# Day 2: User Journeys Deep Dive

**Date:** 2026-03-10
**Facilitator:** AI Advisory Council Facilitator
**Session Type:** User Journey Mapping — 8 Complete Personas
**Output:** 20+ GitHub Issues Created

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## Opening Statement

The Advisory Council spent Day 1 seeing our product as it is. Day 2 is about seeing it as our users experience it. We mapped 8 complete user journeys in extreme detail — from pre-awareness frustration to idealized success state. Every gap in these journeys becomes a GitHub issue. Every "aha!" moment becomes a design target.

Research conducted before this session:
- Developer CLI tool onboarding best practices 2026
- AI developer tools adoption patterns 2026
- Home GPU AI setup frustrations from community forums
- Enterprise AI procurement process 2026
- GPU passive income opportunity research (RTX 4090 at $600-$1400/month at 35-55% utilization)

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## Journey 1: Sarah — ML Engineer at a 50-Person Startup

**Hardware:** MacBook Pro M4 (development) + cloud credits (production)
**Use case:** Run Llama 3.3 70B locally for testing before cloud deployment

### Pre-Awareness State

Sarah is on her third ChatGPT Plus subscription this year. She spends $240/month on OpenAI API credits for her team of 5. They're building a document analysis pipeline and need to iterate fast. Every API call costs $0.01 and adds 2-second latency. She's tried Ollama but it's slow and crashes when two team members use it simultaneously. She's frustrated: "Why can't I just run this on the GPU I already have?"

She reads the vLLM documentation and spends an afternoon trying to install it on her MacBook. The installation fails (CUDA not found — because it's Apple Silicon). She posts on r/LocalLLaMA: "Is there something that just works for local inference on Mac?"

### Discovery Moment

Someone replies to her Reddit post: "Try LLMHosts. One command, works on Mac, and you can share it with your team." She clicks the link.

### First 30 Minutes

1. Lands on llmhosts.com. Reads the headline: "Your hardware. Real AI infrastructure. From anywhere." Scrolls to quickstart.
2. Runs `pip install llmhosts`. (Currently works. Good.)
3. Runs `llmhosts setup`. **Sees HIM recommendations: "For your M4 MacBook Pro (36GB unified memory), we recommend Qwen3-14B Q5_K_M (~14GB) or Llama 4 Scout Q4_K_M (~10GB)."** Approves Qwen3-14B.
4. Model downloads in the background. Progress bar shows "Downloading Qwen3-14B Q5_K_M (9.1GB)... 34%".
5. Opens a new terminal. Runs `llmhosts serve`. Sees "LLMHosts Core running on Apple M4 Metal. Qwen3-14B Q5_K_M loaded. API at http://localhost:4000."
6. Opens VS Code. Configures Continue.dev to point to http://localhost:4000. Code completions start working.
7. **Aha! moment #1**: First response from her own hardware. "Oh wow, that's fast."
8. Runs her document analysis pipeline against localhost:4000. It works. Identical API to OpenAI.
9. Checks the dashboard: "Real-time metrics: 18 tokens/sec, TTFT: 340ms, 0 queue."
10. **Aha! moment #2**: "This is faster than OpenAI for my use case."
11. Calls her teammate: "Hey, can you try http://sarah-mbp.local:4000 and see if it works for you?" It doesn't — no tunnel.
12. Runs `llmhosts tunnel start`. Gets a URL: `https://sarah-uuid.llmhosts.dev`. Shares with teammate.
13. **Aha! moment #3**: Teammate connects remotely. Both are using Sarah's M4 at the same time. No queue, no wait.

### First Week Experience

- Sarah installs LLMHosts on her work desktop (Linux, RTX 3090). Same `pip install llmhosts`. HIM recommends Llama 3.3 70B Q4_K_M.
- She creates a dashboard account, registers both devices. Sees fleet view.
- Team of 5 is now sharing two GPU nodes (M4 MacBook + RTX 3090) via two relay URLs.
- She runs `llmhosts doctor` and sees: "Both nodes healthy. Combined throughput: 31 tokens/sec."
- API costs: $0/week vs. $60/week previously.

### Week 4 Power User Behavior

- She routes Tier 1 router to local node (fast queries), Tier 2 router to cloud (complex queries), using LLMHosts' intelligent routing.
- She monitors the metrics dashboard from her phone.
- She's configured custom model temperature and system prompt via the API proxy.

