LLMs on HPC

Target Audience: HPC users who have completed Intro-to-HPC and are interested in running inference-based LLM methods on YCRC systems

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Workshop content: This workshop will provide researchers interested in LLMs with the skills to launch and run inference-based workflows with open-source models on YCRC systems. Specifically, attendees will learn

• What GPU resources are available for each YCRC system
• How to identify what GPU is needed for different LLMs
• How to launch an LLM on YCRC systems
• How to conduct inference via direct prompting with an LLM using ollama
• How to modify an LLM’s parameters for reproducibility/consistency in response
• How to implement an LLM within python using jupyter and ollama
• Understand additional considerations for RAG and fine-tuning

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LLMs on YCRC Research Computing Systems

MICHAEL ROTHBERG, PHD�YALE CENTER FOR RESEARCH COMPUTING

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Why YCRC HPC?

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No cost for Researchers to use

Reduce spending from $10,000/month to $0!

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If you need extra storage or priority compute?

• At least 10x cheaper than commercial providers

Data Privacy

1,000,000+ free, downloadable models

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Parameter tuning, RAG, fine-tuning, etc

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Model comparisons in same interface

Flexibility

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McCleary

Grace

Bouchet

Milgram

Hopper

Medium-Risk data

20 GPUs

High-Risk data

172 GPUs

120 GPUs

96 GPUs

152 GPUs

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http://hpcllm.ycrc.yale.edu/

Course OnDemand Access:

Logged in as hpcllm_<netid>

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Running Ollama

Give ollama access to a shared model directory

echo 'export OLLAMA_MODELS=/nfs/roberts/project/hpcllm/shared/.ollama' >> .bashrc

mkdir ~/ycrc_llm_workshop

cp /apps/data/training/hpc_llm/ollama.ipynb ~/ycrc_llm_workshop

Make a directory to store the workshop notebook

Copy the workshop notebook

Running Ollama

• Hit enter to return to terminal

• Load YCRC installed Ollama

• Compute node with 10GB CPU memory

• Launch server to manage ollama models
• & forces server to run in background to avoid needing new terminal

• See available models
• llama3.3:70b
• llama3.1:8b

• Run a model

$ salloc -p education --mem=10G

$ ollama run llama3.1:8b

$

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$ ollama serve &

$ module load ollama

Running Ollama

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Enter Prompt: “Why is the sky blue?”

>>> /bye

CTRL-C

Cancel prompt

Exit model

$ exit

Exit the compute node

LLMs

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Running Ollama – with a GPU!

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• Hit enter to return to terminal

$ salloc -p education_gpu --gpus=1

$ nvidia-smi

$ nvidia-smi

$ ollama serve &

$ ollama list

$ ollama run llama3.1:8b

Running Ollama – with a GPU!

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Exit ollama model:�

Now run a different model:

Enter Prompt:

$ ollama run llama3.3:70b

>>> /bye

>>> Why is the sky blue?

Enter Prompt:

>>> Why is the sky blue?

>>> /bye

$ CTRL-C

Cancel prompt

Exit model

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LLM memory requirements?

YCRC GPU Availability

Hardware Memory Limits (LLMs)

CPU Memory (RAM)

• SLURM controlled:
• --mem
• --mem-per-cpu
• Maximum
• 500 – 1000 GB
• If overloaded?
• Out of memory errors (OOM)
• Immediate Failure

GPU Memory (vRAM)

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• Automatically given
• Max of requested GPU
• Maximum (1 GPU)
• 11 – 141 GB
• If overloaded?
• Overflows into CPUs
• Slower

Knowing your LLM’s vRAM requirements

Mathematical way

• Estimate
• Quick calculation
• Requires knowing defaults

Literate way

• More accurate
• Dependent on documentation

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Calculating LLM size

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Parameters

# of trained weights in the LLM

• Controls number of calculations involved when generating output

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Input

Output

Pros:

• Greater generality
• Better at complex inference
• More knowledge

Cons:

• Takes longer to run
• Requires stronger GPUs
• Longer queue times
• Requires more vRAM

YCRC Recommendation:

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Start small and scale up

How to find? 🡪 It is in the model name! 🡪 ModelName_ModelVersion:70b

8b

Precision

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# of bits used to store the value and digits of parameters

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• FP32
• FP16
• INT8
• INT4

A higher precision (FP32) improves accuracy of model at cost of performance and memory

Random model:

32 bit

Size: 100 Gb vRAM

16 bit

Size: 50 Gb

8 bit

Size: 25 Gb vRAM

4 bit

Size: 12.5 Gb vRAM

Inference/RAG:

Fine-tuning

Precision

Fine-tuning: Requires higher precision to ensure values aren’t lost when retraining models��RAG/Inference: Low precision performs well with usually no visible loss in accuracy

Why it matters:

Faster access to resources

More GPU flexibility

Faster Research Progression

Almost never

Precision – How do I know for my model?

Defaults:

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Ollama model website: https://ollama.com/library

• INT4 (inference/RAG models)
• Can find different precisions by clicking model

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Huggingface model website: https://huggingface.co/models

• FP32
• Controlled by dtype=Auto
• Scales based on GPU
• YCRC recommendation: choose own precision

Poll Everywhere multiple choice poll activity�Activity Title: How much vRAM do I need to load LLama 405b at 8 bit precision�Slide 14

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Answer with formula

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Llama3.3:70b

Pulled from ollama model library (default is 4)

Knowing your LLM’s vRAM requirements

Mathematical way

• Estimate
• Quick calculation
• Requires knowing defaults

Literate way

• More accurate
• Dependent on documentation

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Navigate to https://ollama.com/library

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Llama3.3

https://ollama.com/library

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Command to run model with ollama

vRAM requirements

Formula only provides estimate:�70b at 4 bit precision = 35GB, real cost = 43 GB

To see other versions of model, including other precisions

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5 bit precision

6 bit precision

8 bit precision

16 bit precision

To run a model, click the model:

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Poll Everywhere multiple choice poll activity�Activity Title: What cluster(s) are capable of running the fp8 405b (405 GB) model?�Slide 15

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LLM memory requirements?

