt", "*.model"],
+            ignore_patterns=["*.msgpack", "*.h5", "*.ot"],  # codespell:ignore ot
+        )
+        print(f"  ✓ Download completed: {model_id}")
+        return True
+    except Exception as e:
+        print(f"  ✗ Download failed: {e}")
+        return False
+
+
+def get_runner_labels() -> List[str]:
+    """
+    Get the runner labels from environment variables.
+
+    GitHub Actions doesn't expose runner labels directly as environment variables.
+    Workflows should set the RUNNER_LABELS environment variable with a comma-separated
+    list of labels (e.g., "self-hosted,8-gpu-h200,linux").
+
+    Returns:
+        List of runner labels, empty list if not set
+    """
+    labels_str = os.environ.get("RUNNER_LABELS", "")
+    if not labels_str:
+        return []
+
+    # Split by comma and strip whitespace
+    return [label.strip() for label in labels_str.split(",") if label.strip()]
+
+
+def should_validate_runner(runner_labels: List[str]) -> bool:
+    """
+    Check if the runner should have model validation based on its labels.
+
+    Args:
+        runner_labels: List of runner labels
+
+    Returns:
+        True if any label matches a configured label in RUNNER_LABEL_MODEL_MAP
+    """
+    if not runner_labels:
+        return False
+
+    # Check if any label is in the configured map
+    return any(label in RUNNER_LABEL_MODEL_MAP for label in runner_labels)
+
+
+def get_required_models(runner_labels: List[str]) -> List[str]:
+    """
+    Get list of models required based on runner labels.
+
+    Args:
+        runner_labels: List of runner labels (e.g., ["self-hosted", "8-gpu-h200", "linux"])
+
+    Returns:
+        List of model identifiers to validate (deduplicated)
+    """
+    all_models = []
+
+    for label in runner_labels:
+        if label in RUNNER_LABEL_MODEL_MAP:
+            models = RUNNER_LABEL_MODEL_MAP[label]
+            print(
+                f"  ✓ Matched label configuration: '{label}' -> {len(models)} model(s)"
+            )
+            all_models.extend(models)
+
+    if not all_models:
+        print(f"  ⚠ No configuration found for any label in: {runner_labels}")
+
+    # Remove duplicates while preserving order
+    seen = set()
+    unique_models = []
+    for model in all_models:
+        if model not in seen:
+            seen.add(model)
+            unique_models.append(model)
+
+    return unique_models
+
+
+def main() -> int:
+    """
+    Main validation logic.
+
+    Returns:
+        0 if all models are valid or runner doesn't need validation
+        1 if models needed to be downloaded or validation failed
+    """
+    print("=" * 70)
+    print("Model Validation for CI Runners")
+    print("=" * 70)
+
+    runner_labels = get_runner_labels()
+    print(f"Runner labels: {', '.join(runner_labels) if runner_labels else 'NOT SET'}")
+
+    # Check if this runner needs validation
+    if not should_validate_runner(runner_labels):
+        print(
+            "Skipping validation: No runner labels match configured model requirements"
+        )
+        return 0
+
+    print(f"Proceeding with model validation for this runner")
+
+    # Get required models for these runner labels
+    required_models = get_required_models(runner_labels)
+
+    if not required_models:
+        print(f"Warning: No models configured for labels: {runner_labels}")
+        return 0
+
+    print(f"Models to validate: {required_models}")
+    print("-" * 70)
+
+    # Get cache directory
+    cache_dir = get_hf_cache_dir()
+    print(f"HuggingFace cache: {cache_dir}")
+    print("-" * 70)
+
+    # Track validation results
+    models_needing_download = []
+    validation_errors = []
+
+    # Validate each required model
+    for model_id in required_models:
+        is_valid, error_msg = validate_model(model_id, cache_dir)
+
+        if not is_valid:
+            print(f"  ✗ Validation failed: {error_msg}")
+            models_needing_download.append(model_id)
+            validation_errors.append(f"{model_id}: {error_msg}")
+
+    print("-" * 70)
+
+    # If all models are valid, exit successfully
+    if not models_needing_download:
+        print("✓ All models validated successfully!")
+        return 0
+
+    # Models need to be downloaded
+    print(f"⚠ Cache validation failed for {len(models_needing_download)} model(s)")
+    for error in validation_errors:
+        print(f"  - {error}")
+
+    print("-" * 70)
+    print("Attempting to download missing/corrupted models...")
+    print("-" * 70)
+
+    download_failed = False
+    for model_id in models_needing_download:
+        if not download_model(model_id):
+            download_failed = True
+
+    print("-" * 70)
+
+    if download_failed:
+        print("✗ FAILED: Some models could not be downloaded")
+        return 1
+
+    # All downloads succeeded, but we still exit with error to flag cache issues
+    print("✗ FAILED: Models were downloaded due to cache corruption/missing files")
+    print("This indicates the cache was invalid and needed to be repaired.")
+    print("Failing the job to surface this issue for investigation.")
+    return 1
+
+
+if __name__ == "__main__":
+    try:
+        exit_code = main()
+        sys.exit(exit_code)
+    except KeyboardInterrupt:
+        print("\nInterrupted by user")
+        sys.exit(1)
+    except Exception as e:
+        print(f"ERROR: Unexpected error: {e}")
+        import traceback
+
+        traceback.print_exc()
+        sys.exit(1)