Building a Truly Portable AI System: A Practical Guide to Local LLMs

Extensive testing found that true portable local AI is currently a myth, requiring a 2-3 minute installer-based setup. Jan is the clear winning UI, providing 7x faster performance (56 tok/s) than alternatives. The recommended, professional-grade combination is Jan and Llama 3.2 3B, which offers near-instant, private, and cost-effective AI for business use.

by Avi Kumar

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Why This Matters for Business

For the last year, one question keeps coming up in my conversations with business leaders.

“Can we run AI without sending our data to OpenAI or the cloud?”

The answer is yes, but like everything in technology, the devil is in the details.

This isn’t theoretical. I recently attempted to build a completely portable AI system that runs from an external drive, no installation, no internet, no cloud APIs. Here’s what I learned testing four different UIs and four different models with real business use cases, including the surprising conclusion that true portability is harder than expected.

The Challenge: Business-Grade AI That Actually Works Offline

The requirements were specific:

Must run without internet connection
Must work on typical business laptops (not just gaming rigs)
Must produce professional-quality output
Must be simple enough for non-technical users
Must fit on portable storage for demos and distribution

The reality: Most “local AI” tutorials gloss over critical details like GPU requirements, actual performance on business hardware, and quality differences between models. After extensive testing, I discovered that the choice of UI application matters just as much as the model itself—with a 7x speed difference between the fastest and slowest options. I also discovered that true portability (running directly from a USB drive without installation) is not reliably achievable with current tools.

The Testing Environment

Hardware:

Laptop: Lenovo ThinkPad P14s Gen 5 (Model 21G2002DUS)
Processor: Intel Core Ultra 7 155H (16 cores)
GPU: NVIDIA RTX 500 Ada Generation (4GB GDDR6 VRAM)
RAM: 96GB DDR5
External Storage: 2TB USB 3.1 SSD (exFAT formatted)

This represents a high-end business workstation. Note: The 96GB RAM is overkill for local LLMs—8-16GB is sufficient for the models I tested. The 4GB VRAM is the critical constraint for GPU acceleration.

Software Stack:

GPT4All v3.10.0 – Open source desktop application
Jan v0.7.5 – Modern, fast, open source alternative
Ollama – CLI/API-focused tool for developers
LM Studio – Feature-rich but complex setup
Models tested: 4 different sizes (1GB to 4.7GB)
Storage: All models on external SSD for portability testing

The Big Discovery: True Portability Is a Myth (For Now)

Before diving into models, here’s the most important finding from my testing:

None of the UIs Are Truly Portable

I tested all four major local LLM applications with one goal: run AI directly from a USB flash drive without any installation. The results were disappointing:

UI Application	Runs from USB?	Issue
GPT4All	❌ No	Requires config changes, drive letter dependencies
Jan	❌ No	Stores absolute paths, crashes on drive letter change
Ollama	❌ No	Runs as system service
LM Studio	❌ No	Complex setup, path dependencies

The reality: All tested applications store configuration paths as absolute values (e.g., D:\AI\Models). When you plug the USB drive into a different computer that assigns a different drive letter (E:, F:, etc.), the applications either crash or can’t find their models.

Many of these limitations only become obvious once you understand how hardware choices quietly determine whether local AI feels smooth or frustrating in practice.

The Solution: Installer-Based Distribution

Since true portability isn’t achievable, the best approach for USB distribution is:

Include the installer on the USB drive
Include the model files on the USB drive
Provide simple setup instructions (install app, point to USB models folder)

This takes 2-3 minutes instead of “plug and play,” but it actually works reliably.

The Clear Winner: Jan

Given that installation is required regardless of which UI you choose, Jan is the clear winner because of its massive speed advantage:

Speed Comparison (Same Model: Llama 3.2 3B)

Jan is 7x faster than GPT4All with the exact same model file. Since both require installation anyway, there’s no reason to choose the slower option.

