Local AI Tools: Technical Reference Guide

Tool Comparison Matrix

Tool	Stability	Memory Behavior	Production Ready	Setup Complexity	GPU Support
Ollama	Rock solid	Predictable 8GB VRAM	✅ Yes	Easy	CUDA, Metal, OpenCL
LM Studio	Crashes every 2-3h	Memory leaks 8→30GB	❌ Desktop only	Download & run	CUDA, Metal
Jan	Variable	Unpredictable 3-15GB	❌ Desktop only	Easy but complex config	CUDA, Metal
GPT4All	Reliable	Consistent 8-9GB	❌ Desktop only	Download & run	Vulkan, CUDA, Metal
llama.cpp	Bulletproof when working	Minimal usage	✅ Yes	Compilation hell	All backends

Critical Performance Thresholds

VRAM Requirements (Real World)

• 7B models: 8-10GB VRAM (not 4-6GB as marketed)
• 13B models: 12-16GB VRAM minimum
• 30B+ models: 20GB+ or painfully slow
• 70B models: Multiple GPUs required

Breaking Points

• LM Studio: Memory leak hits 25-30GB within 2-3 hours active use
• UI failure: 1000+ spans breaks debugging for distributed transactions
• Thermal throttling: GPUs throttle at 83°C
• Swap death: Model larger than RAM = unusable performance

Production Deployment Reality

What Works in Production

Ollama Only

• Docker container runs months without intervention
• Load balancing with nginx confirmed working
• Multi-user API support
• OpenAI-compatible endpoints

Critical Configuration:

docker run -d --gpus=all -v ollama:/root/.ollama -p 11434:11434 --name ollama --restart=unless-stopped ollama/ollama

Failure Modes by Tool

LM Studio

• Predictable memory leak pattern: 8GB→40GB before crash
• Random crashes on large model loading
• Cannot run headless/server mode
• Restart required every 2-3 hours

Jan

• Configuration overwrites during updates (data loss risk)
• 47 settings across 8 categories require manual tuning
• Memory allocation needs hardware-specific adjustment

GPT4All

• Single-user limitation (no multi-user scaling)
• Slow model downloads
• GPU acceleration inferior to alternatives

llama.cpp

• CUDA compilation fails randomly
• OS update dependency breakage
• Documentation assumes expert knowledge

Resource Investment Requirements

Hardware Costs

• RTX 4070 GPU: ~$600
• Additional RAM: ~$200
• Fast SSD: ~$150
• Total hardware: $900-1000

Time Investment

• Ollama setup: 30 minutes to production
• LM Studio: 10 minutes install + restart management overhead
• Jan configuration: Hours tweaking 47+ settings
• llama.cpp: Weekend-destroying compilation process
• GPT4All: 10 minutes to working state

Ongoing Maintenance

• Ollama: Minimal (months between interventions)
• LM Studio: 2-3 hours/week restarts
• Jan: Variable based on configuration complexity
• GPT4All: Near-zero maintenance
• llama.cpp: Expert-level debugging when broken

Decision Criteria by Use Case

Scenario	Primary Choice	Reason	Critical Limitations
Production (100+ users)	Ollama	Only scalable option	Ugly CLI interface
Individual developer	GPT4All	Zero-maintenance reliability	Desktop-only constraint
Team (5-15 devs)	Ollama + Open WebUI	API + GUI hybrid	Additional complexity
Client demos	LM Studio	Professional appearance	Requires restart monitoring
Maximum performance	llama.cpp	Highest tokens/sec	Compilation expertise required
Compliance/Privacy	GPT4All	Clear licensing, no telemetry	Single-user scaling limit

Common Failure Scenarios

Memory-Related Failures

1. Model swapping: Performance death spiral when model > available RAM
2. Chrome interference: Browser tabs competing for memory resources
3. GPU memory exhaustion: Tools handle differently (crash vs CPU fallback)

Performance Degradation Patterns

1. Overheating throttling: Monitor GPU temps, throttling starts at 83°C
2. Too many CPU threads: Counter-intuitive performance decrease
3. Disk I/O bottlenecks: Slow SSDs create model loading delays

Troubleshooting Checklist

1. Verify GPU utilization: nvidia-smi should show 85-95% during inference
2. Check model quantization: Q4_K_M optimal for most use cases
3. Monitor thermal status: Cooling inadequacy causes silent performance loss
4. Kill competing processes: Browser tabs are primary memory competitor

Break-Even Analysis

Cost Comparison

• Cloud API break-even: $100+/month usage = <1 year ROI
• Electricity costs: $30-50/month for 24/7 operation
• Maintenance time: 2-4 hours/week average across tools

Hidden Costs

• Learning curve: 1-2 weeks to production competency
• Debugging time: Variable by tool complexity
• Hardware obsolescence: 3-4 year replacement cycle
• Model storage: 4-50GB per model (SSD costs)

Critical Warnings

What Documentation Doesn't Tell You

• Default settings fail in production: All tools require hardware-specific tuning
• Version updates break configurations: Jan particularly problematic
• Multi-model memory stacking: Each loaded model consumes VRAM even when idle
• API compatibility gaps: OpenAI compatibility has subtle differences

Absolute Don'ts

• Don't use for mission-critical systems: Downtime during failures guaranteed
• Don't trust memory usage estimates: Marketing numbers 30-50% lower than reality
• Don't run without monitoring: Silent failures and performance degradation common
• Don't deploy without backup plan: Cloud API fallback essential

Model Format Compatibility

• Universal format: GGUF works across all tools
• Migration friendly: Model files portable between tools
• Quantization impact: Q4_K_M provides best quality/performance balance
• Storage planning: Plan 10-50GB per model depending on parameters

Enterprise Considerations

• License compliance: MIT (Ollama, GPT4All) vs Proprietary (LM Studio) vs AGPLv3 (Jan)
• Support availability: Community-driven, no commercial SLA options
• Security implications: Local processing eliminates cloud data exposure
• Audit trail: Minimal logging capabilities across all tools