What is Llama (Meta)?
Meta's open-weight LLM family — Llama 3.x is the dominant open-weight base for enterprise on-prem deployments through 2025-2026.
Also known as
Llama (Meta) — explained.
Llama is Meta's open-weight LLM family — released under a permissive licence that allows commercial use up to defined operator-scale limits. The Llama 3.x series (3.0 in April 2024, 3.1 in July 2024, 3.2 in September 2024, 3.3 in December 2024) is the dominant open-weight base for enterprise on-prem deployments through 2025-2026. The flagship 70B and 405B parameter versions are competitive with hosted-API frontier models on most enterprise tasks (instruction-following, code, reasoning, multilingual). The smaller 8B variant is widely used for cost-sensitive deployments and edge inference. Llama models support context windows up to 128K, instruction tuning, and tool-calling. Meta also provides safety-tuned variants (Llama Guard, Prompt Guard). For Zeour on-prem AI deployments, Llama 3.x is a default starting point — well-supported by vLLM, Ollama, TGI, and TensorRT-LLM; widely documented; and large enough community to absorb most regressions quickly.
Zeour solutions that operate on this layer.
Verticals where llama (meta) is operationally critical.
Blog posts that go deeper on llama (meta).
Adjacent definitions to read next.
Open-Weight LLM
AI & ModelsA large language model whose trained parameters (weights) are published openly — runnable on the operator's own hardware without API dependency.
vLLM
AI & ModelsA high-throughput LLM inference server using paged-attention memory management — the typical production runtime for self-hosted open-weight models.
On-Premises AI
AI & ModelsOpen-weight large language models running on the operator's own hardware — no prompt, completion, or embedding ever leaves the perimeter.
Fine-Tuning
AI & ModelsAdapting a pre-trained LLM to your domain or task by continuing its training on a small, high-quality dataset — typically via LoRA or full SFT.
Arabic Language Model
AI & ModelsAn open-weight or fine-tuned LLM that handles Modern Standard Arabic and major dialects with appropriate tokenisation efficiency and right-to-left rendering at the application layer.
Context Window
AI & ModelsThe maximum amount of text an LLM can process in a single request, measured in tokens — caps how much document context can be fed for RAG and long-form analysis.
Embeddings
AI & ModelsNumerical vector representations of text (or images, or audio) where semantically similar inputs land in similar regions of vector space — the substrate of semantic search and RAG.
Large Language Model
AI & ModelsA neural network trained on internet-scale text that produces fluent generative output and powers most of what people call "AI" in 2026 — including on-premises sovereign deployments.
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A 30-minute scoping call to walk your operational profile against where llama (meta) actually sits in your stack, then a fixed-fee Discovery price by the end of the call.