Why does it take 5 minutes to load a 7B model?

You're probably using a hard drive. Get an SSD or suffer. I loaded Llama 13B from a 5400rpm drive once - took 12 minutes every time I wanted to switch models. Same model loads in 30 seconds from my NVMe drive.

Can I run this stuff on CPU only?

Technically yes, practically no. I tried running Llama 3.1 8B on CPU-only (32-core Threadripper). Got about 2 tokens per second. My GPU does 45+ tokens per second. CPU is fine for testing if you have patience, but you'll want to upgrade to at least a 1660 Ti for real work.

My model won't load - says "CUDA out of memory"

You ran out of VRAM. Check with `nvidia-smi` to see what's using your GPU memory. Chrome with hardware acceleration eats 2GB easily. Close everything GPU-related and try again. If it still fails, use a smaller model or higher quantization (4-bit instead of 8-bit).

Should I use 4-bit or 8-bit quantization?

4-bit for most stuff. The quality loss is barely noticeable unless you're doing complex reasoning tasks. I use 4-bit Llama models and can't tell the difference for coding or writing. 8-bit if you have the VRAM to spare, but 4-bit gets you 2x more model for the same memory.

Why is Ollama slower than the benchmarks claim?

Because benchmarks lie. Ollama adds overhead for model management, API stuff, and being user-friendly. Raw llama.cpp is 10-20% faster, but good luck managing multiple models manually. For most development work, Ollama's convenience beats the performance hit.

How do I get AMD GPUs working with ROCm?

ROCm on Linux is doable but prepare for pain. Install ROCm drivers first, then fight with environment variables until something works. On Ubuntu 22.04, I had to install rocm-dev-* packages and set HIP_VISIBLE_DEVICES. Performance is about 80% of equivalent NVIDIA once it's running. On Windows, just buy NVIDIA.

Can I use multiple GPUs?

vLLM does multi-GPU well with tensor parallelism. llama.cpp has basic multi-GPU support that works okay. Ollama barely supports multi-GPU - it's designed for single-card setups. Multi-GPU is complex to configure but can double your performance if you have matching cards.

GGML vs GGUF - which one?

GGUF. GGML is dead, replaced in 2023. Don't download GGML files anymore - they're slower to load and miss features. All the new models use GGUF anyway.

My download keeps failing at 90%

Check disk space first - these models are huge. Ollama's resume (`ollama pull --resume`) works sometimes. When it doesn't, delete the partial download and start over. Don't waste hours trying to fix corrupted downloads like I did.

Why is my GPU at 0% but inference is slow?

You're probably running on CPU. Check `nvidia-smi` - if you see 0% GPU usage, your CUDA installation is broken or the model fell back to CPU. Make sure you compiled with CUDA support and your drivers are current.

How do I connect this to VSCode?

Install a code assistant extension that supports OpenAI APIs. Point it to `http://localhost:11434/v1` for Ollama. Most extensions work - Continue.dev, Codeium, and others. Some need API keys even for local models - just put "sk-local" or whatever.

What about the new RTX 50 series GPUs?

The RTX 5090 with 32GB VRAM is a beast for LLM inference. Can run quantized 70B models on a single card. The RTX 5080 with 16GB is the new sweet spot for most users - handles 13B models easily and some 30B with tight quantization. Both cards cost more than my first car but they're fast as hell.

Should I trust cloud GPU services?

Runpod, Vast.ai, and Lambda Labs are solid for testing without buying hardware. Costs $0.30-2.00/hour depending on GPU. Good for evaluating models before committing to local hardware. Just don't put sensitive data through them - you never know who's logging your prompts.

What's this about quantization formats?

Stick with GGUF Q4_K_M for most stuff. It's the best balance of quality and size. Q8_0 if you have VRAM to spare and want maximum quality. Q2_K is garbage - only use if desperate. The newer Q4_0_4_8 format gives 2-3x speedup on ARM but only works with recent llama.cpp builds.

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Local LLM Deployment: AI-Optimized Technical Reference

Executive Summary

Local LLM deployment eliminates per-token costs but requires significant hardware investment and technical expertise. VRAM is the primary constraint - insufficient VRAM causes fallback to system RAM at 0.5 tokens/second performance degradation.

