Multi-modal AI Infrastructure: Vision-Language Model Deployment Guide

Updated December 11, 2025

December 2025 Update: Open-source VLMs (Qwen2.5-VL-72B, InternVL3-78B) now within 5-10% of proprietary OpenAI/Google models. Google Gemini built from ground up as multimodal (text, code, audio, images, video). Meta Llama 4 introducing early fusion for shared latent spaces across modalities. Multimodal workloads require more memory, different batching, specialized serving vs. text-only LLMs.

Open-source vision-language models like Qwen2.5-VL-72B and InternVL3-78B now perform within 5-10% of proprietary models from OpenAI and Google.¹ The performance convergence transforms multimodal AI from a capability reserved for hyperscaler APIs into infrastructure organizations can deploy, fine-tune, and control. But multimodal workloads demand fundamentally different infrastructure than text-only LLMs—simultaneous processing of images, video, and text requires more memory, different batching strategies, and specialized serving configurations.

Multimodal models represent the trajectory of AI development. Google built Gemini from the ground up as a multimodal system, processing text, code, audio, images, and video in a unified architecture.² Meta's Llama 4 introduced early fusion designs that create shared latent spaces across modalities.³ Understanding the infrastructure requirements for serving these models—memory allocation, GPU selection, architecture patterns, and deployment strategies—helps organizations prepare for workloads that will increasingly define production AI.

Multimodal architecture fundamentals

Fusion strategies

How models combine visual and textual information determines infrastructure requirements:⁴

Early fusion: Models process raw multimodal inputs together from the start. Visual tokens and text tokens enter the same transformer architecture, creating shared representations.

• Examples: Chameleon, Gemini, Llama 4
• Advantages: Better cross-modal understanding, captures fine-grained interactions
• Requirements: Higher computational resources, synchronized inputs
• Infrastructure impact: More memory for combined token sequences

Late fusion: Models process each modality independently, combining results at decision time. Separate encoders handle vision and language before integration.

• Examples: Earlier CLIP-based architectures
• Advantages: Flexibility, fault tolerance, simpler inference
• Requirements: Less memory pressure during individual encoding
• Infrastructure impact: Can parallelize modality-specific processing

Apple Research findings (April 2025): Research demonstrated that early-fusion and late-fusion approaches perform comparably when trained from scratch, with early-fusion showing advantages at lower compute budgets while being more efficient to train. Sparse architectures using Mixture of Experts naturally develop modality-specific specialization, improving performance without increasing inference costs.

Architecture patterns

Adapter-based (vision encoder + LLM):⁵ A pretrained vision encoder (like SigLIP or ViT) extracts visual features, which an adapter layer projects into the LLM's embedding space. The LLM then processes combined visual and text tokens.

Image → Vision Encoder → Adapter → LLM (with text tokens) → Output

• Memory: Vision encoder + adapter + LLM weights
• Examples: LLaVA, Qwen-VL, InternVL
• Inference: Vision encoding happens once per image; text generation follows standard LLM patterns

Native multimodal (unified architecture):⁶ The model handles all modalities within a single architecture, trained jointly on multimodal data from the start.

[Image Tokens + Text Tokens] → Unified Transformer → Output

• Memory: Single model weight set (typically larger)
• Examples: Gemini, GPT-4V
• Inference: All tokens processed together

Mixture of Experts (MoE) multimodal: Sparse expert architectures activate subsets of parameters per token. DeepSeek-VL2 activates only 1-2.8 billion of 4.5 billion total parameters per input, cutting inference latency 50-70% compared to dense models.⁷

Memory requirements

Model size and VRAM

Multimodal models require more memory than text-only equivalents due to vision encoders and longer context from image tokens:⁸

Memory calculation:

Weight Memory = Parameters × Bytes per Parameter

FP16: Parameters × 2 bytes
FP8:  Parameters × 1 byte
INT4: Parameters × 0.5 bytes

Example (72B model in FP16):
72B × 2 = 144 GB VRAM for weights alone

KV cache for images: Each image generates hundreds to thousands of tokens in the KV cache. A single 1024×1024 image might produce 256-1024 visual tokens, each requiring cache storage proportional to sequence length and batch size.

GPU configurations

Image resolution impact: Higher resolution images generate more tokens. Models supporting 4K input may produce 4-16x more visual tokens than 512×512 inputs, dramatically increasing memory requirements.

Memory optimization

Quantization strategies:⁹

AWQ (Activation-aware Weight Quantization): Delivers 4x memory savings with better quality preservation than GPTQ. Often runs 2x faster on GPUs. Recommended for production VLM deployment.