Why Jan Wins: The Complete Picture

Speed: 7x Faster

Jan: 56 tokens/second
GPT4All: 7-8 tokens/second
Same model, same hardware

Real-world impact:

Task	Jan	GPT4All
LinkedIn post (150 words)	~3 seconds	~20 seconds
Business email (100 words)	~2 seconds	~13 seconds
Code function	~5 seconds	~35 seconds

Modern UI

Clean, polished interface
Dark mode support
Conversation history
Easy model switching

Open Source (AGPLv3)

Fully customizable
No vendor lock-in
Active development community
Can be forked and white-labeled

Built-in API Server

Local REST API for app development
No need for separate Ollama installation
Same speed advantage applies to API calls

Customizable

Change welcome message
Change assistant name
Change icons (emoji-based)
Full source code available for deeper customization

Jan's Only Weakness: First-Try Quality

In my testing, Jan occasionally made minor terminology errors on first generation:

Example error: “Language Model Learning” instead of “Large Language Models”

However: Asking for a revision produced excellent output. And here’s the key insight:

Even with 2-3 iterations, Jan is faster than GPT4All’s single generation:

Approach	Time	Quality
Jan (3 iterations)	9 seconds	⭐⭐⭐⭐⭐
GPT4All (1 iteration)	20 seconds	⭐⭐⭐⭐⭐

Jan wins even in worst-case scenarios.

UI Deep Dive: Why Others Fall Short

Initial assumption: GPT4All would be the portable champion.

Reality: GPT4All also requires configuration changes and has drive letter dependencies. Since it’s not actually more portable than Jan, its 7x slower speed makes it the wrong choice.

Speed: 7-8 tokens/second (7x slower than Jan)

Verdict: No longer recommended. Jan is faster and equally (not) portable.

Ollama: Developer Tool Only

Best for: API development, scripting, backend integration

Strengths:

REST API at http://localhost:11434
Easy integration with applications
Modelfile system for custom configurations

Weaknesses:

CLI only – no user interface
Runs as Windows service
Same speed as GPT4All (6-7 tok/s)

Speed: 6-7 tokens/second

Verdict: Use only if you need a separate API server. Jan’s built-in API is faster.

LM Studio: Beautiful but No Advantage

Strengths:

Most polished, beautiful interface
Built-in model browser and downloader

Weaknesses:

Requires specific nested folder structure
Complex setup
Same speed as GPT4All (6-7 tok/s)
Not portable

Speed: 6-7 tokens/second

Verdict: No compelling reason to choose over Jan.

Testing Methodology: Real Business Use Cases

I didn’t test with toy examples. These are actual queries businesses need answered:

Test Query 1: Content Creation

				
					Write a LinkedIn post for founders about why running local LLMs 
matters for business. Make it practical, not hype-focused. 
Keep it under 200 words.

Why this matters: Content creation is one of the most common AI use cases for businesses.

Test Query 2: Technical Implementation

				
					Write a Python function that reads a CSV file and calculates 
the average of a specific column. Include error handling and 
comments explaining each step.

Why this matters: Tests the model’s ability to handle technical tasks with practical business applications.

Test Query 3: Strategic Business Advice

				
					I'm a CEO of a 20-person software company. We're considering 
whether to build AI features in-house or use OpenAI's API. 
What factors should guide this decision?

Why this matters: Tests reasoning, business context understanding, and ability to provide nuanced advice.

The Models: Size vs Performance Trade-offs

Model 1: DeepSeek-R1-Distill-Qwen-1.5B (~1GB)

Specifications:

Size: 1,043,758 KB (~1GB)
Parameters: 1.5 billion
License: MIT (no commercial restrictions)
Quantization: Q4_0

Performance (Jan):

Speed: ~28 tokens/second
Load time: ~5 seconds
RAM required: 3GB minimum

Unique Feature: Shows reasoning process during generation (collapsed by default in final output)

Quality Ratings:

LinkedIn Post: ⭐⭐⭐ (3/5) – Verbose, shows meta-commentary
Code: ⭐⭐⭐ (3/5) – Functional but buried in reasoning
Business Advice: ⭐⭐⭐⭐ (4/5) – Good thinking, verbose presentation