Critical Hardware Requirements

VRAM Specifications (Production-Tested)

7B models: 4-6GB minimum VRAM
- Real performance: 45 tokens/second on RTX 3060 (12GB)
- Below 4GB: Falls back to system RAM with severe performance penalty
13B models: 8-12GB VRAM required
- Performance degrades significantly below 8GB
34B+ models: 24GB+ VRAM mandatory
- RTX 4090 achieves 15 tokens/second with Llama 34B
- Smaller VRAM configurations unusable

System Memory Requirements

32GB RAM recommended: Models frequently spill over from VRAM
16GB minimum: Only viable for single-model usage
Performance impact: System RAM fallback reduces speed to 0.5 tokens/second

Storage Performance Critical

NVMe SSD mandatory: Model loading times differ drastically
- NVMe: 20 seconds for large models
- HDD: 8+ minutes (operationally unusable)
Space requirements:
- Llama 3.1 8B: 4.7GB
- Llama 3.1 70B: 40GB (4-bit), 140GB (unquantized)
- Code Llama 34B: 20GB
Minimum capacity: 500GB for multiple models

GPU Platform Comparison

NVIDIA (Recommended)

Compatibility: Universal CUDA support across frameworks
Performance baseline: 100% reference performance
Power consumption:
- RTX 4080: 320W ($40/month electricity increase)
- RTX 4090: 450W (substantial power draw)
Cost analysis: RTX 4080 ($800), RTX 4090 ($1600)

AMD ROCm

Setup complexity: 6+ hours configuration on Ubuntu 22.04
Performance penalty: 20% slower than equivalent NVIDIA
Reliability issues: Kernel module conflicts, documentation inconsistencies
Linux only: Windows support nonexistent

Apple Silicon

Unified memory advantage: Uses system RAM as VRAM
Performance: M2 Mac Studio (64GB) achieves 25 tokens/second on 13B models
Power efficiency: Silent operation, low power consumption
Limitation: Slower than dedicated GPU solutions

Framework Performance Analysis

Framework	Setup Complexity	Performance (TPS)	Memory Efficiency	Concurrent Users	Production Ready
Ollama	Minimal	41 peak	Good	Single user	Development only
llama.cpp	Moderate	Excellent	Excellent	Limited	Resource-constrained
vLLM	High	793 peak	Good	Excellent	Enterprise

Ollama

Installation success rate: High across platforms
Performance overhead: 10-20% slower than raw llama.cpp
Scaling limitation: Maximum 4 concurrent users
Use case: Rapid prototyping, local development

llama.cpp

Compilation requirements: CUDA toolkit version must match exactly
Performance: Best single-user throughput
API compatibility: OpenAI-compatible HTTP server
Multi-GPU support: Basic, not production-grade

vLLM

Installation failure rate: High due to CUDA/PyTorch conflicts
Multi-GPU capability: Advanced tensor parallelism up to 8 GPUs
Throughput: Designed for production inference loads
Resource overhead: Requires server infrastructure

Critical Installation Warnings

llama.cpp Compilation Issues

CUDA version conflicts: "nvcc fatal: Unsupported gpu architecture" common error
Build requirements: Visual Studio on Windows causes 3+ hour setup time
Solution: Use exact CUDA toolkit version specified in documentation

vLLM Installation Failures

Common error: "RuntimeError: CUDA error: no kernel image available"
Root cause: PyTorch CUDA version mismatch with driver CUDA version
Resolution: Complete environment rebuild required
Time investment: 2+ hours for successful installation

Model Format Compatibility

GGML format: Deprecated since 2023, avoid completely
GGUF format: Current standard, required for all new implementations
Migration impact: GGML models have slower loading and missing features

Production Deployment Considerations

Performance Monitoring Critical Points

VRAM utilization: Monitor with nvidia-smi continuously
Memory leak detection: Grafana dashboards prevent 3am alerts
Load balancing: Multiple vLLM instances behind NGINX/HAProxy
Capacity planning: 10-50 concurrent requests per instance depending on hardware

Quantization Trade-offs

4-bit (Q4_K_M): Recommended default, minimal quality loss
8-bit: Use only with abundant VRAM
2-bit: Quality severely degraded, emergency use only
Performance impact: 4-bit provides 2x memory efficiency vs 8-bit

Common Failure Scenarios

Memory Exhaustion

Symptoms: "CUDA out of memory" errors
Diagnosis: Chrome with hardware acceleration consumes 2GB VRAM
Solution: Close all GPU-accelerated applications before model loading

