Small local AI, large controlled memory.

Do not force the largest possible model onto limited hardware.

Running artificial intelligence locally creates a real engineering trade-off. Larger models often provide stronger general reasoning, coding assistance and flexibility, but they need substantially more memory, processing power, storage, cooling and electricity. Smaller models are easier to run privately on ordinary computers, but their internal capability ceiling is lower.

A well-designed Vault offers a practical middle path. It does not alter the model's trained parameters. Instead, it reduces how much the model must remember or reconstruct by itself. The complete assistant becomes a coordinated system made from a model, persistent memory, retrieval, deterministic tools, permissions and verification.

AEGIS is optional. Sentinel Linux does not automatically install an AI model or runtime during operating-system conversion or installation. The user deliberately chooses and configures a supported local runtime and model later.

The model should not be treated as the database.

The language model is temporary working intelligence. The Vault is persistent local knowledge. Keeping these responsibilities separate improves maintainability and makes important information easier to inspect, correct, back up and protect.

Bring only the most relevant approved records into working context.

1. Understand the request

   Identify the subject, intent, entities, timeframe and type of evidence required.

2. Search the Vault

   Search indexed records using semantic and keyword retrieval, preferring canonical and directly relevant material.

3. Rank and filter

   Remove duplicates, obsolete drafts, withdrawn baselines and records the current user is not authorised to access.

4. Build controlled context

   Supply the smallest useful set of records. Too much context can make a small model less reliable.

5. Generate the answer

   The model interprets the retrieved evidence and responds within its actual reasoning limits.

6. Verify important claims

   Check versions, package manifests, checksums, source files, command output and accepted policy records where appropriate.

7. Record an approved update

   Store new information only with a clear status such as draft, confirmed, accepted, superseded, withdrawn or archived.

A smaller model can become highly effective inside a controlled domain.

Memory improves usefulness, not the model's underlying intelligence ceiling.

Local context can matter more than generic size for familiar work.

A strong 7B or 8B model with excellent retrieval may feel comparable to a 14B, 32B or occasionally larger model when answering questions about the user's own environment. It will not consistently match that larger model across broad knowledge, unfamiliar programming or novel reasoning.

Model weights are only one part of the system memory requirement.

Actual requirements depend on parameter count, numerical precision, quantisation, architecture, context length, KV cache, batch size, concurrency, vision or audio components, runtime overhead and how many layers are placed on the GPU.

Additional memory is required for the KV cache, runtime buffers, prompt processing, output generation, the desktop, retrieval databases, embeddings and other applications. A system should not be planned so that the model consumes every available gigabyte.

Capacity, speed and cost change depending on where the model runs.

System RAM only

CPU and RAM inference is the most accessible configuration. It avoids a dedicated GPU and makes large amounts of memory comparatively affordable, but generation is slower and heavily dependent on memory bandwidth.

Primarily in VRAM

Full GPU offload is usually the fastest interactive configuration. It improves prompt processing and token generation but makes memory capacity more expensive and still requires adequate host RAM.

Combined RAM and VRAM

Hybrid inference divides model layers between GPU memory and system memory. It is a practical way to use a model that is larger than the GPU, but it is slower than complete GPU offload and does not combine RAM and VRAM into one perfectly efficient pool.

Good retrieval is both more accurate and more memory-efficient.

The context window contains system instructions, the user's request, retrieved Vault records, conversation history, tool results and the generated answer. The runtime also maintains a KV cache, whose memory use generally grows with context length and concurrent sessions.

A model that fits at 4,000 tokens may exceed available memory at 32,000, 64,000 or 128,000 tokens. The Vault should therefore retrieve a small amount of highly relevant information instead of inserting the entire knowledge store into every prompt.

For comfortable 70B operation on a unified-memory system, 96 to 128 GB is a safer planning target than trying to use a 64 GB machine at its absolute limit.

Choose the best complete assistant, not merely the largest model that loads.

Small models benefit from compact, authoritative and clearly labelled records.

Project: Sentinel Linux ISO
Release: Sentinel Linux 1.0 AMBROSO
Status: accepted baseline
Canonical record: current ISO build phase and path
Withdrawn record: previous contaminated baseline
Pending requirement: next audited correction
Last verified: YYYY-MM-DD

Run the most capable model the computer can support comfortably.

For many users, a modern 7B to 14B model with 4-bit quantisation, a structured Memory Vault, semantic and keyword search, canonical records, permission controls, source tracking, document sanitisation and sufficient unused memory will produce a better complete system than a much larger model that leaves no room for the desktop or supporting services.

The result is not an artificial intelligence that knows everything. It is a local assistant that can reliably retrieve the information that matters to its user while keeping that knowledge private, maintainable and under user control.

This web guide is adapted from Small Local AI, Large Memory: How a Memory Vault Can Make Efficient Models Far More Capable. Hardware figures are planning estimates and vary by model family, quantisation, runtime, context length and workload.