diff --git a/README.md b/README.md
index edda4998..2e7f343f 100644
--- a/README.md
+++ b/README.md
@@ -169,7 +169,7 @@ Peak GPU memory usage during the benchmark is `4.2GB` for nougat, and `4.1GB` fo
 
 **Throughput**
 
-Marker takes about 4.5GB of VRAM on average per task, so you can convert 10 documents in parallel on an A6000.
+Marker takes about 4GB of VRAM on average per task, so you can convert 12 documents in parallel on an A6000.
 
 ![Benchmark results](data/images/per_doc.png)
 
diff --git a/convert.py b/convert.py
index 8a5012ef..4d927456 100755
--- a/convert.py
+++ b/convert.py
@@ -1,8 +1,11 @@
-import argparse
 import os
-from typing import Dict, Optional
 
-import ray
+os.environ["IN_STREAMLIT"] = "true" # Avoid multiprocessing inside surya
+os.environ["PDFTEXT_CPU_WORKERS"] = "1" # Avoid multiprocessing inside pdftext
+
+import pypdfium2 # Needs to be at the top to avoid warnings
+import argparse
+import torch.multiprocessing as mp
 from tqdm import tqdm
 import math
 
@@ -19,8 +22,19 @@
 configure_logging()
 
 
-@ray.remote(num_cpus=settings.RAY_CORES_PER_WORKER, num_gpus=.05 if settings.CUDA else 0)
-def process_single_pdf(filepath: str, out_folder: str, model_refs, metadata: Optional[Dict] = None, min_length: Optional[int] = None):
+def worker_init(shared_model):
+    global model_refs
+    model_refs = shared_model
+
+
+def worker_exit():
+    global model_refs
+    del model_refs
+
+
+def process_single_pdf(args):
+    filepath, out_folder, metadata, min_length = args
+
     fname = os.path.basename(filepath)
     if markdown_exists(out_folder, fname):
         return
@@ -86,45 +100,27 @@ def main():
 
     total_processes = min(len(files_to_convert), args.workers)
 
-    ray.init(
-        num_cpus=total_processes,
-        num_gpus=1 if settings.CUDA else 0,
-        storage=settings.RAY_CACHE_PATH,
-        _temp_dir=settings.RAY_CACHE_PATH,
-        log_to_driver=settings.DEBUG
-    )
+    # Dynamically set GPU allocation per task based on GPU ram
+    if settings.CUDA:
+        tasks_per_gpu = settings.INFERENCE_RAM // settings.VRAM_PER_TASK if settings.CUDA else 0
+        total_processes = min(tasks_per_gpu, total_processes)
 
+    mp.set_start_method('spawn') # Required for CUDA, forkserver doesn't work
     model_lst = load_all_models()
-    model_refs = ray.put(model_lst)
-
-    # Dynamically set GPU allocation per task based on GPU ram
-    gpu_frac = settings.VRAM_PER_TASK / settings.INFERENCE_RAM if settings.CUDA else 0
+    for model in model_lst:
+        if model:
+            model.share_memory()
 
     print(f"Converting {len(files_to_convert)} pdfs in chunk {args.chunk_idx + 1}/{args.num_chunks} with {total_processes} processes, and storing in {out_folder}")
-    futures = [
-        process_single_pdf.options(num_gpus=gpu_frac).remote(
-            filepath,
-            out_folder,
-            model_refs,
-            metadata=metadata.get(os.path.basename(filepath)),
-            min_length=args.min_length
-        ) for filepath in files_to_convert
-    ]
-
-    # Run all ray conversion tasks
-    progress_bar = tqdm(total=len(futures))
-    while len(futures) > 0:
-        finished, futures = ray.wait(
-            futures, timeout=7.0
-        )
-        finished_lst = ray.get(finished)
-        if isinstance(finished_lst, list):
-            progress_bar.update(len(finished_lst))
-        else:
-            progress_bar.update(1)
+    task_args = [(f, out_folder, metadata.get(os.path.basename(f)), args.min_length) for f in files_to_convert]
+
+    with mp.Pool(processes=total_processes, initializer=worker_init, initargs=(model_lst,)) as pool:
+        list(tqdm(pool.imap(process_single_pdf, task_args), total=len(task_args), desc="Processing PDFs", unit="pdf"))
+
+        pool._worker_handler.terminate = worker_exit
 
