What is the best large language model (LLM) to run on RunPod?

Best Large Language Models (LLMs) to Run on RunPod

Choosing the right Large Language Model (LLM) to run on RunPod depends on your specific use case, available hardware, and performance requirements. RunPod provides GPU-accelerated cloud infrastructure ideal for running powerful LLMs efficiently. Here are some of the best and most popular LLMs you can deploy on RunPod:

1. GPT-Based Models (Llama 2, GPT-J, GPT-NeoX)

GPT-based models developed by Meta (Llama 2), EleutherAI (GPT-J, GPT-NeoX), and others are highly popular open-source models suitable for various NLP tasks, including text generation, question-answering, and chatbots.

Recommended GPT-Based Models:

• Llama 2: Meta's powerful open-source LLM with excellent performance and flexibility.
• GPT-J (6B parameters): Ideal compromise between performance and GPU resource requirements.
• GPT-NeoX (20B parameters): Larger model with higher accuracy and better text quality, but requiring more GPU memory.

Hardware Recommendation:

• Llama 2 (7B parameters): Recommended GPU: NVIDIA A10G or RTX A5000 (24 GB VRAM)
• GPT-J (6B parameters): Recommended GPU: NVIDIA RTX A5000 or A6000
• GPT-NeoX (20B parameters): Recommended GPU: NVIDIA A100 or multiple RTX A6000 GPUs

Example Deployment with Hugging Face Transformers:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = 'meta-llama/Llama-2-7b-chat-hf'

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name).cuda()

prompt = "What is the capital city of France?"
inputs = tokenizer(prompt, return_tensors='pt').to('cuda')
outputs = model.generate(**inputs, max_length=50)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)

2. Mistral AI Models (Mistral 7B, Mixtral)

Mistral AI's models such as Mistral 7B and Mixtral offer excellent performance with optimized resource utilization. They are highly suitable due to their strong balance between accuracy, speed, and GPU memory requirements.

Recommended Mistral AI Models:

• Mistral 7B: Fast, compact, and effective at many NLP tasks.
• Mixtral (8x7B): Mixture-of-experts model providing significantly better performance and scalability.

Hardware Recommendation:

• Mistral 7B: NVIDIA RTX A5000, RTX A6000, or A10G
• Mixtral (8x7B): NVIDIA A100 (80GB) or multiple GPUs recommended

Example Deployment with Hugging Face:

from transformers import AutoModelForCausalLM, AutoTokenizer

model_name = "mistralai/Mistral-7B-Instruct-v0.2"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Explain the theory of relativity briefly."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=100)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)

3. Falcon Models (Falcon-7B, Falcon-40B)

Falcon models developed by Technology Innovation Institute (TII) offer state-of-the-art performance and efficiency, especially with Falcon-7B and Falcon-40B.

Recommended Falcon Models:

• Falcon-7B: Smaller and highly efficient for general-purpose tasks.
• Falcon-40B: Larger and more accurate, suitable for demanding applications but requires substantial GPU resources.

Hardware Recommendation:

• Falcon-7B: NVIDIA RTX A5000 or RTX A6000
• Falcon-40B: NVIDIA A100 or multiple GPUs

Example Deployment with Hugging Face:

from transformers import AutoTokenizer, AutoModelForCausalLM

model_name = "tiiuae/falcon-7b-instruct"

tokenizer = AutoTokenizer.from_pretrained(model_name)
model = AutoModelForCausalLM.from_pretrained(model_name, device_map="auto")

prompt = "Summarize the benefits of renewable energy."
inputs = tokenizer(prompt, return_tensors="pt").to("cuda")
outputs = model.generate(**inputs, max_new_tokens=50)
response = tokenizer.decode(outputs[0], skip_special_tokens=True)

print(response)

Factors to Consider When Choosing an LLM for RunPod

When deciding on the best LLM for your RunPod deployment, consider the following factors:

• GPU resources available: Larger models require more VRAM and computational power.
• Latency vs. accuracy trade-off: Smaller models offer faster inference but lower accuracy; larger models provide better performance but higher latency.
• Cost: GPU type and number of GPUs directly impact costs.
• Open-source availability: Open-source models provide flexibility and ease of deployment.

Recommended RunPod GPU Instances by Model Sizes:

• Small Models (7B parameters): NVIDIA RTX A5000 or A10G (24GB VRAM)
• Medium Models (13B - 20B): NVIDIA RTX A6000 (48GB VRAM) or A100 (40GB/80GB VRAM)
• Large Models (40B+ parameters): NVIDIA A100 (80GB) or multiple GPU instances

Conclusion: Best LLM for RunPod

In summary, the best LLM to run on RunPod currently includes Llama 2, Mistral 7B, and Falcon-7B due to their excellent balance of performance, efficiency, and GPU resource requirements. For larger and more demanding use cases, Mixtral, GPT-NeoX, or Falcon-40B offer superior accuracy at the cost of increased GPU resources.

RunPod's flexible GPU infrastructure makes it easy to experiment with different models and configurations to find the ideal LLM for your specific needs.