Ubuntu not uses swap as much as needed

I'm trying to run Gemma-2b on my computer with 16GB of RAM, but it gives me an error below:

2024-03-19 16:23:38.722556: I external/local_tsl/tsl/framework/bfc_allocator.cc:1112] Sum Total of in-use chunks: 4.78GiB2024-03-19 16:23:38.722568: I external/local_tsl/tsl/framework/bfc_allocator.cc:1114] Total bytes in pool: 8589934592 memory_limit_: 16352886784 available bytes: 7762952192 curr_region_allocation_bytes_: 85899345922024-03-19 16:23:38.722589: I external/local_tsl/tsl/framework/bfc_allocator.cc:1119] Stats: Limit:                     16352886784InUse:                      5129633792MaxInUse:                   6157238272NumAllocs:                         509MaxAllocSize:               1073741824Reserved:                            0PeakReserved:                        0LargestFreeBlock:                    02024-03-19 16:23:38.722623: W external/local_tsl/tsl/framework/bfc_allocator.cc:499] ************************_______________________________________*************************************...tensorflow.python.framework.errors_impl.ResourceExhaustedError: Out of memory while trying to allocate 11685516680 bytes. [Op:__inference_generate_step_12720]

Also, I have around 160GB of swap.

$ swaponNAME                     TYPE      SIZE USED PRIO/dev/nvme0n1p7           partition  60G   0B   -2/media/.../ram           file      100G 768K    0$ sysctl vm.swappiness                vm.swappiness = 100

I have more than enough swap partition to cover the needed RAM, and I'm also running it on the CPU and not the GPU. The problem is, when I run this code on my Mac with 16GB of RAM, it uses the swap and runs smoothly. I am wondering why Ubuntu cannot manage memory as efficiently as Mac. What can I do to make Ubuntu use the swap partition to run the code? I even ran Gemma-7b with 50GB of swap memory usage on Mac like a piece of cake.

Here is the codedownload the model and run the model offline to see the result

import tensorflow as tftf.keras.backend.set_floatx('float16')from keras_nlp.src.models import GemmaCausalLMfrom keras_nlp.src.backend import kerasdef read_config(path):    import json    with open(path) as config_file:        return json.load(config_file)if __name__ == '__main__':    gemma_path = '/opt/gemma_2b'    config_path = f'{gemma_path}/config.json'    tokenizer_path = f'{gemma_path}/tokenizer.json'    config = read_config(config_path)    tokenizer_config = read_config(tokenizer_path)    cls = keras.saving.get_registered_object(config["registered_name"])    backbone = keras.saving.deserialize_keras_object(config)    backbone.load_weights(f'{gemma_path}/model.weights.h5')    tokenizer = keras.saving.deserialize_keras_object(tokenizer_config)    tokenizer.load_assets(f'{gemma_path}/assets/tokenizer')    preprocessor = GemmaCausalLM.preprocessor_cls(tokenizer=tokenizer)    gemma_lm = GemmaCausalLM(backbone=backbone, preprocessor=preprocessor)    response = gemma_lm.generate(["Keras is a"], max_length=10)

Thanks.