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app.py

@@ -191,22 +191,15 @@ def run_batch_conversion(uploaded_files, max_workers):
     if not files_to_process:
         return "No supported document files found to convert.", gr.update(visible=False), gr.update(visible=False), []
 
-    # Process files in GPU-efficient chunks.
-    # Grouping files into chunks of 32 enables concurrent processing in VRAM on a single GPU node.
-    chunk_size = 32
-    chunks = [files_to_process[i:i + chunk_size] for i in range(0, len(files_to_process), chunk_size)]
-    
+    # Process ALL files in a single GPU call.
+    # The GPU function uses ThreadPoolExecutor(max_workers=32) internally,
+    # so 32 files run concurrently and as each finishes, the next one starts.
+    # This maximizes GPU utilization with a single model load.
     results = []
-    print(f"Processing {len(files_to_process)} files in {len(chunks)} GPU batches (max concurrent batches={max_workers})...")
+    print(f"Processing {len(files_to_process)} files on single GPU node (32-way VRAM worker pool)...")
     
-    with ThreadPoolExecutor(max_workers=max_workers) as executor:
-        futures = {
-            executor.submit(convert_file_batch_on_single_gpu, chunk, output_dir): idx
-            for idx, chunk in enumerate(chunks)
-        }
-        for future in as_completed(futures):
-            chunk_results = future.result()
-            results.extend(chunk_results)
+    batch_results = convert_file_batch_on_single_gpu(files_to_process, output_dir)
+    results.extend(batch_results)
 
     zip_output_path = os.path.join(temp_workspace, "converted_markdown_files.zip")
     with zipfile.ZipFile(zip_output_path, 'w', zipfile.ZIP_DEFLATED) as zip_out: