Inference: What is Inference in Artificial Intelligence?

What is Inference?

Inference is the process by which a trained artificial intelligence model uses acquired knowledge to process new data and produce a result. If training is the learning phase (the model learns from billions of examples), inference is the application phase (the model uses what it learned to answer new queries). Every API call to GPT-4 or Claude, every question asked to a chatbot, every image analyzed by a vision model triggers an inference process.

For LLMs, inference works autoregressively: the model generates one token at a time, with each new token conditioned on all previous tokens (the prompt plus already-generated tokens). This sequential process explains why long text generation takes time — each token requires a full pass through the model's billions of parameters.

Inference represents the dominant operational cost of AI systems in production. Unlike training, which is a one-time (or periodic) cost, inference is a recurring cost that increases with the number of users and requests. Optimizing inference is therefore a major economic and technical challenge for any company deploying AI at scale.

Why Inference Matters

Inference is the moment of truth for AI systems: it is where the model creates value for the end user. Its performance directly determines user experience and the economic viability of applications.

• Latency: inference time defines application responsiveness. A chatbot that takes 10 seconds to respond offers a poor experience. Time to First Token (TTFT) and throughput in tokens per second are the key metrics.
• Costs: for LLMs via API, every generated token has a cost. At scale (millions of requests per month), inference optimization can represent savings of tens of thousands of euros.
• Scalability: inference infrastructure must handle load spikes without performance degradation. GPU server sizing is both a technical and financial challenge.
• Quality: inference parameters (temperature, top-p, maximum length) directly influence the quality and relevance of generated responses.
• Availability: in production, inference must be reliable 24/7. LLM API outages directly affect end users, requiring fallback strategies.

How It Works

The LLM inference process occurs in two main phases. The prefill phase processes the entire prompt in parallel: all prompt tokens pass simultaneously through the Transformer layers, and the model computes contextual representations (KV-cache). This phase is compute-intensive but can be efficiently parallelized on GPUs.

The decoding phase generates output tokens one by one. For each new token, the model consults the KV-cache (avoiding recalculation of prompt representations), performs a pass through the layers, and produces a probability distribution over the entire vocabulary. The selected token is added to the sequence, and the process repeats. The KV-cache is a crucial optimization that avoids recalculating attention for already-processed tokens.

Several optimization techniques accelerate inference. Quantization reduces model weight precision (from 16 bits to 8 or 4 bits), reducing required memory and accelerating computation with minimal quality loss. Continuous batching groups multiple requests to maximize GPU utilization. Speculative decoding uses a small, fast model to propose multiple tokens at once, then validated by the large model.

Concrete Example

At Kern-IT, KERNLAB optimized inference for the A.M.A assistant to ensure a smooth user experience while controlling costs. The system uses intelligent request routing: simple questions (classification, data extraction) are handled by a lightweight, fast model (Claude Haiku or GPT-4o-mini), while complex tasks (document analysis, multi-step reasoning) are directed to more powerful models (Claude Sonnet or Opus). This strategy reduced inference costs by 60% while maintaining user-perceived quality.

For an e-commerce client, Kern-IT deployed an LLM-powered recommendation system that must respond in under 200 milliseconds to avoid slowing the purchase journey. Optimization combined semantic caching (similar queries reuse previously generated responses), prefix caching, and streaming to progressively display the response.

Implementation

1. Define SLAs: establish latency targets (TTFT, throughput), availability, and cost per request based on application requirements.
2. Choose infrastructure: managed API (OpenAI, Anthropic) for simplicity, or on-premise deployment (vLLM, TGI) for control and sensitive data.
3. Implement routing: direct requests to the most suitable model (small model for simple tasks, large model for complex tasks).
4. Enable caching: implement prefix caching and semantic caching to reduce redundant computations and costs.
5. Configure streaming: use token streaming to improve perceived latency on the user side, even when total generation takes time.
6. Monitor continuously: track latency (P50, P95, P99), error rate, cost per request, and throughput to identify degradations and optimize.

Associated Technologies and Tools

• Inference servers: vLLM, TGI (Hugging Face), TensorRT-LLM (NVIDIA) for open-source model deployment
• Managed APIs: OpenAI API, Anthropic API, Google Vertex AI, Azure OpenAI Service, AWS Bedrock
• Optimization: quantization (GPTQ, AWQ, GGUF), speculative decoding, continuous batching, Flash Attention
• Caching: prefix caching (Anthropic), GPTCache for semantic caching, Redis for application cache
• Monitoring: LangSmith, Helicone, Portkey for inference performance and cost tracking

Conclusion

Inference is the economic engine of AI in production. It is the process that transforms a trained model into concrete value for the user. At Kern-IT, KERNLAB masters inference optimization techniques to ensure fast, reliable, and economically viable AI applications. The intelligent routing strategy — using the right model for the right task — is at the heart of Kern-IT's approach, ensuring the best quality-to-cost ratio for each use case.