-    # Shutdown ray to free resources
-    ray.shutdown()
+    # Delete all CUDA tensors
+    del model_lst
 
 
 if __name__ == "__main__":
diff --git a/convert_single.py b/convert_single.py
index aa3bdd97..78ee9c5f 100755
--- a/convert_single.py
+++ b/convert_single.py
@@ -1,3 +1,4 @@
+import pypdfium2 # Needs to be at the top to avoid warnings
 import argparse
 import os
 
diff --git a/marker/convert.py b/marker/convert.py
index eec53124..5ebbf964 100644
--- a/marker/convert.py
+++ b/marker/convert.py
@@ -1,7 +1,7 @@
 import warnings
 warnings.filterwarnings("ignore", category=UserWarning) # Filter torch pytree user warnings
 
-import pypdfium2 as pdfium
+import pypdfium2 as pdfium # Needs to be at the top to avoid warnings
 from PIL import Image
 
 from marker.utils import flush_cuda_memory
@@ -33,8 +33,8 @@
 def convert_single_pdf(
         fname: str,
         model_lst: List,
-        max_pages=None,
-        metadata: Optional[Dict]=None,
+        max_pages: int = None,
+        metadata: Optional[Dict] = None,
         langs: Optional[List[str]] = None,
         batch_multiplier: int = 1
 ) -> Tuple[str, Dict[str, Image.Image], Dict]:
diff --git a/marker/models.py b/marker/models.py
index 9b113621..7333e8d0 100644
--- a/marker/models.py
+++ b/marker/models.py
@@ -71,7 +71,7 @@ def load_all_models(langs=None, device=None, dtype=None, force_load_ocr=False):
     edit = load_editing_model(device, dtype)
 
     # Only load recognition model if we'll need it for all pdfs
-    ocr = setup_recognition_model(langs, device, dtype) if ((settings.OCR_ENGINE == "surya" and settings.OCR_ALL_PAGES) or force_load_ocr) else None
+    ocr = setup_recognition_model(langs, device, dtype)
     texify = setup_texify_model(device, dtype)
     model_lst = [texify, layout, order, edit, detection, ocr]
     return model_lst
\ No newline at end of file
diff --git a/marker/ocr/heuristics.py b/marker/ocr/heuristics.py
index 916707f9..b3cf6b2b 100644
--- a/marker/ocr/heuristics.py
+++ b/marker/ocr/heuristics.py
@@ -8,7 +8,11 @@
 
 
 def should_ocr_page(page: Page, no_text: bool):
-    detected_lines_found = detected_line_coverage(page)
+    detected_lines_found, total_lines = detected_line_coverage(page)
+
+    # No reason to OCR page if it has no text lines
+    if total_lines == 0:
+        return False
 
     # OCR page if we got minimal text, or if we got too many spaces
     conditions = [
@@ -55,7 +59,6 @@ def no_text_found(pages: List[Page]):
 def detected_line_coverage(page: Page, intersect_thresh=.5, detection_thresh=.4):
     found_lines = 0
     for detected_line in page.text_lines.bboxes:
-
         # Get bbox and rescale to match dimensions of original page
         detected_bbox = detected_line.bbox
         detected_bbox = rescale_bbox(page.text_lines.image_bbox, page.bbox, detected_bbox)
@@ -70,5 +73,6 @@ def detected_line_coverage(page: Page, intersect_thresh=.5, detection_thresh=.4)
 