### Critical Success Moments (Aha!)

1. **"It works on my Mac without CUDA"** — WGPU Metal backend working
2. **"My team can share it"** — relay tunnel + concurrent serving via PagedAttention
3. **"It's cheaper than OpenAI"** — cost tracking showing $0 local vs. $240/month cloud
4. **"The API is compatible"** — no code changes needed for existing OpenAI-format apps

### Churn Risks

- Relay tunnel is unreliable (flaky connections → team stops using it)
- Slow inference speeds vs. OpenAI (especially on CPU-only path)
- Model setup is confusing (which model? which quantization?)
- Not enough concurrent users supported (her team of 5 creates queue)

### Ideal State

Sarah's team runs all their AI workloads on their own hardware. Cloud spend drops to near zero. They have enterprise-grade reliability, team sharing, and cost transparency — all from a single `pip install`.

### Metrics Defining Success

- TTFT: <500ms for 8K context
- Concurrent users: 5 simultaneous, no queuing
- Setup time: <10 minutes from `pip install` to first response
- Cost savings: documented in dashboard

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## Journey 2: Tomas — Privacy-Conscious Enterprise Developer (GDPR)

**Hardware:** Enterprise workstation, Lenovo ThinkStation with RTX A6000 (48GB VRAM)
**Use case:** Process European customer support tickets with AI — GDPR compliance required

### Pre-Awareness State

Tomas is a senior backend engineer at a fintech company in Vienna. He's been evaluating every AI inference option for 6 months. The requirement is absolute: customer data must never leave their datacenter. OpenAI, Anthropic, Google — all rejected by their DPO. Azure OpenAI with EU data residency was considered but GDPR Art. 28 analysis was inconclusive. They need on-premises inference with zero-dependency on external APIs.

He's been running Ollama internally but it has no authentication, no audit logs, no GDPR-compliant data handling documentation. He can't pass a GDPR compliance audit with it.

### Discovery Moment

At a Hacker News thread on "GDPR-compliant LLM inference," someone links to LLMHosts' privacy documentation. He reads: "Zero data egress. On-premises serving. Cryptographically verified that no data leaves your hardware." He schedules a proof-of-concept.

### First 30 Minutes (Enterprise POC)

1. Installs on the ThinkStation (Ubuntu 22.04 + CUDA 12.8).
2. Runs `llmhosts setup`. HIM recommends Llama 3.3 70B Q8_0 (fits in 48GB A6000).
3. Configures: `LLMHOST_SECURITY_REQUIREAUTH=true`, `LLMHOST_PRIVACY_MODE=strict`, `LLMHOST_DATA_RESIDENCY=eu`.
4. Enables PII detection: `llmhosts config set pii_detection=true`.
5. Tests: sends a request with a customer email in the prompt. **LLMHosts blocks it**: "PII detected: email address. IslandRun privacy mode: request blocked. No data sent to external API."
6. **Aha! moment**: "This actively protects us. It's not just local, it's privacy-enforced."
7. Checks audit logs: every request logged with timestamp, user, model, PII flag, no payload stored.
8. Reads the privacy documentation. It references specific GDPR articles (Art. 5(1)(f) integrity and confidentiality, Art. 25 data protection by design).

### Critical Success Moments

1. **PII detection blocking external routing** — data sovereignty enforced by code, not policy
2. **Audit logs in GDPR-required format** — compliance documentation ready
3. **Zero-egress mode** — no network calls to external services during inference
4. **Data Processing Agreement template** — LLMHosts SaaS DPA available for download

### Churn Risks

- Lack of formal compliance documentation (SOC 2, ISO 27001 reports)
- No enterprise support SLA
- GDPR DPA not available from LLMHosts
- No audit log export to SIEM

### Ideal State

Tomas runs LLMHosts in a fully air-gapped configuration with formal GDPR compliance documentation, passed their company's internal security review, and processes 50,000 customer support tickets per week with zero cloud AI costs.

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## Journey 3: Priya — Home Lab Enthusiast with RTX 4090

**Hardware:** Custom gaming PC, ASUS ProArt RTX 4090 24GB, Ryzen 9 7950X, 64GB RAM
**Use case:** Generate passive income from idle GPU + personal AI assistant

### Pre-Awareness State

Priya built a $5,000 PC for game development and AI experiments. Her GPU sits idle 16 hours a day. She's heard about GPU rental income but Vast.ai makes her feel like a commodity host. She wants something where she controls the terms, knows exactly who's using her GPU, and can set her own pricing.

She's explored crypto GPU mining (too volatile, too noisy for the neighborhood), ran Stable Diffusion for a while (too much babysitting), and briefly tried setting up a Vast.ai node (confusing, feels like surrendering her machine).