FP8 quantization: Available on H100/H200/B200 hardware. Provides 2x memory reduction with minimal quality loss. Enables running 70B+ VLMs on single 8-GPU nodes.

Flash Attention: Reduces memory complexity for attention computation from O(n²) to O(n). Critical for long image token sequences.

KV cache optimization: PagedAttention (vLLM) manages KV cache efficiently through paging. Prevents memory fragmentation that accumulates with variable-length image inputs.

Serving infrastructure

vLLM for multimodal

vLLM supports multimodal models with specific configuration:¹⁰

from vllm import LLM, SamplingParams

# Initialize multimodal model
llm = LLM(
    model="Qwen/Qwen2.5-VL-72B-Instruct",
    tensor_parallel_size=4,  # Distribute across 4 GPUs
    gpu_memory_utilization=0.9,
    max_model_len=32768,
    trust_remote_code=True,
)

# Process image + text
sampling_params = SamplingParams(
    temperature=0.7,
    max_tokens=2048,
)

outputs = llm.generate(
    [
        {
            "prompt": "Describe this image in detail:",
            "multi_modal_data": {"image": image_data}
        }
    ],
    sampling_params=sampling_params
)

Key configurations: - tensor_parallel_size: Distribute model across GPUs for large VLMs - gpu_memory_utilization: Balance between throughput and headroom - max_model_len: Account for image tokens in context budget

TensorRT-LLM multimodal

NVIDIA's optimized inference with multimodal support:¹¹

Supported models: - LLaVA variants - Qwen-VL - InternVL - Custom vision-language architectures

Optimization features: - FP8 quantization for H100/B200 - Tensor parallelism across GPUs - Inflight batching for mixed workloads - Vision encoder optimization

Triton Inference Server

Deploy multimodal pipelines with Triton:¹²

Client Request
     │
     ▼
┌─────────────────────┐
│  Triton Ensemble    │
├─────────────────────┤
│  ┌───────────────┐  │
│  │ Image Encoder │  │ (Vision preprocessing)
│  └───────┬───────┘  │
│          │          │
│  ┌───────▼───────┐  │
│  │  VLM Backend  │  │ (Main model inference)
│  └───────┬───────┘  │
│          │          │
│  ┌───────▼───────┐  │
│  │ Postprocessor │  │ (Response formatting)
│  └───────────────┘  │
└─────────────────────┘

Benefits: - Pipeline orchestration for complex workflows - Model version management - Metrics and monitoring - Multi-framework support

Batching strategies

Multimodal batching differs from text-only LLMs:¹³

Image preprocessing batching: Batch image encoding separately from text generation. Vision encoders process images in parallel before LLM inference.

Dynamic batching with variable images: Requests with different image counts create batching complexity. Padding to maximum images per batch wastes compute.

Continuous batching: vLLM's PagedAttention enables continuous batching for multimodal models, though image token handling requires careful memory management.

Recommendation: Separate image encoding from text generation in production pipelines. Process images in batches, then feed visual embeddings to the LLM alongside text.

Leading multimodal models

Proprietary options

GPT-4V/GPT-4o (OpenAI):¹⁴ - Context: Up to 128K tokens - Capabilities: Image understanding, document analysis, visual reasoning - Infrastructure: API-only (no self-hosting) - Pricing: Per-token with image token costs

Gemini Pro/Ultra (Google): - Context: Up to 1M tokens - Capabilities: Native multimodal (text, image, audio, video) - Infrastructure: Vertex AI or API - Optimization: TPU v4/v5 optimized

Claude 3.5 (Anthropic): - Context: 200K tokens - Capabilities: Image understanding, document analysis - Infrastructure: API or Amazon Bedrock - Strength: Document and chart understanding

Open-source options

Qwen2.5-VL (Alibaba):¹⁵ - Sizes: 3B, 7B, 72B - Context: 32K tokens standard - Capabilities: Vision-language reasoning, agentic tasks - Infrastructure: Self-hostable, vLLM support - Best for: Agentic workflows, production deployment

InternVL3 (OpenGVLab): - Sizes: Up to 78B parameters - Capabilities: Near GPT-4V performance - Infrastructure: Full open weights - Best for: High-quality self-hosted vision

Llama 3.2 Vision (Meta): - Sizes: 11B, 90B - Capabilities: Image understanding - Infrastructure: Wide ecosystem support - Best for: Organizations already using Llama

DeepSeek-VL2: - Architecture: MoE with 1-2.8B active parameters - Efficiency: 50-70% latency reduction vs dense models - Best for: Cost-sensitive deployments

Model selection criteria

Production deployment patterns

Cloud deployment

Single-GPU inference (small models):