     total_lines = len(page.text_lines.bboxes)
     if total_lines == 0:
-        return False
-    return found_lines / total_lines > detection_thresh
+        return True, 0
+
+    return found_lines / total_lines > detection_thresh, total_lines
diff --git a/marker/ocr/recognition.py b/marker/ocr/recognition.py
index 4fad04a4..20ff0fd8 100644
--- a/marker/ocr/recognition.py
+++ b/marker/ocr/recognition.py
@@ -48,17 +48,7 @@ def run_ocr(doc, pages: List[Page], langs: List[str], rec_model, batch_multiplie
     if ocr_method is None:
         return pages, {"ocr_pages": 0, "ocr_failed": 0, "ocr_success": 0, "ocr_engine": "none"}
     elif ocr_method == "surya":
-        # Load model just in time if we're not OCRing everything
-        del_rec_model = False
-        if rec_model is None:
-            lang_tokens = langs_to_ids(langs)
-            rec_model = setup_recognition_model(lang_tokens)
-            del_rec_model = True
-
         new_pages = surya_recognition(doc, ocr_idxs, langs, rec_model, pages, batch_multiplier=batch_multiplier)
-
-        if del_rec_model:
-            del rec_model
     elif ocr_method == "ocrmypdf":
         new_pages = tesseract_recognition(doc, ocr_idxs, langs)
     else:
diff --git a/poetry.lock b/poetry.lock
index a5ce1264..03527d66 100644
--- a/poetry.lock
+++ b/poetry.lock
@@ -1,19 +1,5 @@
 # This file is automatically @generated by Poetry 1.5.0 and should not be changed by hand.
 
-[[package]]
-name = "aiosignal"
-version = "1.3.1"
-description = "aiosignal: a list of registered asynchronous callbacks"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "aiosignal-1.3.1-py3-none-any.whl", hash = "sha256:f8376fb07dd1e86a584e4fcdec80b36b7f81aac666ebc724e2c090300dd83b17"},
-    {file = "aiosignal-1.3.1.tar.gz", hash = "sha256:54cd96e15e1649b75d6c87526a6ff0b6c1b0dd3459f43d9ca11d48c339b68cfc"},
-]
-
-[package.dependencies]
-frozenlist = ">=1.1.0"
-
 [[package]]
 name = "annotated-types"
 version = "0.7.0"
@@ -412,20 +398,6 @@ files = [
     {file = "charset_normalizer-3.3.2-py3-none-any.whl", hash = "sha256:3e4d1f6587322d2788836a99c69062fbb091331ec940e02d12d179c1d53e25fc"},
 ]
 
-[[package]]
-name = "click"
-version = "8.1.7"
-description = "Composable command line interface toolkit"
-optional = false
-python-versions = ">=3.7"
-files = [
-    {file = "click-8.1.7-py3-none-any.whl", hash = "sha256:ae74fb96c20a0277a1d615f1e4d73c8414f5a98db8b799a7931d1582f3390c28"},
-    {file = "click-8.1.7.tar.gz", hash = "sha256:ca9853ad459e787e2192211578cc907e7594e294c7ccc834310722b41b9ca6de"},
-]
-
-[package.dependencies]
-colorama = {version = "*", markers = "platform_system == \"Windows\""}
-
 [[package]]
 name = "colorama"
 version = "0.4.6"
@@ -587,92 +559,6 @@ files = [
     {file = "fqdn-1.5.1.tar.gz", hash = "sha256:105ed3677e767fb5ca086a0c1f4bb66ebc3c100be518f0e0d755d9eae164d89f"},
 ]
 