Best for: Ultra-lightweight deployments, educational contexts

Not ideal for: Professional content requiring polish

Model 2: Llama 3.2 3B (~1.9GB) ⭐ WINNER

Specifications:

Size: 1,876,865 KB (~1.9GB)
Parameters: 3 billion
Developer: Meta
License: Meta Llama 3.2
Community License
Quantization: Q4_0

Performance (Jan):

Speed: 56 tokens/second ⚡
Load time: ~15 seconds
RAM required: 4GB minimum

Quality Ratings:

LinkedIn Post: ⭐⭐⭐⭐⭐ (5/5) – Professional, ready to publish
Code: ⭐⭐⭐⭐⭐ (5/5) – Clean, production-ready
Business Advice: ⭐⭐⭐⭐⭐ (5/5) – Nuanced, comprehensive

Sample Output:

				
					As founders, we're constantly seeking ways to stay ahead of the curve 
and drive growth. One often-overlooked area is local Large Language 
Models (LLMs)...

Running a local LLM can help you:
- Improve customer service
- Enhance marketing efforts
- Boost efficiency

Professional, well-structured, and business-appropriate.

Best for: Everything – professional content, code, strategic analysis

The verdict: This is the sweet spot for business applications.

Model 3: Phi-3 Mini (~2.2GB)

Specifications:

Size: 2,125,178 KB (~2.2GB)
Parameters: 4 billion
Developer: Microsoft
License: MIT (no restrictions)
Quantization: Q4_0

Performance (Jan):

Speed: ~27 tokens/second
Load time: ~10 seconds

Quality Ratings:

LinkedIn Post: ⭐⭐ (2/5) – Too casual, emoji-heavy
Code: ⭐⭐⭐⭐⭐ (5/5) – Excellent technical implementation
Business Advice: ⭐⭐⭐ (3/5) – Surface-level

Best for: Coding tasks only

Not ideal for: Professional business writing

Model 4: Llama 3.1 8B 128k (~4.7GB)

Specifications:

Size: 4,551,965 KB (~4.7GB)
Parameters: 8 billion
Context window: 128k tokens
Quantization: Q4_0

Performance:

Speed: ~7 tokens/second (CPU only – GPU VRAM exceeded)
Critical finding: Did NOT fit in 4GB VRAM

The Reality Check: This model attempted to load on GPU but exceeded VRAM capacity, falling back to CPU-only mode. Quality matched the 3B model, but with no speed advantage.

Best for: Workstations with 8GB+ VRAM only

Not practical for: Typical business laptops

Performance Summary

Speed by Model (Jan)

Model	Size	Speed	GPU Status
DeepSeek R1 1.5B	1GB	28 tok/s	✅ GPU
Phi-3 Mini	2.2GB	27 tok/s	✅ GPU
Llama 3.2 3B	1.9GB	56 tok/s	✅ GPU
Llama 3.1 8B	4.7GB	7 tok/s	❌ CPU fallback

Quality by Model

Model	Content	Code	Strategy	Overall
Llama 3.2 3B	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	Best
Llama 3.1 8B	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐⭐⭐	Too large
DeepSeek R1	⭐⭐⭐	⭐⭐⭐	⭐⭐⭐⭐	Verbose
Phi-3 Mini	⭐⭐	⭐⭐⭐⭐⭐	⭐⭐⭐	Code only

The Recommended Setup

For USB Distribution

What to include on the USB drive:

				
					USB-Drive/
├── Jan-Installer/
│   └── Jan-Setup-0.7.5.exe
├── Models/
│   └── meta-llama/
│       └── Llama-3.2-3B-Instruct/
│           └── Llama-3.2-3B-Instruct-Q4_0.gguf (1.9GB)
├── SETUP-INSTRUCTIONS.txt
└── README.txt

Setup Instructions (for recipients):

Run Jan-Setup-0.7.5.exe to install Jan
Open Jan → Settings → General → Change App Data location
Point to the Models folder on this USB drive
Import the Llama 3.2 3B model
Start chatting!

