AI/ML
Model Inference
What model inference is and why it matters
Definition
Model inference is the process of running a trained AI model on new data to produce outputs - predictions, classifications, generated text, or other results. Inference is distinct from training: training builds the model, inference uses it. In production AI systems, inference determines response latency, compute costs per request, and overall system performance.
How it works
Training a model happens once (or periodically). Inference happens every time a user interacts with the AI. For a customer-facing chatbot, inference might happen thousands of times per hour. This is why inference cost and latency matter more than training cost for most businesses - training is a one-time investment, but inference is an ongoing operational expense.
Inference speed depends on model size, hardware, and optimization. A 7-billion-parameter model runs faster and cheaper than a 70-billion-parameter model. Techniques like quantization (reducing numerical precision), batching (processing multiple requests together), and caching (storing common responses) can dramatically reduce inference costs without meaningful quality loss.
For production applications, the infrastructure choice for inference matters. Options range from API providers (OpenAI, Anthropic) where you pay per token, to self-hosted models on GPU servers where you control costs but manage infrastructure. The right choice depends on volume, latency requirements, data privacy constraints, and budget.
How 1Raft uses Model Inference
We optimize inference for every AI product we ship. In a high-traffic commerce application, we reduced inference costs by 60% by switching from GPT-4 to a fine-tuned smaller model for product classification tasks without sacrificing accuracy. We model inference costs during architecture planning so clients understand their per-user and per-request economics before committing to a technical approach.
Related terms
AI/ML
Large Language Model (LLM)
A large language model is a neural network trained on massive text datasets to understand and generate human language. LLMs power chatbots, content generation, code assistants, and most modern AI products.
AI/ML
Token (AI Context)
A token is the basic unit of text that a language model processes. Words, parts of words, and punctuation are all broken into tokens. Token counts determine model costs, context window limits, and response length constraints.
AI/ML
Fine-Tuning
Fine-tuning is the process of training a pre-trained AI model on a smaller, domain-specific dataset to adapt its behavior for a particular task. It modifies the model's internal weights so it performs better on your specific use case without training from scratch.
AI/ML
Transformer Architecture
The transformer is the neural network architecture behind virtually all modern language models. Introduced in 2017, it uses a mechanism called self-attention to process entire sequences of text in parallel, making it far more efficient and capable than previous approaches.
AI/ML
MLOps
MLOps (Machine Learning Operations) is the set of practices for deploying, monitoring, and maintaining machine learning models in production. It applies DevOps principles to ML systems, keeping models accurate, reliable, and cost-effective after launch.
Related services
Next Step
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We apply this in production across industries. Tell us what you are building and we will show you how it fits.