-[[package]]
-name = "frozenlist"
-version = "1.4.1"
-description = "A list-like structure which implements collections.abc.MutableSequence"
-optional = false
-python-versions = ">=3.8"
-files = [
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+    {file = "pydantic_core-2.18.3-pp39-pypy39_pp73-manylinux_2_17_aarch64.manylinux2014_aarch64.whl", hash = "sha256:b367a73a414bbb08507da102dc2cde0fa7afe57d09b3240ce82a16d608a7679c"},
+    {file = "pydantic_core-2.18.3-pp39-pypy39_pp73-manylinux_2_17_x86_64.manylinux2014_x86_64.whl", hash = "sha256:0ecce4b2360aa3f008da3327d652e74a0e743908eac306198b47e1c58b03dd2b"},
+    {file = "pydantic_core-2.18.3-pp39-pypy39_pp73-manylinux_2_5_i686.manylinux1_i686.whl", hash = "sha256:bd4435b8d83f0c9561a2a9585b1de78f1abb17cb0cef5f39bf6a4b47d19bafe3"},
+    {file = "pydantic_core-2.18.3-pp39-pypy39_pp73-musllinux_1_1_aarch64.whl", hash = "sha256:616221a6d473c5b9aa83fa8982745441f6a4a62a66436be9445c65f241b86c94"},
+    {file = "pydantic_core-2.18.3-pp39-pypy39_pp73-musllinux_1_1_x86_64.whl", hash = "sha256:7e6382ce89a92bc1d0c0c5edd51e931432202b9080dc921d8d003e616402efd1"},
+    {file = "pydantic_core-2.18.3-pp39-pypy39_pp73-win_amd64.whl", hash = "sha256:ff58f379345603d940e461eae474b6bbb6dab66ed9a851ecd3cb3709bf4dcf6a"},
+    {file = "pydantic_core-2.18.3.tar.gz", hash = "sha256:432e999088d85c8f36b9a3f769a8e2b57aabd817bbb729a90d1fe7f18f6f1f39"},
 ]
 
 [package.dependencies]
@@ -2799,50 +2600,6 @@ files = [
 [package.extras]
 full = ["numpy"]
 