Total size: ~2.5GB (fits on 32GB drive with room to spare)

Setup time: 2-3 minutes

For Local Development (API Access)

Jan includes a built-in Local API Server:

Install Jan
Load Llama 3.2 3B model
Enable Local API Server in Jan settings
Access API at http://localhost:1337

Benefits over Ollama:

Same REST API interface
7x faster (56 tok/s vs 7 tok/s)
No separate installation needed

Example API call:

				
					curl http://localhost:1337/v1/chat/completions \
  -H "Content-Type: application/json" \
  -d '{
    "model": "llama-3.2-3b-instruct",
    "messages": [
      {"role": "user", "content": "Hello!"}
    ]
  }'

For Teams/Enterprise

Recommended approach:

Central model storage on shared drive or NAS
Jan installed on each workstation
Point Jan to shared model folder
One model library serves entire team

Benefits:

No duplicate model downloads
Consistent model versions
Easy updates (update once, everyone gets it)
56 tok/s performance for all users

The Speed Mystery: Why Is Jan 7x Faster?

I investigated this thoroughly because a 7x speed difference with the same model seemed impossible.

What I tested:

Top-K settings – Jan had top_k: 2 (aggressive). Changed to top_k: 40 (standard). Speed remained at 56 tok/s. Not the cause.
GPU utilization – Both apps showed similar GPU usage (~27%). Not the cause.
Same model file – Verified both apps loaded the identical .gguf file. Same model.

Conclusion: Jan has a genuinely better-optimized inference engine. This appears to be superior llama.cpp optimization, not a quality trade-off.

This is a real, significant advantage—not a trick.

Cost Analysis: Local vs Cloud

Cloud AI (OpenAI API)

GPT-4o (similar quality):

Input: $2.50 per 1M tokens
Output: $10.00 per 1M tokens
Average query: ~500 input + 500 output tokens
Cost per query: ~$0.006

Monthly costs (100 queries/day):

Small team: ~$18/month
Medium team: ~$180/month
Large team: ~$1,800/month

Annual costs: $216 – $21,600

Local AI (Jan + Llama 3.2 3B)

One-time costs:

USB drive (64GB): $15-25
Time to setup: 30 minutes
Total: ~$50

Ongoing costs:

Electricity: negligible
Updates: free

Break-even point:

Small team: 3 months
Medium team: 1 week
Large team: 1 day

Privacy & Security Benefits

What Stays Local

All queries and responses
Custom configurations
Model weights
Conversation history

What Never Leaves Your Device

Proprietary business information
Customer data
Strategic plans
Financial information

Compliance Benefits

GDPR compliance (data doesn’t leave EU)
HIPAA compliance (health data stays local)
No third-party data retention
Full audit trail control

Common Pitfalls & How to Avoid Them

Pitfall 1: Expecting True Portability

Problem: Assuming local LLM apps run directly from USB drives

Reality: All tested UIs require installation or significant configuration

Solution: Accept that 2-3 minute setup is required. Include installer + models on USB.

Pitfall 2: Choosing GPT4All for "Portability"

Problem: GPT4All is often recommended as the “portable” option

Reality: GPT4All has the same drive letter dependencies as Jan, but is 7x slower

Solution: Use Jan. Since both require setup, choose the faster option.

Pitfall 3: Wrong Model Size

Problem: Downloading the largest model thinking “bigger is better”

Solution: Match model size to your VRAM:

4GB VRAM: Llama 3.2 3B (perfect fit)
6GB VRAM: Up to 7B models
8GB+ VRAM: Up to 13B models

Pitfall 4: Ignoring the Speed Difference

Problem: Settling for 8 tok/s when 56 tok/s is available

Solution: Jan’s 7x speed improvement transforms the user experience. A 3-second response vs 20-second response is the difference between “useful tool” and “frustrating wait.”

Technical Discovery: Unified Model Library

One useful finding: You can use a single model library across multiple UIs.