### Discovery Moment

YouTube video: "I Make $800/Month Letting AI Researchers Use My RTX 4090 While I Sleep." She watches it. LLMHosts is mentioned. She clicks through.

### First 30 Minutes

1. Installs LLMHosts. (`pip install llmhosts` — 90 seconds.)
2. Runs `llmhosts setup`. HIM immediately recognizes RTX 4090. Recommends: "For marketplace hosting: Llama 3.3 70B Q4_K_M (best demand) or Llama 4 Scout Q4_K_M (emerging demand). Revenue estimate: $0.48-$1.20/hr at current demand."
3. **Aha! moment #1**: "It tells me how much I'll earn."
4. Creates LLMHosts account. Connects Stripe. Sets her GPU remit: "Allow use when GPU <40% utilized. Max 2 concurrent requests. Rate: $1.00/hr."
5. Enables marketplace: `llmhosts marketplace enable`.
6. **Aha! moment #2**: "My GPU is listed on the marketplace and people can use it."
7. First request comes in 4 minutes later. She sees it in the dashboard: "Request #1 from user@email.com. Model: Llama 3.3 70B Q4_K_M. Duration: 45s. Earnings: $0.012."
8. **Aha! moment #3**: "I just made money while watching YouTube."

### Week 4 Power User Behavior

- She's earned $240 that month.
- She monitors uptime dashboard from her phone to ensure good reputation score.
- She's configured automatic VRAM release when her own games need the GPU.
- She posts in the LLMHosts Discord: "RTX 4090 operator AMA."

### Critical Success Moments

1. **Revenue estimate at setup** — removes ambiguity about income potential
2. **GPU remit system** — user stays in control (her GPU, her rules)
3. **First earning** — instant gratification validates the income potential
4. **Reputation dashboard** — she can see why her reputation score matters

### Churn Risks

- Earnings lower than expected (marketplace liquidity is low)
- GPU remit conflicts with gaming (inference starts during game session)
- Payout delays or Stripe issues
- Reputation score drops despite good uptime (unfair scoring)

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## Journey 4: Eva — DevOps Engineer Managing 10 GPU Nodes

**Hardware:** 10 GPU workstations across 3 offices (mix of RTX 3090, 4090, and A100)
**Use case:** Unified AI inference fleet for her company's development teams

### Pre-Awareness State

Eva manages infrastructure for a 200-person software company. 10 high-end GPU workstations sit in three offices — purchased for AI projects but poorly utilized due to lack of consistent serving infrastructure. Each developer has their own Ollama setup. No centralization. No cost tracking. No fleet management. The CTO asks: "Are we getting ROI on these GPUs?"

### Discovery Moment

She's evaluating fleet management tools. A colleague sends her the LLMHosts docs page on fleet management. She's intrigued by the centralized dashboard and fleet view.

### First 30 Minutes

1. Installs LLMHosts on all 10 nodes via Ansible playbook in 20 minutes.
2. Creates organization account on LLMHosts dashboard.
3. **Aha! moment**: Fleet view shows all 10 nodes, their GPU models, VRAM, and health status. One click reveals which models are loaded on each.
4. Sets up routing policy: "Route 8B model requests to lowest-latency node. Route 70B model requests to A100 node."
5. Deploys unified relay endpoint: `https://company-ai.llmhosts.dev`. All 10 nodes are load-balanced behind one URL.
6. **Aha! moment #2**: "This replaces 10 separate Ollama endpoints with one API that routes intelligently."
7. Runs cost analysis: "Previously: $8,000/month in cloud AI credits. Now: $240/month (LLMHosts Pro plan + electricity). ROI: 33x."
8. Shows dashboard to CTO: "Here's our utilization, here's our savings, here's what each node is doing right now."

### Critical Success Moments

1. **One-line fleet install via Ansible** — no manual SSH per node
2. **Unified endpoint** — one URL, intelligent routing
3. **ROI dashboard** — proves value to management
4. **Fleet health monitoring** — proactive before users complain

### Churn Risks

- Fleet setup requires IT security approval (no SOC 2 report)
- Per-node license costs at scale (pricing concerns)
- Central relay is a SPOF (single point of failure) without redundancy
- Model synchronization across fleet is manual

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## Journey 5: Wei — University Researcher

**Hardware:** University HPC cluster (via quota) + personal RTX 3090
**Use case:** Long-context document analysis for PhD research (budget-constrained)

### Pre-Awareness State

Wei is a 3rd-year PhD student studying legal document analysis. He needs to process 10,000-token court transcripts through LLMs. His university HPC quota is 40 GPU-hours/week — not enough. OpenAI API costs are $200/month — too much for his stipend. He's been running Ollama on his personal RTX 3090 but the 24GB VRAM limits him to 32K context.

