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AI Memory
Stored conversation state, preferences and task records, distinguished from context windows and model training, with examples of freshness and access boundaries.
Context Window
The amount of tokenized input and generated text a model can consider within one inference context.
Fine-tuning
Additional training that adapts a pre-trained model to a narrower task, domain, or behavior.
GPU
A highly parallel processor widely used to train and run modern machine-learning models.
Inference
The process of running a trained model on new input to produce predictions, embeddings, or generated output.
KV Cache
A cache of attention keys and values used to avoid recomputing earlier context during autoregressive generation.
Knowledge Distillation
A training method in which a smaller student model learns from the outputs or representations of a larger teacher model.
Latency and Throughput
Two core serving metrics describing response delay and the amount of work a system completes over time.
Mistral AI
An AI company providing models and inference services, with a guide to model-specific licenses, hosted versus self-operated deployment and task-level comparison.
Mixture of Experts (MoE)
A model architecture that routes each input to a subset of specialized parameter blocks called experts.
Quantization
A technique that represents model weights or activations with lower numerical precision to reduce resource use.
Red Teaming
Adversarial testing intended to discover failure modes, unsafe behavior, or exploitable weaknesses in an AI system.
Safety Evaluation
Testing designed to measure risks, harmful behaviors, and the effectiveness of safeguards in an AI system.
Voice AI
Speech recognition, synthesis and conversation systems, including full-duplex interaction, voice replication and evaluation of actual task outcomes.