Max Headroom 
Creating a self-hosted batch prompt processing cluster using Ollama
Introduction
In a previous article I covered the basics of Ollama, what it is, and how to use it. In this article I’m going to show you how to use Ollama to batch process a large number of prompts across multiple hosts and GPUs. I also provide an example how to use this method for structured data extraction from records such as clinical visit notes. All the code, configuration and data used in this article is available in this companion code repository.
Using the method discussed here, I batch processed a large data set with a cluster of 7 servers, each with 4 NVIDIA L40S GPUs (28 GPUs with a total of 1.344TB of VRAM) using a 32 billion parameter LLM and was able to keep the cluster’s average GPU utilization above 90% for over 24 hours. The GPUs alone consumed over 9kW (kilowatts) of power while the batch job was running, and totaled approximately 220kWh (kilowatt hours) for the entire run to complete. This works!
I conducted tests with varying size models and prompt complexity. With simple prompts and a small model (Llama 3.2 3B) this setup could process nearly 100,000 prompts per hour. With a larger model (Qwen 2.5 32B) and more complex prompts it averaged over 2,000 prompts processed per hour.
Starting the Ollama servers
The first thing we’ll need to do is start up the Ollama servers, one per GPU. If you only have one GPU, or one GPU per multiple servers, and Ollama is already running, you probably don’t need to do this. To start the Ollama servers, one per GPU, we are going to use the provided ollama-batch-servers.sh shell script. It takes a single integer argument which indicates the number of GPUs in the system you want to use.
Usage:
chmod +x ollama-batch-servers ./ollama-batch-servers.sh <number of gpus>
Example executing on a system with 4 GPUs:
If you have multiple GPU servers that you want to run batch jobs against, run the above script on each server indicating the number of GPUs to use on each server.
Preparing your prompts
Next, you’ll need to create a JSONL (JSONL not JSON) formatted file, with a single prompt per line. The following is an example:
{"role": "user", "content": "Analyze the reasons behind the collapse of the Western Roman Empire."}
{"role": "user", "content": "How did Roman architecture influence urban development in Europe?"}
{"role": "user", "content": "Compare and contrast the political systems of the Roman Republic and the Roman Empire."}
{"role": "user", "content": "Discuss the role of religion in the daily life of Roman citizens and its impact on the Empire."}
{"role": "user", "content": "Evaluate the effects of Roman conquest on the cultures of the conquered territories."}
{"role": "user", "content": "How did the Roman Empire maintain control over such a vast territory?"}
{"role": "user", "content": "Examine the relationship between the Roman Senate and the Emperor."}
{"role": "user", "content": "What technological innovations did the Romans contribute to modern society?"}
{"role": "user", "content": "Analyze the role of the Roman economy in sustaining the empire’s growth and stability."}
{"role": "user", "content": "Describe the causes and consequences of the Roman Empire's split into Eastern and Western regions."}
Configuring the batch client
The configuration file is a TOML formatted file that includes the LLM model to use, the list of Ollama instances to run the prompts against, and the system message to provide the LLM that will determine how it responds to the prompts. Here is an example configuration file for a setup with 4 servers, each with 2 GPUs:
model = "llama3.2" system_message = """You are an alien that can only respond with strings of emoji characters to convey your answer.""" [ollama_instances] #format: "hostname:port" = GPU index "server1:11432" = 0 "server1:11433" = 1 "server2:11432" = 0 "server2:11433" = 1 "server3:11432" = 0 "server3:11433" = 1 "server4:11432" = 0 "server4:11433" = 1
If you are running this on your laptop with the single standard Ollama process running on the default port, your configuration file should look like this:
model = "llama3.2" system_message = """You are an alien that can only respond with strings of emoji characters to convey your answer.""" [ollama_instances] "127.0.0.1:11434" = 0
Running a batch job
Now that we have our servers running, prompts prepared and a configuration file, it’s time to process the prompts across the cluster of hosts and GPUs. To do that we’ll use my ollama-batch-process.py script. But first we’ll need to install the required dependencies, the ollama and toml modules:
pip install ollama toml
The following is the usage documentation for my Ollama batch processing client:
usage: ollama-batch-process.py [-h] [--config CONFIG] --prompts PROMPTS [--output_dir OUTPUT_DIR]
Ollama Batch Processing Client
options:
-h, --help show this help message and exit
--config CONFIG Path to the configuration TOML file
--prompts PROMPTS Path to the JSONL file with prompts
--output_dir OUTPUT_DIR
Directory to save the response JSON files
To run your batch job on the ollama cluster, run the following command:
python ollama-batch-process.py --config config.toml --prompts prompts.jsonl --output_dir batch11
Below is an example of the output you’ll see after submitting a batch of prompts for processing:
In the example above, over two minutes (121 seconds) of work for a single GPU was completed in just 9 seconds, which is 13.5x faster than if it was run serially on a single GPU.
