Inference
Definition & meaning
Definition
Inference is the process of running a trained AI model to generate predictions or outputs from new input data. While training teaches a model to recognize patterns (and can take weeks on GPU clusters), inference is the real-time application of that learning — generating text, images, or classifications in milliseconds to seconds. Inference speed and cost are critical factors in production AI deployments: faster inference means better user experience, lower costs, and the ability to serve more requests. Cerebras Inference has pioneered hardware-optimized inference chips that achieve dramatically faster token generation than GPU-based solutions. The inference cost per token has dropped 100x since 2023, making AI applications increasingly viable at scale.
How It Works
Inference is the process of running a trained model to generate predictions or outputs from new inputs. While training teaches the model by updating billions of parameters over weeks using massive GPU clusters, inference uses those frozen parameters to produce results in real time. For LLMs, inference works autoregressively: the model generates one token at a time, with each new token conditioned on all previous tokens. This creates a sequential bottleneck—generating 1,000 tokens requires 1,000 forward passes through the network. Key optimizations include KV-caching (storing computed attention states so they're not recalculated for each new token), batching (processing multiple requests simultaneously to maximize GPU utilization), quantization (reducing weight precision from FP16 to INT8 or INT4 to reduce memory and compute), and speculative decoding (using a smaller draft model to propose tokens that the larger model verifies in parallel). Inference infrastructure spans cloud APIs (OpenAI, Anthropic), managed platforms (Together AI, Fireworks AI), and self-hosted solutions using frameworks like vLLM, TensorRT-LLM, or llama.cpp.
Why It Matters
Inference is where AI costs actually accumulate. Training a model is a one-time expense; inference runs every time a user sends a message. For production applications serving thousands of concurrent users, inference optimization directly determines your margins. Latency matters too—users expect sub-second first-token times for chat applications. Understanding inference helps developers choose the right serving stack: a vLLM deployment on dedicated GPUs for high-throughput workloads, or a managed API for variable demand. Quantization trade-offs (speed versus quality), batching strategies, and caching patterns are practical engineering decisions that compound into significant cost and performance differences at scale.
Real-World Examples
OpenAI and Anthropic charge per-token for inference through their APIs—the dominant deployment model for most applications. Together AI and Fireworks AI offer optimized open-source model inference at lower prices. Groq uses custom LPU hardware to achieve extremely low inference latency. For self-hosting, vLLM is the leading open-source inference engine, while llama.cpp enables CPU-based inference on consumer hardware. On ThePlanetTools.ai, we benchmark inference speed and cost in our reviews—comparing time-to-first-token and tokens-per-second across providers, which directly impacts user experience in tools like Cursor, ChatGPT, and other AI-powered applications.
Tools We've Reviewed
Related Terms
Model Distillation
AITraining a smaller model to replicate a larger model's capabilities efficiently.
LLM
AIAI model trained on massive text to understand and generate human language.
Token
AIFundamental text unit that LLMs process — roughly 3-4 characters.
Transformer
AINeural network architecture using self-attention — the foundation of modern AI.