-[[package]]
-name = "ray"
-version = "2.23.0"
-description = "Ray provides a simple, universal API for building distributed applications."
-optional = false
-python-versions = ">=3.8"
-files = [
-    {file = "ray-2.23.0-cp310-cp310-macosx_10_15_x86_64.whl", hash = "sha256:4f5ea8dc8fc014704ea12ef8a569abf0deca2ba2a6f157dc5fdd1789db4e0a65"},
-    {file = "ray-2.23.0-cp310-cp310-macosx_11_0_arm64.whl", hash = "sha256:fc950898871c3ecf3b921295c5fcf47b4a30b57b54be8f369014fb1eb9b4cfa5"},
-    {file = "ray-2.23.0-cp310-cp310-manylinux2014_x86_64.whl", hash = "sha256:55610f8eae65ce5686bde75a5782ce63e2a0112ccd2262b8acd707264da6dbea"},
-    {file = "ray-2.23.0-cp311-cp311-macosx_10_15_x86_64.whl", hash = "sha256:1a43d94ce3f14490e6f1e3e868fd6a5f3be4878cbf83c4bcdc741861d6a4dbf6"},
-    {file = "ray-2.23.0-cp311-cp311-macosx_11_0_arm64.whl", hash = "sha256:f2d2c1d59d7c8bd8b97288f7ae9a6bf762bd4e703b57787282400d3176dd159d"},
-    {file = "ray-2.23.0-cp311-cp311-manylinux2014_x86_64.whl", hash = "sha256:15c109fd9969326323c8bdb0701cd9af21c85f465002f74950622f9a580ec4e5"},
-    {file = "ray-2.23.0-cp39-cp39-macosx_10_15_x86_64.whl", hash = "sha256:e7d059f094dedae36dddeaf792ebb74d4eed1a8ab1fb540dbffce4ac22694800"},
-    {file = "ray-2.23.0-cp39-cp39-macosx_11_0_arm64.whl", hash = "sha256:7c305f31674fb8319c147d66e27dd210c7ad6d375626307ddfc62137a26d4155"},
-    {file = "ray-2.23.0-cp39-cp39-manylinux2014_x86_64.whl", hash = "sha256:b40f85c67ee3d58732b4021460c4297eb418f466313d70b577e5bf9fbb4c2d16"},
-]
-
-[package.dependencies]
-aiosignal = "*"
-click = ">=7.0"
-filelock = "*"
-frozenlist = "*"
-jsonschema = "*"
-msgpack = ">=1.0.0,<2.0.0"
-packaging = "*"
-protobuf = ">=3.15.3,<3.19.5 || >3.19.5"
-pyyaml = "*"
-requests = "*"
-
-[package.extras]
-air = ["aiohttp (>=3.7)", "aiohttp-cors", "colorful", "fastapi", "fsspec", "grpcio (>=1.32.0)", "grpcio (>=1.42.0)", "memray", "numpy (>=1.20)", "opencensus", "pandas", "pandas (>=1.3)", "prometheus-client (>=0.7.1)", "py-spy (>=0.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2.0.dev0 || >=2.5.dev0,<3)", "requests", "smart-open", "starlette", "tensorboardX (>=1.9)", "uvicorn[standard]", "virtualenv (>=20.0.24,!=20.21.1)", "watchfiles"]
-all = ["aiohttp (>=3.7)", "aiohttp-cors", "colorful", "dm-tree", "fastapi", "fsspec", "grpcio (!=1.56.0)", "grpcio (>=1.32.0)", "grpcio (>=1.42.0)", "gymnasium (==0.28.1)", "lz4", "memray", "numpy (>=1.20)", "opencensus", "opentelemetry-api", "opentelemetry-exporter-otlp", "opentelemetry-sdk", "pandas", "pandas (>=1.3)", "prometheus-client (>=0.7.1)", "py-spy (>=0.2.0)", "pyarrow (>=6.0.1)", "pydantic (<2.0.dev0 || >=2.5.dev0,<3)", "pyyaml", "ray-cpp (==2.23.0)", "requests", "rich", "scikit-image", "scipy", "smart-open", "starlette", "tensorboardX (>=1.9)", "typer", "uvicorn[standard]", "virtualenv (>=20.0.24,!=20.21.1)", "watchfiles"]
-client = ["grpcio (!=1.56.0)"]
-cpp = ["ray-cpp (==2.23.0)"]
-data = ["fsspec", "numpy (>=1.20)", "pandas (>=1.3)", "pyarrow (>=6.0.1)"]
-default = ["aiohttp (>=3.7)", "aiohttp-cors", "colorful", "grpcio (>=1.32.0)", "grpcio (>=1.42.0)", "memray", "opencensus", "prometheus-client (>=0.7.1)", "py-spy (>=0.2.0)", "pydantic (<2.0.dev0 || >=2.5.dev0,<3)", "requests", "smart-open", "virtualenv (>=20.0.24,!=20.21.1)"]
-observability = ["opentelemetry-api", "opentelemetry-exporter-otlp", "opentelemetry-sdk"]
-rllib = ["dm-tree", "fsspec", "gymnasium (==0.28.1)", "lz4", "pandas", "pyarrow (>=6.0.1)", "pyyaml", "requests", "rich", "scikit-image", "scipy", "tensorboardX (>=1.9)", "typer"]
-serve = ["aiohttp (>=3.7)", "aiohttp-cors", "colorful", "fastapi", "grpcio (>=1.32.0)", "grpcio (>=1.42.0)", "memray", "opencensus", "prometheus-client (>=0.7.1)", "py-spy (>=0.2.0)", "pydantic (<2.0.dev0 || >=2.5.dev0,<3)", "requests", "smart-open", "starlette", "uvicorn[standard]", "virtualenv (>=20.0.24,!=20.21.1)", "watchfiles"]
-serve-grpc = ["aiohttp (>=3.7)", "aiohttp-cors", "colorful", "fastapi", "grpcio (>=1.32.0)", "grpcio (>=1.42.0)", "memray", "opencensus", "prometheus-client (>=0.7.1)", "py-spy (>=0.2.0)", "pydantic (<2.0.dev0 || >=2.5.dev0,<3)", "requests", "smart-open", "starlette", "uvicorn[standard]", "virtualenv (>=20.0.24,!=20.21.1)", "watchfiles"]
-train = ["fsspec", "pandas", "pyarrow (>=6.0.1)", "requests", "tensorboardX (>=1.9)"]
-tune = ["fsspec", "pandas", "pyarrow (>=6.0.1)", "requests", "tensorboardX (>=1.9)"]
-
 [[package]]
 name = "referencing"
 version = "0.35.1"
@@ -3379,13 +3136,13 @@ tests = ["cython", "littleutils", "pygments", "pytest", "typeguard"]
 
