Practical local LLM infrastructure design

Build a local LLM
setup that fits your work

Oxtane turns your local workload and data needs into a practical LLM infrastructure plan. We size the machine — GPU, VRAM, RAM, storage, and power — then outline local model serving, security, monitoring, and handover. If local is not suitable, we flag that early; cloud or hybrid alternatives are only a fallback note, not a full architecture deliverable.

45-minute local LLM call$500 flat fee~2-hour deliverableGPU / VRAM / RAM / storage / powerPlan before you build
Core offer
Local LLM Infrastructure Audit
Practical starting point
Price
$500
Flat fee per audit
Delivery time
~2 hours
plus a 45-minute discovery call
Outcome
Local setup plan
Workload constraints, machine route, and a small implementation plan
This is a focused local LLM planning engagement, not a promise that local is always suitable. We test the workload, data path, latency, and operating constraints, then document the machine and serving plan. If those constraints rule local out, we note a cloud or hybrid fallback without designing that alternative architecture.
Offer

Choose a local LLM setup before buying hardware.

Start with a $500 audit that connects the local workload and data path to a buildable machine and serving plan. If local is unsuitable, the report says so and records a brief fallback note.

Local workload and data path
We map the target local use case, data location and rough volume, privacy constraints, expected users, latency, and current environment.
Machine and GPU sizing
We size GPU, VRAM, RAM, storage, power, thermals, and uptime around the workload and the machine you have or may buy.
Local serving and operations
You receive a practical model/runtime, secure access, storage, monitoring, handover, and first implementation plan. Cloud or hybrid appears only as a fallback note if needed.
Process

How the Local LLM Infrastructure Audit works

A 45-minute call and approximately two-hour deliverable that turns a real workload into a local setup plan.

A short intake covers four local-design prompts: target workload/use case, current machine/GPU/environment, data location/types/rough volume plus privacy/access/retention constraints, and expected users/concurrency/latency/outcome. Identity and contact fields are separate; share what you know. The audit call itself uses 12 core questions, with up to 3 extra follow-ups only when needed.

01
Understand the local workload and data
We map the target local use case, data location and rough volume, privacy/access/retention constraints, expected users, concurrency, latency, and current environment.
02
Size the local machine
We turn the workload into GPU, VRAM, RAM, storage, power, thermals, and uptime requirements for an existing machine or a practical BOM.
03
Plan local serving and operations
We outline the model/runtime, secure remote access, storage, monitoring, handover, TCO assumptions, and a small implementation plan. If local is unsuitable, we add only a brief fallback note.
Deliverables

What you walk away with

A practical local LLM setup decision and a clear first implementation step, not a large build commitment.

✓Local workload and data constraints, including location, rough volume, privacy/access/retention, users, latency, and the current environment
✓Local LLM machine recommendation with explicit sizing assumptions, costs, trade-offs, and operating constraints
✓Hardware BOM or existing-machine plan covering GPU, VRAM, RAM, storage, and power
✓Model/runtime, secure remote access, storage, monitoring, and handover plan
✓TCO assumptions, a small implementation plan, and a brief cloud/hybrid fallback note only if local is unsuitable
Good fit

Who this is for

Best for teams that need a grounded local LLM decision before they buy hardware or expose sensitive data. It also makes clear when local is not a fit.

•Solo founders and small teams exploring local LLM inference for a real workload
•Operators with an existing machine or GPU who need a safe local serving and access plan
•Data-sensitive teams that need a local data path and machine plan before implementation
•Teams that want explicit trade-offs instead of guaranteed savings or performance claims
Audit sample

What the Local LLM Infrastructure Audit report actually looks like

The output is a decision-ready local LLM memo: constraints, machine sizing, runtime plan, operating safeguards, and a small next step.

Memo structure

A decision-ready local LLM plan

The report turns workload evidence into a practical local setup route.

•1. Executive Summary — The local workload reviewed, data boundary, machine recommendation, key trade-offs, and the first validation step.
•2. Local Workload and Data Constraints — Users, concurrency, latency, data location and rough volume, privacy/access/retention, current hardware, and operational ownership.
•3. Local Machine and Runtime Plan — A hardware BOM or existing-machine plan for GPU, VRAM, RAM, storage, power, model runtime, and upgrade boundaries.
•4. Local Serving and Security Plan — Model serving, secure remote access, storage, monitoring, handover, and explicit operating assumptions.
•5. Implementation and Fallback Note — A small implementation plan with the next validation step; if local is unsuitable, a brief cloud or hybrid fallback note without full alternative architecture.
Example workloads

From workload to a local operating route

Recommendations keep data boundaries, machine trade-offs, and human approval visible.