Folder Structure That Works:

				
					Models-Shared/
├── meta-llama/
│   └── Llama-3.2-3B-Instruct/
│       └── Llama-3.2-3B-Instruct-Q4_0.gguf
├── microsoft/
│   └── Phi-3-mini/
│       └── Phi-3-mini-4k-instruct.Q4_0.gguf
└── deepseek/
    └── DeepSeek-R1-Distill/
        └── DeepSeek-R1-Distill-Qwen-1.5B-Q4_0.gguf

Jan: Point to this folder ✅
GPT4All: Scans subdirectories automatically ✅
LM Studio: Works with this structure ✅
Ollama: Use Modelfiles to reference these paths ✅

No duplicate model files needed across applications.

Final Recommendations

For USB Distribution (Conference Swag, Demos)

Component	Details
UI	Jan (installer on USB)
Model	Llama 3.2 3B Q4_0
Total Size	~2.5GB
Setup Time	2-3 minutes

Why: Jan’s speed advantage makes the installation worthwhile. Recipients get 7x faster AI than with any “portable” alternative.

For Daily Business Use

Component	Details
UI	Jan (installed locally)
Model	Llama 3.2 3B Q4_0
Speed	56 tok/s
Experience	Near-instant responses

Why: No reason to use anything slower when Jan is free and open source.

For App Development (API Access)

Component	Details
UI	Jan (with Local API Server)
API	http://localhost:1337
Speed	56 tok/s (same as UI)
Format	OpenAI-compatible endpoints

Why: Jan’s built-in API server provides the same 7x speed advantage. No need for separate Ollama installation.

For Coding Tasks

Component	Details
UI	Jan
Model	Phi-3 Mini (for code) + Llama 3.2 3B (for everything else)
Switch	Use Phi-3 for code generation, Llama for business content

Why: Phi-3 excels at code but produces unprofessional business content. Keep both models loaded.

Conclusion: Jan + Llama 3.2 3B Is the Answer

After extensive hands-on testing, the conclusion is clear:

The Winning Combination

✅ UI: Jan (7x faster than alternatives) ✅ Model: Llama 3.2 3B (best quality/size ratio) ✅ Speed: 56 tokens/second ✅ Quality: Professional-grade output ✅ Size: 1.9GB (fits in 4GB VRAM) ✅ License: Open source, commercially usable

The Myth Busted

❌ “Portable” local AI – None of the UIs run reliably from USB without installation ❌ GPT4All for portability – Same setup requirements as Jan, but 7x slower ❌ Bigger models are better – 8B models exceed typical laptop VRAM

The Reality

Local AI is ready for business use, but requires a 2-3 minute installation. Once installed, Jan + Llama 3.2 3B provides:

Faster than ChatGPT response times
Professional-quality output
Complete privacy (nothing leaves your device)
Zero ongoing costs
No internet required

The 2-3 minute setup is a small price for 7x performance and complete data privacy.

Resources

Official Downloads:

Jan: https://jan.ai/ (Recommended)
Jan GitHub: https://github.com/janhq/jan
Models: https://huggingface.co/

Appendix: Speed Comparison Summary

UI	Speed	Portable?	Verdict
Jan	56 tok/s	❌ (install required)	✅ Winner
GPT4All	8 tok/s	❌ (install required)	❌ Too slow
Ollama	7 tok/s	❌ (service required)	❌ API only
LM Studio	7 tok/s	❌ (install required)	❌ No advantage

Since all options require installation, choose the fastest: Jan.

Appendix: Full Test Environment

				
					Laptop: Lenovo ThinkPad P14s Gen 5
Model: 21G2002DUS
CPU: Intel Core Ultra 7 155H (16 cores, up to 4.8GHz)
GPU: NVIDIA RTX 500 Ada Generation (4GB GDDR6)
RAM: 96GB DDR5 5600MHz
Storage: 2TB NVMe SSD (internal) + 2TB USB 3.1 SSD (external)
OS: Windows 11 Pro

Avi Kumar

Avi Kumar is a marketing strategist, AI toolmaker, and CEO of Kuware, InvisiblePPC, and several SaaS platforms powering local business growth.

Read Avi’s full story here.

Greatest hits

AI (Artificial Intelligence)