# Kubernetes pod for 7B VLM
resources:
  limits:
    nvidia.com/gpu: 1
    memory: "32Gi"
  requests:
    nvidia.com/gpu: 1
    memory: "24Gi"

Multi-GPU inference (large models):

# Kubernetes deployment for 72B VLM
resources:
  limits:
    nvidia.com/gpu: 4  # 4× H100 for 72B FP8
    memory: "512Gi"

Autoscaling considerations: - VLM cold starts are slower (loading vision encoder + LLM) - Maintain warm instances for latency-sensitive workloads - Scale based on GPU utilization and queue depth

Edge deployment

Edge VLM deployment enables on-device vision intelligence:¹⁶

RamaLama deployment: Container-native philosophy simplifies edge deployment:

# Deploy VLM to edge device
ramalama run qwen2.5-vl-3b

# Generate deployment artifacts for Kubernetes
ramalama generate --kubernetes qwen2.5-vl-3b

Edge-optimized models: - Mistral's lightweight VLMs for mobile/edge - MiniCPM-V outperforms GPT-4V while running on phones - DeepSeek-VL2 MoE for efficient edge inference

Use cases: - Smart glasses and AR headsets - In-car assistants - Industrial inspection systems - Retail automation

Serverless GPU

Serverless platforms handle scaling complexity:¹⁷

Benefits: - No infrastructure management - Pay-per-use economics - Automatic scaling - Cold start optimization

Platforms: - RunPod Serverless - Modal - Beam - Replicate

Considerations: - Cold starts impact latency for VLMs - Memory requirements limit model size on some platforms - Cost optimization requires careful batching

Safety and compliance

Multimodal safety

Vision-language models require content filtering for both inputs and outputs:¹⁸

Input filtering: - Screen images for harmful content before processing - Detect NSFW, violence, or policy-violating imagery - Apply content policies specific to use case

Output filtering: - Monitor generated text for harmful content - Detect hallucinations about image content - Verify factual claims against visual evidence

ShieldGemma 2 (Google, early 2025): First open multimodal safety model. Takes images and content policies as input, returns whether content is safe for given policies.

# Example: Multimodal safety pipeline
def process_with_safety(image, prompt, vlm, safety_model):
    # Check input image safety
    if not safety_model.is_safe_image(image, policies):
        return "Image violates content policy"

    # Generate response
    response = vlm.generate(image, prompt)

    # Check output safety
    if not safety_model.is_safe_text(response, policies):
        return "Response filtered for safety"

    return response

Data privacy

Multimodal models process potentially sensitive visual data:

Considerations: - Images may contain PII (faces, documents, screens) - Medical images require HIPAA compliance - Financial documents require appropriate controls - Location data embedded in image metadata

Mitigations: - Strip EXIF metadata before processing - Implement face blurring for privacy - Encrypt images in transit and at rest - Maintain audit logs of processed images

Cost optimization

API vs. self-hosted economics

API costs (example rates): - GPT-4V: ~$0.01-0.03 per image + text tokens - Gemini Pro Vision: Similar range - High-volume: $100K+/month possible

Self-hosted costs: - 4× H100 cluster: ~$15-20K/month (cloud) - Can process millions of images at fixed cost - Break-even typically at 100K-500K images/month

Inference optimization

Reduce image resolution: Process at minimum resolution meeting quality requirements. 512×512 vs 2048×2048 can reduce costs 4-16x.

Batch image encoding: Amortize encoding costs across requests. Cache encoded images when reprocessing same content.

Quantization: FP8 or INT4 quantization reduces memory and compute costs with minimal quality impact for most use cases.

Model selection: Use smallest model meeting quality requirements. 7B VLMs handle many tasks 72B models accomplish.

Organizations deploying multimodal AI infrastructure can leverage Introl's global expertise for GPU deployment and optimization across 257 locations worldwide.

The multimodal future

Multimodal AI represents the convergence of computer vision and language understanding into unified systems that perceive and reason about the world more like humans do. The infrastructure requirements—larger models, more memory, complex pipelines—reflect this expanded capability.

Organizations building multimodal AI infrastructure face a clear choice between API simplicity and self-hosted control. Proprietary APIs like GPT-4V and Gemini offer immediate capability without infrastructure investment. Open-source alternatives like Qwen2.5-VL and InternVL3 deliver competitive performance with full data control and customization potential.

The infrastructure decisions made today—GPU selection, serving architecture, safety pipelines—will determine how effectively organizations deploy multimodal capabilities. Models that understand images, process documents, analyze charts, and reason about visual content will increasingly define AI application possibilities. Building infrastructure that supports these workloads positions organizations for the AI capabilities that define the next generation of intelligent systems.

