Powering Deeper Insights with Vision-Language Models on Nebula Block

Introduction

NVIDIA’s recent release of Llama Nemotron Nano VL marks a pivotal advancement in vision-language models optimized for document understanding. While Nemotron Nano VL sets new benchmarks for extracting complex data from PDFs, diagrams, and tables, emerging trends in AI point toward compact, energy-efficient models that empower real-world applications without the overhead of traditional GPU deployments. Although Nebula Block’s current offerings are focused on large language models, our cost-efficient, serverless GPU platform proactively paves the way for incorporating specialized vision-language capabilities in future releases.

The Power of Vision-Language Models

Vision-Language Models (VLMs), such as the 8B-parameter Llama Nemotron Nano VL, integrate language models (e.g., Llama 3.1) with vision encoders (e.g., CRadioV2-H) to process multi-page documents with up to 16K token context lengths. These models excel in tasks like invoice parsing, contract analysis, and table extraction, rivaling larger models like Claude 3.5 Haiku on benchmarks like OCR Bench v2. Their compact design (24GB vRAM for float16) ensures low-latency, energy-efficient inference, making them ideal for startups and enterprises.

Nebula Block’s platform is built to power VLM workloads with:

• Cost Savings: Save 30% on compute costs ($1.95/hour for A100 vs. $3.40/hour on opponents).
• Scalability: Global infrastructure supports high-volume tasks like real-time analytics.
• Ease of Integration: API-first design and Hugging Face compatibility simplify workflows.
• vLLM Integration: Optimized inference reduces latency for real-time document processing.
• Future-Ready: Built to support emerging VLMs for multimodal AI applications.

Querying VLMs on Nebula Block

You can use Multimodal Models or Vision Models. To illustrate the ease of deployment on Nebula Block, consider this API example from our Qwen2.5-VL-7B-Instruct release:

import requests 
import os
 
url = "https://inference.nebulablock.com/v1/chat/completions"

headers = { 
    "Content-Type": "application/json", 
    "Authorization": f"Bearer {os.environ.get('NEBULA_API_KEY')}" 
} 
 
data = {
    "messages":[
		{"role":"user","content":[
		{"type":"image_url","image_url":
		{"url":"https://qianwen-res.oss-cn-beijing.aliyuncs.com/Qwen-VL/assets/demo.jpeg"}},
		{"type":"text","text":"What is this image?"}
	]}],
    "model":"Qwen/Qwen2.5-VL-7B-Instruct",
    "max_tokens":None,
    "temperature":1,
    "top_p":0.9,
    "stream":False
}

response = requests.post(url, headers=headers, json=data) 
print(response.json())

Conclusion and Next Steps

NVIDIA’s Llama Nemotron Nano VL offers a glimpse into the future of document understanding, emphasizing efficiency and specialized performance for vision-language tasks. Nebula Block provides a strong foundation for incorporating emerging AI models as they mature.
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