 [[package]]
 name = "surya-ocr"
-version = "0.4.10"
+version = "0.4.12"
 description = "OCR, layout, reading order, and line detection in 90+ languages"
 optional = false
 python-versions = "!=2.7.*,!=3.0.*,!=3.1.*,!=3.2.*,!=3.3.*,!=3.4.*,!=3.5.*,!=3.6.*,!=3.7.*,!=3.8.*,>=3.9"
 files = [
-    {file = "surya_ocr-0.4.10-py3-none-any.whl", hash = "sha256:18236c422b3855a1f6ece34f96137afd70d78078edc4ae002f972580f37918bb"},
-    {file = "surya_ocr-0.4.10.tar.gz", hash = "sha256:a5ab764c6797e41854aed3e462a526361cf130c0c5da0208575152eefb762685"},
+    {file = "surya_ocr-0.4.12-py3-none-any.whl", hash = "sha256:68029c0dff58ed2041ada99fd2cbe81c249204c61fe65a02a8478d3a46fbc87a"},
+    {file = "surya_ocr-0.4.12.tar.gz", hash = "sha256:b6d50f430e72f182a661354afb9c4ee94afb4129c0b296fb06454e08f2abf6ee"},
 ]
 
 [package.dependencies]
@@ -3948,13 +3705,13 @@ test = ["websockets"]
 
 [[package]]
 name = "widgetsnbextension"
-version = "4.0.10"
+version = "4.0.11"
 description = "Jupyter interactive widgets for Jupyter Notebook"
 optional = false
 python-versions = ">=3.7"
 files = [
-    {file = "widgetsnbextension-4.0.10-py3-none-any.whl", hash = "sha256:d37c3724ec32d8c48400a435ecfa7d3e259995201fbefa37163124a9fcb393cc"},
-    {file = "widgetsnbextension-4.0.10.tar.gz", hash = "sha256:64196c5ff3b9a9183a8e699a4227fb0b7002f252c814098e66c4d1cd0644688f"},
+    {file = "widgetsnbextension-4.0.11-py3-none-any.whl", hash = "sha256:55d4d6949d100e0d08b94948a42efc3ed6dfdc0e9468b2c4b128c9a2ce3a7a36"},
+    {file = "widgetsnbextension-4.0.11.tar.gz", hash = "sha256:8b22a8f1910bfd188e596fe7fc05dcbd87e810c8a4ba010bdb3da86637398474"},
 ]
 
 [[package]]
@@ -3975,4 +3732,4 @@ testing = ["big-O", "jaraco.functools", "jaraco.itertools", "jaraco.test", "more
 [metadata]
 lock-version = "2.0"
 python-versions = ">=3.9,<3.13,!=3.9.7"
-content-hash = "fa892a80f72a88ccd0cb9d5e7d1a115f53eb2f19ddd3a5e502e5f57e3d9d2af3"
+content-hash = "47f21358aee7b60b96fdf7d407518f4278dc4de198ad9407168d2ed742260e14"
diff --git a/pyproject.toml b/pyproject.toml
index 1fe6e7a4..e52e1699 100644
--- a/pyproject.toml
+++ b/pyproject.toml
@@ -27,13 +27,12 @@ transformers = "^4.36.2"
 numpy = "^1.26.1"
 python-dotenv = "^1.0.0"
 torch = "^2.2.2" # Issue with torch 2.3.0 and vision models - https://github.com/pytorch/pytorch/issues/121834
-ray = "^2.20.0"
 tqdm = "^4.66.1"
 tabulate = "^0.9.0"
 ftfy = "^6.1.1"
 texify = "^0.1.9"
 rapidfuzz = "^3.8.1"
-surya-ocr = "^0.4.10"
+surya-ocr = "^0.4.12"
 filetype = "^1.2.0"
 regex = "^2024.4.28"
 pdftext = "^0.3.10"