Collecting the responses
The responses from each prompt will be written to the designated output directory (or the ‘responses’ directory if an output directory name wasn’t provided) in JSON format, with a file per prompt. The name of the files are the Unix epoch time followed by a random element to avoid collision (example: 1732121830–4358.json). The output data files include the prompt and response pair.
Example output file contents:
{
"prompt": "Why is the sky blue?",
"response": "The sky appears blue because molecules in Earth's atmosphere scatter shorter blue wavelengths of sunlight more than longer wavelengths like red."
}
If you what to combine all the responses into a single output, the provided response-printer.py script will merge the responses into a single output.
Usage:
python response-printer <directory of response JSON files>
The following is an example of the combined responses using emoji responses to keep the listing short:
######################################## # File: 1732088793-9394.json ######################################## Prompt: Discuss the influence of Roman culture on the development of Western civilization. Response: 🏯💫🔥📚💡👑🤴🏻💪🌎️🕊️👸💃🏻🕺😍 ######################################## # File: 1732088814-4841.json ######################################## Prompt: How did the Roman Empire maintain control over such a vast territory? Response: 🏛️🔒💪🚣♂️🌄📜👑💼 ######################################## # File: 1732121810-9808.json ######################################## Prompt: How did the Roman legal system shape the foundation of modern law? Response: 🏛️📜🔒👮♂️💼🕊️🚫👫🤝
Batch Data Extraction
Note: the clinical notes in this section are synthetic and contain no PII or PHI
In the previous example, it just provided an unstructured text (emojis actually 😀) output, but you can also use this method to extract structured data such as JSON from of a batch of unstructured inputs. In this example, we’ll take a bunch of unstructured clinic visit notes, process them batch style across multiple GPUs (it would also work with a single GPU, just much slower) that will extract data into a defined data structure (defined in the system message located in the configuration file) which will result in a json file per clinic note in the designated outpu directory. We’ll then use a provided script to merge those data files into a single json document. The resulting data could be imported into database as records, or to create a parquet file in a data lake, etc.
All the files (input and example outputs) in this example are provided in the data-extraction-example directory of this article’s companion code repository.
Configuration
First we’ll need to create a configuration file that provides a system prompt that instructs the LLM to use the data structure we want to conform the extracted data to. In this example we are using the 32b version of Qwen2.5 model. This model is good at following instructions and handling JSON; use a smaller model as needed to fit the VRAM capacity of your GPUs
Config file: clinical-notes-config.toml
model = "qwen2.5:32b"
system_message = """
Your task is to extract structured data from unstructured clinical notes. For each input, you must extract the following fields if present and respond only with a JSON object in the exact format below. Do not provide any other responses or comments.
{
"mrn": "",
"patient_name": "",
"age": 0,
"sex": "",
"visit_date": "",
"reason_for_visit": "",
"diagnosis": "",
"medications": []
}
If a field is not present in the input, leave it empty or as an empty array ("[]" for "medications").
Only respond with the JSON object, nothing else.
