VLM Architecture Trade-offs: When to Specialize vs Generalize

The Core Dilemma

Should you build one general purpose any to any VLM that handles text, images, video, and audio, or compose specialized components that excel at specific tasks? This is not an academic question. The wrong choice costs millions in infrastructure and months of engineering time.

When Generalization Wins

General VLMs like Qwen 2.5 Omni or GPT 4o make sense when your product needs true cross modal reasoning. For example, analyzing a dashboard screenshot while listening to a recorded user complaint and reading chat history. The model must understand relationships between what the user said (audio), what they saw (image), and what they typed (text). Splitting this across three specialized models loses context and introduces synchronization complexity. The cost: these models are large (70B to 200B parameters), require multi GPU inference, and run slower. A single query with 2 images, 30 seconds of audio, and 500 tokens of text might take 5 to 10 seconds at p99 and cost $0.02 to $0.05 per request. At 1 million requests per day, that is $20,000 to $50,000 daily.

When Specialization Wins

For document heavy workloads like invoice processing, receipt extraction, or form filling, a specialized OCR model followed by a text reasoning model is dramatically cheaper. DeepSeek OCR processes an invoice image in 200ms and outputs 300 compressed tokens. A 7B text model then extracts fields in another 150ms. Total latency: 350ms. Total cost per request: $0.0005. Compare this to a general 70B VLM that processes the raw image (4,096 tokens) in 1.5 seconds at $0.003 per request. The specialized pipeline is 4x faster and 6x cheaper. At 10 million invoices per month, you save $25,000 monthly.

The Mixture of Experts (MoE) Middle Ground

MoE architectures activate only a fraction of parameters per token. A 70B MoE model might activate only 14B parameters per forward pass, giving 70B model quality at roughly 20B model cost. This is why models like Qwen 2.5 and Gemini use MoE decoders. The tradeoff: MoE adds serving complexity. Load imbalance between experts causes tail latency spikes. If one expert handles 3x more tokens than others, that GPU becomes a bottleneck. You need sophisticated load balancing and potentially expert replication.

Model Size vs Deployment

Mobile and edge deployment changes the calculus entirely. A 200MB model barely fits on a smartphone and drains battery. Quantized small models like SmolVLM or Gemma 3 4B (compressed to 20MB to 50MB) run on device with only 2% to 5% accuracy drop compared to full precision versions. The decision framework: If your product requires offline operation or sub 100ms latency (camera apps, AR filters), on device small models are mandatory. If accuracy is paramount (medical diagnosis, legal analysis), server side large models are worth the latency and cost.