[{"data":1,"prerenderedAt":-1},["ShallowReactive",2],{"glossary-parameter::en":3,"gloss-cluster-parameter::en":23,"gloss-next-parameter::en":9},{"slug":4,"category":5,"name":6,"definition":7,"meta_desc":8,"faq":9,"schema_markup":9,"related":10},"parameter","core-ai","Parameter","A parameter is one of the individual numeric weights inside a neural network that gets adjusted during training and collectively encodes everything the model has \"learned\" — there is no separate database of facts inside an LLM; all of its knowledge and capability is distributed across billions of these numbers, each representing the strength of a connection between artificial neurons. Parameter count has become the most commonly cited (if imperfect) shorthand for model scale and rough capability: models are described as \"7B,\" \"70B,\" or \"trillion-parameter\" as a quick signal of size, with the general (though not absolute) trend that more parameters, trained on more high-quality data, tends to produce more capable models — a relationship formalized in AI research as \"scaling laws.\" This matters for SaaS builders in very practical ways: parameter count roughly correlates with inference cost, memory footprint, and latency (a 70B-parameter model needs more GPU memory and compute per token generated than a 7B model, and is typically slower and more expensive to run), which is why model selection is fundamentally a trade-off between capability and cost\u002Fspeed rather than \"always pick the biggest model.\" A concrete example: Claude's model family spans this trade-off explicitly — Claude Opus (the largest, most capable, most expensive tier) suits complex reasoning and high-stakes tasks; Claude Sonnet (a balanced mid-tier) suits most production workloads; Claude Haiku (the smallest, fastest, cheapest tier) suits high-volume, latency-sensitive tasks like real-time chat classification or simple extraction, where the extra capability of a larger model would be wasted expense. Builders architecting a multi-feature AI product often route different tasks to different parameter-scale models within the same family — a support ticket triage feature might use a Haiku-class model given its simplicity and volume, while a complex contract-analysis feature might justify an Opus-class model given the stakes of getting it right — rather than defaulting every call to the largest available model. Note that raw parameter count alone doesn't fully determine quality — training data quality, architecture refinements, and post-training alignment (RLHF) all matter, which is why a newer, well-trained smaller model can outperform an older, larger one. It's worth being skeptical of parameter count as a marketing number in isolation: a well-trained, well-aligned smaller model frequently outperforms a larger, poorly-trained or poorly-aligned one on real tasks, and post-training techniques (RLHF, fine-tuning, RAG grounding) often move the needle on practical output quality more than raw parameter scale alone. When evaluating models for a specific SaaS feature, builders get a far more useful signal from benchmarking actual output quality on representative production examples than from comparing headline parameter counts between providers.","Parameters are the learned numeric weights inside a neural network — parameter count is a rough (imperfect) proxy for model scale and capability.",null,[11,14,17,20],{"slug":12,"name":13},"distillation","Knowledge Distillation",{"slug":15,"name":16},"foundation-model","Foundation Model",{"slug":18,"name":19},"llm","Large Language Model (LLM)",{"slug":21,"name":22},"quantization","Quantization",[24,28,32,36,39,42,45,48,51,54,57,60],{"slug":25,"category":5,"name":26,"updated_at":27},"agentic","Agentic AI","2026-08-24T02:46:36+00:00",{"slug":29,"category":5,"name":30,"updated_at":31},"alignment-tax","Alignment Tax","2026-08-24T02:46:37+00:00",{"slug":33,"category":5,"name":34,"updated_at":35},"artificial-intelligence","Artificial Intelligence (AI)","2026-08-24T02:46:38+00:00",{"slug":37,"category":5,"name":38,"updated_at":27},"attention","Attention",{"slug":40,"category":5,"name":41,"updated_at":35},"beam-search","Beam Search",{"slug":43,"category":5,"name":44,"updated_at":31},"benchmark-contamination","Benchmark Contamination",{"slug":46,"category":5,"name":47,"updated_at":31},"catastrophic-forgetting","Catastrophic Forgetting",{"slug":49,"category":5,"name":50,"updated_at":35},"computer-vision","Computer Vision",{"slug":52,"category":5,"name":53,"updated_at":31},"constitutional-ai","Constitutional AI",{"slug":55,"category":5,"name":56,"updated_at":27},"context-window","Context Window",{"slug":58,"category":5,"name":59,"updated_at":35},"deep-learning","Deep Learning",{"slug":61,"category":5,"name":62,"updated_at":27},"diffusion-model","Diffusion Model"]