"""
# two servers, each with 2 L40S GPUs:
[ollama_instances]
"gpu-server1:11432" = 0
"gpu-server1:11433" = 1
"gpu-server2:11434" = 0
"gpu-server2:11435" = 1
Data Preparation
Next take your collection of data and put them in the required JSONL format. Below is a sample of the contents of the provided clinical-notes.jsonl data file.
{"role": "user", "content": "Patient: John Doe (MRN: 123456), a 45-year-old male, visited the clinic on 03/15/2023 complaining of severe chest pain that started a few hours prior. He was diagnosed with angina following an ECG and blood tests. He was prescribed aspirin and nitroglycerin for management and advised to follow up in two weeks."}
{"role": "user", "content": "Visit on April 20, 2023. Jane Smith (MRN: 234567), 32 years old, presented with persistent and debilitating headaches that have been occurring for the past month. Reason for visit: chronic migraines. She was prescribed sumatriptan and advised on lifestyle modifications. Diagnosis confirmed as migraine after ruling out secondary causes."}
{"role": "user", "content": "Mark Johnson (MRN: 345678), male, age 50, came to the hospital on 02/02/2023 reporting shortness of breath and a persistent cough. Examination revealed signs consistent with COPD. Pulmonary function tests were ordered to confirm the diagnosis, and he was started on bronchodilators for symptom relief."}
{"role": "user", "content": "On June 5, 2023, Sarah Lopez (MRN: 456789), a 27-year-old female, visited for a routine checkup. She reported no specific complaints but requested birth control counseling. Reason: wellness exam. No new diagnosis was made, and no medications were prescribed during the visit."}
{"role": "user", "content": "Visit Date: 07/10/2023. Patient: Emily Zhang (MRN: 567890), a 40-year-old woman, presented with a three-day history of severe abdominal pain. A thorough examination suggested gastritis, and omeprazole was prescribed for acid suppression. She was advised to avoid spicy foods and alcohol."}
{"role": "user", "content": "Patient Henry Lee (MRN: 678901), a 65-year-old male, came to the clinic on 01/22/2023 for a follow-up on his long-standing hypertension. He reported good adherence to his medication regimen, which includes lisinopril. No new diagnosis was made, and his current treatment plan was continued."}
{"role": "user", "content": "Karen Davis (MRN: 789012), a 37-year-old woman, arrived on 10/14/2023 with complaints of a worsening skin rash over her arms and neck. On examination, she was diagnosed with contact dermatitis, likely due to a new detergent. Hydrocortisone cream was prescribed, and she was advised to avoid irritants."}
{"role": "user", "content": "Michael Brown (MRN: 890123), a 29-year-old male, was seen on May 8, 2023, with complaints of fever, fatigue, and body aches for the past three days. After ruling out bacterial infections, he was diagnosed with influenza. Medications: Tamiflu was prescribed, and hydration and rest were recommended."}
{"role": "user", "content": "Alice Taylor (MRN: 901234), a 47-year-old female, came on 03/30/2023 complaining of lower back pain that started after heavy lifting. Examination revealed muscle strain. She was advised to take ibuprofen and start physical therapy. No red flags for other conditions were noted."}
{"role": "user", "content": "David Wilson (MRN: 112345), a 55-year-old man, visited on 09/09/2023 for his annual physical exam. He had no specific complaints and no significant changes in his medical history. Routine labs were ordered, and no new medications were initiated. He was encouraged to maintain his healthy lifestyle."}
{"role": "user", "content": "Lucy Carter (MRN: 223456), a 24-year-old female, presented on August 1, 2023, with complaints of anxiety that has been interfering with her daily life. She was diagnosed with generalized anxiety disorder (GAD) and prescribed sertraline. She was also referred to a counselor for therapy."}
{"role": "user", "content": "On 06/18/2023, Thomas Moore (MRN: 334567), a 72-year-old male, visited for worsening shortness of breath over the past week. A thorough workup revealed heart failure, and he was started on furosemide and carvedilol. Follow-up was scheduled to monitor his response to treatment."}