•Private research assistant — Founder / analyst: Sensitive notes need local summarization without an undefined data path → Define the local data boundary, machine fit, serving runtime, and human review
•Internal document search — Operations team: A shared machine exists, but GPU headroom, remote access, and retention are unclear → Existing-machine assessment + serving runtime + secure access and monitoring plan
•Team knowledge workflow — Ops / leadership: Private records need a deliberate local model path and access boundary → Local serving plan with client-controlled records, model routing, and approval gates

Final recommendations are ranked by local workload fit, implementation effort, operating risk, and total-cost assumptions. If local is not suitable, the report says so and adds only a brief fallback note.

Typical use cases

Where the audit tends to help first

Common entry points for a grounded local LLM decision.

•Local LLM inference for research and internal operations
•Existing GPU or workstation assessment before a model-serving build
•Local data-boundary, sizing, and TCO planning
•Secure remote access, storage, monitoring, and handover planning
•Workflow and knowledge systems that depend on a deliberate local model route
Commercial logic

Simple local LLM service economics

The $500 Local LLM Infrastructure Audit is a focused assessment, not a build. After a 45-minute call, you receive an approximately two-hour deliverable that makes the local setup decision concrete.

That keeps the entry point light and gives implementation a measured starting point: validate local fit, then build only what the evidence supports.

Implementation capabilities

What Oxtane can design and build after the audit

Infrastructure comes first. Where the audit supports implementation, we can extend the plan into model serving, secure operations, and the workflow systems that depend on it.

GPU & machine architecture
Planning GPU selection, VRAM headroom, RAM, storage, power, thermals, and uptime around the workload and the constraints of the existing machine or proposed BOM.
Local inference & model serving
Designing a model/runtime path for local inference, with explicit scope for supported models, capacity, upgrades, fallback options, and operator handover.
Secure remote access, storage & monitoring
Designing access boundaries, encrypted storage, backup or retention choices, health checks, logs, and monitoring so a private system can be operated deliberately.
Workflow and database infrastructure — separately scoped after the audit
Separately scoped implementation work after the audit: PostgreSQL/Supabase schema and data modeling, migrations/ingestion/idempotency, RLS/access, indexes/performance, backup/retention/observability, resolver/API contracts, and operational handover. pgvector or retrieval/RAG is considered where appropriate. This database implementation work is not included in the $500 audit; workflow and knowledge systems remain separately scoped as needed.
AI routing & cost controls — QuorumRouter
Designing capability- and budget-aware model routing with structured validation, safe fallbacks, circuit breakers, and failure telemetry — without claiming guaranteed token savings.
Safe agent operations — SafeLoop
Adding checkpoints, tamper-evident artifacts, approval gates, and covered local-file recovery. External actions require separate compensation or operator handling.
Role-separated agents & evaluation
Retaining planner, specialist, reviewer, red-team, and evaluation workflows with explicit context, supported actions, deterministic tests, and evidence-based iteration.
Research & operations automation
Turning recurring research, reporting, synthesis, approvals, and internal handoffs into consistent human-in-the-loop workflows after the infrastructure is ready.
Operator experience under sustained GPU load
Prior mining/GPU experience includes building and operating GPU systems under sustained load, with practical attention to power, thermals, hardware selection, and uptime.
Daily AI Alpha

Research radar and operator-grade AI intel

This is the layer I want to keep expanding: curated AI signals, applied research summaries, and practical notes on what matters for real operators.

What shows up here
Memory signal
MemMachine posts a strong LoCoMo result for personalized agent memory
A new memory-system paper reports 0.9169 on LoCoMo with gpt-4.1-mini and positions itself above several open memory-framework baselines.
Research paper · archiveRead more
Evaluation signal
Beyond Task Completion argues agentic systems need broader evaluation
The paper frames non-determinism, tool choice, and memory retrieval variability as first-class evaluation problems for agent systems.
Research paper · archiveRead more
Architecture signal
Graph-native cognitive memory explores formal belief revision for agents
Kumiho proposes a graph-native memory architecture that tries to unify versioning, retrieval, consolidation, and belief updates more formally.
Research paper · archiveRead more
AI research paper spotlight
Agent reliability and evaluation
Research worth watching on evaluation loops, verifier design, failure modes, and how to make agents useful without making them brittle.
Memory, retrieval, and context systems
Paper summaries focused on persistent memory, retrieval quality, and how long-horizon AI systems keep state without drifting.
Applied workflow automation
Less theory, more implementation: papers and technical notes that can actually influence internal ops, content, and research workflows.
Auto-refreshes from recent research signals (agent / memory / evaluation), with a curated fallback if the live feed fails.
Open intel archive →
Contact

Book a Local LLM Infrastructure Audit with a short intake

Share the local workload, current machine/GPU/environment, data location/types/rough volume and privacy constraints, and expected users/concurrency/latency/outcome so the 45-minute call can start with useful facts.

Flat fee: $500 per audit. Deliverables include local workload and data constraints, a local LLM machine plan, model/runtime and operating safeguards, TCO trade-offs, and a small implementation plan. Cloud or hybrid is only a brief fallback note if local is unsuitable.