### Discovery Moment

Research group Slack channel: "Anyone found a way to run 128K context models locally?" A labmate mentions LLMHosts' phase-split serving that offloads KV cache to CPU RAM for longer contexts.

### Critical Success Moments

1. **128K context via CPU-offloaded KV cache** — his research use case becomes possible
2. **Free tier** — he doesn't need a paid plan for personal research use
3. **Batch processing API** — he can submit 1,000 documents overnight

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## Journey 6: Carlos — Mobile-First Developer

**Hardware:** M4 MacBook Pro (development), iPhone 15 (field testing)
**Use case:** Low-latency AI backend for his iOS productivity app

### Pre-Awareness State

Carlos is building an iOS app that uses AI for note summarization. He's using OpenAI API but latency (1-3s per call) degrades the UX. He needs sub-500ms responses. He's looked at on-device models (too small for quality) and edge inference (too complex). He wants something in between: a reliable backend on his own hardware.

### Critical Success Moments

1. **<500ms TTFT for 8B models** — meets his UX requirement
2. **OpenAI SDK compatibility** — zero code changes in his iOS app
3. **VS Code extension for LLMHosts** — development workflow integration

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## Journey 7: Aisha — Fine-Tuning Researcher

**Hardware:** 2x RTX 4090 (custom build)
**Use case:** Fine-tune LLMs on proprietary medical dataset

### Pre-Awareness State

Aisha is a ML researcher at a hospital system. She has proprietary patient outcome data (de-identified) and wants to fine-tune a base LLM for medical record summarization. She cannot use cloud services (HIPAA compliance). She has two RTX 4090s but no fine-tuning infrastructure.

### Discovery Moment

She reads the LLMHosts fine-tuning documentation (Wave 5 feature). The headline: "Fine-tune models on your own hardware. Data never leaves your GPU."

### Critical Success Moments

1. **LoRA fine-tuning via `llmhosts finetune start`** — one command to begin
2. **HIPAA-compliant local execution** — data never leaves her hardware
3. **Serving fine-tuned model immediately** — seamless transition from training to serving
4. **Multi-GPU training distribution** — 2x RTX 4090 for 2x training speed

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## Journey 8: David — Enterprise CTO

**Hardware:** Enterprise GPU fleet (managed by IT, mixed NVIDIA)
**Use case:** Evaluate LLMHosts for 500-person organization AI infrastructure

### Pre-Awareness State

David is CTO of a 500-person fintech. He's spent $1.2M in cloud AI in 2025. His board wants him to reduce costs. He's evaluated every enterprise AI infrastructure option. On-premises solutions require custom DevOps. Cloud solutions lock him into a vendor. He needs something that: passes IT security review, has an enterprise SLA, integrates with Okta, and provides audit logs for compliance.

### Discovery Moment

His Chief Architect forwards him a benchmark: "LLMHosts Core vs. vLLM — 15% faster on RTX 4090 with better memory efficiency." He requests a demo.

### Critical Success Moments

1. **SOC 2 Type II report** — passes IT security review
2. **Okta SSO integration** — IT approval granted
3. **GDPR DPA available** — legal team signs off
4. **ROI dashboard** — board presentation: "$800K annual savings vs. cloud AI"
5. **Enterprise SLA** — 99.9% uptime commitment

### Churn Risks

- No formal enterprise support
- No SOC 2 report (current state)
- No SSO (current state)
- No GDPR DPA template
- No pilot program structure

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## Journey Gap Analysis Summary

| Journey | Top 3 Gaps Today |
|---------|-----------------|
| Sarah | macOS Metal not optimized; no team concurrent serving until PagedAttention ships; no cost savings dashboard |
| Tomas | No GDPR DPA; no formal compliance docs; no audit log export |
| Priya | No revenue projections at setup; marketplace liquidity uncertain; no earnings history chart |
| Eva | No fleet install automation; no unified endpoint routing; no ROI calculator |
| Wei | No 128K context via CPU KV offload; no batch processing API; no free tier clearly defined |
| Carlos | TTFT not yet <500ms for 8B on Mac; no VS Code extension; no iOS SDK example |
| Aisha | Fine-tuning not available (Wave 5); no HIPAA compliance documentation |
| David | No SOC 2; no SSO; no enterprise pricing; no pilot program |

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## Issues Created Today

Day 2 produces 20+ GitHub issues covering all journey gaps above.