{"role": "user", "content": "Jennifer Scott (MRN: 445678), a 36-year-old woman, attended on 02/12/2023 complaining of nausea and vomiting lasting two days. Diagnosis: viral gastroenteritis. She was advised to maintain hydration and rest. No specific medications were prescribed as symptoms were expected to resolve spontaneously."}
{"role": "user", "content": "James Kim (MRN: 556789), a 42-year-old male, visited on 04/30/2023 for right knee pain that had been worsening over several months. Imaging confirmed osteoarthritis. He was advised to use ibuprofen for pain relief and start a physical therapy program to improve joint function."}
{"role": "user", "content": "Maria Gonzalez (MRN: 667890), a 58-year-old female, was seen on 11/05/2023 for follow-up on her diabetes. She reported difficulty controlling her blood sugar despite adherence to her current medications. Her treatment was adjusted to include metformin and glipizide, and she was scheduled for a nutrition counseling session."}
{"role": "user", "content": "Paul Evans (MRN: 778901), a 48-year-old man, came on July 25, 2023, for intermittent chest discomfort. A detailed evaluation, including ECG, showed no acute changes. Diagnosis: non-cardiac chest pain, likely musculoskeletal. He was reassured and given exercises to reduce tension."}
Run the batch data extraction
With the source data and configuration in place, we are now ready to run the data extraction job. Execute the following command:
python ollama-batch-process.py --prompts clinical-notes.jsonl --config clinical-notes-config.toml --output_dir clinical_notes_data
Collect the Output Data
After the process is complete, you should have a directory filled with multiple JSON files. The following is the contents of one output files that contains the original clinical note (prompt field) and the resulting structured data (response field):
{
"prompt": "Patient: John Doe (MRN: 123456), a 45-year-old male, visited the clinic on 03/15/2023 complaining of severe chest pain that started a few hours prior. He was diagnosed with angina following an ECG and blood tests. He was prescribed aspirin and nitroglycerin for management and advised to follow up in two weeks.",
"response": "{n "mrn": "123456",n "patient_name": "John Doe",n "age": 45,n "sex": "male",n "visit_date": "03/15/2023",n "reason_for_visit": "severe chest pain that started a few hours prior",n "diagnosis": "angina",n "medications": ["aspirin", "nitroglycerin"]n}"
}
Merging the Results
If you want to converge the data of all output JSON files into a single JSON file, you can use the provided response-json-merge.py script.
python response-json-merge.py --input-dir clinical_notes_data --output-file clinical_notes_data.json
After merging the output data the file will look like this extract below:
[{
"mrn": "678234",
"patient_name": "William Roberts",
"age": 50,
"sex": "male",
"visit_date": "06/20/2023",
"reason_for_visit": "joint pain and swelling in his fingers",
"diagnosis": "rheumatoid arthritis",
"medications": [
"Methotrexate"
]
},
{
"mrn": "667890",
"patient_name": "Maria Gonzalez",
"age": 58,
"sex": "female",
"visit_date": "11/05/2023",
"reason_for_visit": "follow-up on her diabetes",
"diagnosis": "diabetes",
"medications": [
"metformin",
"glipizide"
]
},
{
"mrn": "123456",
"patient_name": "John Doe",
"age": 45,
"sex": "male",
"visit_date": "03/15/2023",
"reason_for_visit": "severe chest pain that started a few hours prior",
"diagnosis": "angina",
"medications": [
"aspirin",
"nitroglycerin"
]
}]
Now that you have clean, structured data, you can process it via an ETL pipeline to insert it into a database, or convert to a parquet file or similar to ingest by a data lake.
Conclusion
In this article I demonstrated that with a little code, Ollama can easily be used as an LLM engine for a large scale batch processing cluster. While it has performed great so far, in the near future I’m going to replicate this using vLLM to see if it can provide higher processing throughput.
llm, data-processing, ollama, batch-processing, artificial-intelligence
Author
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