YCRC GPU Availability

https://ollama.com/library

• Size of models
• Different precisions of models
• How to run models

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Inference/RAG = 4 bit/8bit

Fine-tuning = 16 bit

Increasing values = increased memory

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YCRC GPUs

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GPU node

H200 Node

--nodes=1 or –N 1

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LLM vRAM reqs can be spread on multiple GPUs on a single node

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Selecting YCRC GPUs

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#SBATCH --Constraint=a100 or –C a100

https://docs.ycrc.yale.edu/clusters/grace/#partitions-and-hardware

https://docs.ycrc.yale.edu/clusters/mccleary/#partitions-and-hardware

https://docs.ycrc.yale.edu/clusters/bouchet/#partitions-and-hardware

(Also applies to Hopper and Milgram)

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Interactive

Submission

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Need: GPU mem > 16 GB 🡪 --Constraint=“a5000|a100”

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LLM memory requirements?

YCRC GPU Availability

https://ollama.com/library

• Size of models
• Different precisions of models
• How to run models

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Inference/RAG = 4 bit/8bit

Fine-tuning = 16 bit

Increasing values = increased memory

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1. Small LLMs (< 7b parameters)
1. Any cluster
2. Medium LLMs (4 bit 70b parameters)
• Grace/Bouchet
3. Any larger
• Bouchet

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Ollama can use multiple GPUs on same node�� Can request specific GPUs with –C flag��salloc –p gpu_devel --gpus=2 –C “a100|v100”

Ollama python interface

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Ollama in Jupyter/Python

Already done – don’t need to follow!

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Recipe:

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Compute node!

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Can install other packages (pandas, etc) as needed

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http://hpcllm.ycrc.yale.edu/

Course OnDemand Access:

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education_gpu

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Necessary step!

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Launches server that allow ollama to load and submit inputs to LLMs

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Sleep 2 -> forces notebook to pause for 2 seconds to ensure server completes launching

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Control quality/consistency/style of output provided by LLM

LLM Generation Settings

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Temperature

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Temperature

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Essentially, decreasing temperature improves reproducibility but reduces creativity

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Try it Yourself!��Modify temperature in the jupyter notebook and see how the experiment design changes.

LLM Generation Settings

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Top_p

Controls the number of possible words considered during text generation

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Prompt

Dog 0.6

Cat 0.3

Mouse 0.1

Output

Top_p

Controls the number of possible words considered during text generation

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Prompt

Dog 0.6

Cat 0.3

Mouse 0.1

Dog/Cat/

Mouse

Top_p=1.0

Top_p

Controls the number of possible words considered during text generation

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Prompt

Dog 0.6

Cat 0.3

Mouse 0.1

Dog/Cat

Top_p=0.9

Top_p

Controls the number of possible words considered during text generation

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Prompt

Dog 0.6

Cat 0.3

Mouse 0.1

Dog

Top_p=0.5

Set Temperature back to 1.0 (changes aren’t obvious at low temperatures)

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Try it Yourself!��Modify top_p in the jupyter notebook and see how the experiment design changes.

LLM Generation Settings

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Num_ctx

Length of tokens remembered by LLM

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Maximum value – found on ollama model webpage for model

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LLM Generation Settings

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Reproducibility and Modelfiles

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Reproducibility!

Reproducibility and Modelfiles

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FROM llama3.1:8b

PARAMETER temperature 0.3

PARAMETER top_p 0.9

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~

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Original model

New Settings

$ vim Modelfile

$ ollama create my_model_<netid> –f Modelfile

Exercise 3: Create and test a Modelfile

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Step 1: Modify Generation Settings to answer prompt

1. Choose a prompt:
1. Scientific response – Evaluate the strengths and weaknesses of using machine learning to predict disease risk
2. Ideation – Propose innovative ways to reduce plastic pollution in the ocean
3. Succinctly response – Define entropy and its role in thermodynamics�
2. Modify Settings to get an answer you like

Step 2: Save your parameters in a Modelfile

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1. Create a file, Modelfile, using text editor of choice and input parameters
2. Use ollama create command to create modified LLM

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Step 3: Run your saved model in jupyter and test it on all three prompts above. Compare how tone, creativity, and focus change.

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Key Points: From GPUs to Reproducible LLM Research

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Future AI/ML Developments for YCRC

New GPUs!!! – B200s

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Localized Server to run inference on YCRC systems

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Planned Training

1. Workshop on RAG and fine-tuning
2. Workshop on running neural networks
3. Workshop on running machine learning models

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Planned documentation:

1. Jupyter notebook templates to run basic versions of ai/ml models
2. Jupyter notebooks to understand loading single vs multiple GPUs
3. Expansion of current LLM documentation

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External Resources

ACCESS

• NSF funded resources for researchers: https://www.youtube.com/watch?v=hspbZlQvP0Q
• https://allocations.access-ci.org/

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NAIRR

• NSF funded allocations for AI Research and education
• Includes cloud credits and API credits
• https://nairrpilot.org/

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Contacting YCRC

Any questions or issues about research?

• reach out to HPC@Yale.edu

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Questions (about LLMs or anything else)

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