References

1. Koyeb. "Best Open Source Multimodal Vision Models in 2025." 2025. https://www.koyeb.com/blog/best-multimodal-vision-models-in-2025

2. Google. "Introducing Gemini: our largest and most capable AI model." 2023. https://blog.google/technology/ai/google-gemini-ai/

3. NVIDIA Technical Blog. "Securing Agentic AI: How Semantic Prompt Injections Bypass AI Guardrails." 2025. https://developer.nvidia.com/blog/securing-agentic-ai-how-semantic-prompt-injections-bypass-ai-guardrails/

4. MarkTechPost. "Multimodal Models Don't Need Late Fusion: Apple Researchers Show Early-Fusion Architectures are more Scalable, Efficient, and Modality-Agnostic." April 2025. https://www.marktechpost.com/2025/04/14/multimodal-models-dont-need-late-fusion-apple-researchers-show-early-fusion-architectures-are-more-scalable-efficient-and-modality-agnostic/

5. Label Your Data. "VLM: How Vision-Language Models Work (2025 Guide)." 2025. https://labelyourdata.com/articles/machine-learning/vision-language-models

6. Prateek Vishwakarma. "OpenAI GPT-4o (2025): Features, Capabilities & Multimodal AI Explained." 2025. https://prateekvishwakarma.tech/blog/gpt-4o-ultimate-guide-2025/

7. Hugging Face. "Vision Language Models (Better, faster, stronger)." 2025. https://huggingface.co/blog/vlms-2025

8. Introl. "Local LLM Hardware Guide 2025: Pricing & Specifications." 2025. https://introl.com/blog/local-llm-hardware-pricing-guide-2025

9. AIMultiple. "LLM VRAM Calculator for Self-Hosting." 2025. https://research.aimultiple.com/self-hosted-llm/

10. VMware. "LLM Inference Sizing and Performance Guidance." 2024. https://blogs.vmware.com/cloud-foundation/2024/09/25/llm-inference-sizing-and-performance-guidance/

11. NVIDIA. "TensorRT-LLM Documentation." 2025. https://github.com/NVIDIA/TensorRT-LLM

12. NVIDIA. "Triton Inference Server." 2025. https://developer.nvidia.com/triton-inference-server

13. IBM Research. "Mind the Memory Gap: Unveiling GPU Bottlenecks in Large-Batch LLM Inference." 2025. https://research.ibm.com/publications/mind-the-memory-gap-unveiling-gpu-bottlenecks-in-large-batch-llm-inference

14. Promptitude. "Ultimate 2025 AI Language Models Comparison." 2025. https://www.promptitude.io/post/ultimate-2025-ai-language-models-comparison-gpt5-gpt-4-claude-gemini-sonar-more

15. Dextra Labs. "Top 10 Vision Language Models in 2025." 2025. https://dextralabs.com/blog/top-10-vision-language-models/

16. Red Hat Developer. "Multimodal AI at the edge: Deploy vision language models with RamaLama." October 2025. https://developers.redhat.com/articles/2025/10/27/multimodal-ai-edge-deploy-vision-language-models-ramalama

17. RunPod. "Multimodal AI Development: Building Systems That Process Text, Images, Audio, and Video." 2025. https://www.runpod.io/articles/guides/multimodal-ai-development-building-systems-that-process-text-images-audio-and-video

18. Voxel51. "ICCV 2025 Papers Advancing Vision Language Models." 2025. https://voxel51.com/blog/iccv-papers-vision-language-models

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Key takeaways

For ML engineers: - Open-source VLMs (Qwen2.5-VL-72B, InternVL3-78B) within 5-10% of GPT-4V/Gemini performance - 72B VLM in FP16: 144GB VRAM for weights alone + KV cache for image tokens (256-1024 per image) - DeepSeek-VL2 MoE: 1-2.8B of 4.5B params active—50-70% latency reduction vs dense models

For infrastructure architects: - Early fusion (Gemini, Llama 4): higher memory for combined sequences; late fusion enables parallelized encoding - 72B VLM configs: FP16 = 2-4× H100; FP8 = 1-2× H100; INT4 = 1× H100 - Image resolution impact: 4K input generates 4-16x more visual tokens than 512×512

For production deployments: - vLLM multimodal: tensor_parallel_size for distributed serving; PagedAttention critical for variable image inputs - Batch image encoding separately from text generation; cache encoded images for repeated content - API vs self-hosted breakeven: typically 100K-500K images/month

For safety teams: - ShieldGemma 2 (Google, early 2025): first open multimodal safety model for input/output filtering - Strip EXIF metadata, implement face blurring, encrypt images in transit/at rest - Medical images require HIPAA compliance; financial documents need appropriate controls