Performance Degradation

CPU fallback: 0% GPU utilization indicates CUDA failure
System swap: vm.swappiness=10 prevents swap death spiral
Network bottleneck: Large model downloads fail at 90% frequently

Model Loading Failures

Disk space: Verify available space before download
Resume capability: Ollama resume works inconsistently
Corruption recovery: Delete partial downloads, restart completely

Cost-Benefit Analysis

Hardware Investment Thresholds

Entry level: RTX 3060 (12GB) - $400, handles 7B models effectively
Professional: RTX 4080 (16GB) - $800, supports 13B models
Enterprise: RTX 4090 (24GB) - $1600, enables 34B model deployment

Operational Costs

Electricity: $40/month increase for RTX 4080 continuous operation
Time investment: 2-6 hours initial setup per framework
Maintenance overhead: Regular driver updates, model management

Break-even Calculation

Token usage threshold: Cost-effective after high-volume usage
Privacy benefit: No data transmission to external APIs
Development velocity: Immediate inference without API dependencies

Integration Specifications

API Compatibility

OpenAI standard: All frameworks support compatible endpoints
Local endpoints:
- Ollama: http://localhost:11434/v1
- llama.cpp: http://localhost:8080
- vLLM: http://localhost:8000

IDE Integration

VSCode extensions: Continue.dev, Codeium support local endpoints
Authentication: Use dummy API key "sk-local" for local models
Performance impact: Local inference eliminates network latency

Resource Links (Verified Quality)

Official Documentation: Ollama - Installation success rate >90%
Technical Reference: llama.cpp GitHub - Comprehensive troubleshooting
Model Repository: Hugging Face GGUF Models - Filter by download count
Hardware Analysis: Local LLM Hardware Guide 2025 - Real performance data
Community Support: Ollama Discord - Fast technical support

Decision Matrix

Choose Ollama When:

Development/prototyping focus
Minimal setup time required
Single-user environment
Hardware constraints (limited VRAM)

Choose llama.cpp When:

Maximum performance required
Resource-constrained environment
Technical expertise available
Custom optimization needed

Choose vLLM When:

Production deployment planned
Multiple concurrent users
Multi-GPU hardware available
Enterprise scalability required

Critical Success Factors

VRAM adequacy: Verify model requirements before hardware purchase
Storage performance: NVMe SSD mandatory for operational usage
Framework alignment: Match framework to use case and expertise level
Monitoring implementation: Prevent resource exhaustion failures
Quantization strategy: Balance quality vs resource requirements

Useful Links for Further Investigation

Resources That Don't Suck

Link	Description
Ollama Official Website	The official Ollama site. Their docs are actually decent, which is rare. Has all the models you'll probably want without digging through Hugging Face's chaos. Install instructions work most of the time.
llama.cpp GitHub Repository	The source code and compilation instructions. README is huge but comprehensive. Issues section is where you'll find solutions to weird compile errors. Performance optimization tips are buried in the docs but worth finding.
vLLM Documentation	Better than most enterprise software docs. Actually tells you how to configure multi-GPU setups instead of just saying \"it's supported.\" Installation section prepares you for the dependency hell you're about to enter.
Hugging Face Model Hub	Where all the models actually are. Filter by GGUF format or you'll waste time with incompatible files. Lots of garbage mixed with gems - check the download counts and recent activity.
Ollama Model Search	Pre-configured models that work with Ollama out of the box. Less selection than Hugging Face but everything actually works. Good starting point before diving into the HF rabbit hole.
LocalLLM.in Model Reviews	Someone actually tests these models instead of just posting download links. Focuses on coding performance which is what most of us care about. Updates regularly with new releases.
Local LLM Hardware Guide 2025	Real hardware recommendations based on actual testing. No affiliate marketing bullshit, just what works for different budgets. GPU recommendations are spot-on.
VRAM Usage Calculator	Helps you figure out if your GPU can handle a specific model before downloading 40GB files. Math checks out for the models I've tested.
LocalLLM Community Hub	Where people actually share what works and what's bullshit. Skip the vendor marketing and see what models people are running on real hardware. Great benchmarks and honest reviews of local models.
Ollama Discord Community	Fast support when stuff breaks. Community is helpful and the devs actually respond. Better than GitHub issues for quick \"is this normal?\" questions.
Continue.dev	Open-source code assistant that works with local models. Actually respects your privacy instead of sending your code to random APIs. Setup takes 5 minutes and works with most editors.
Posit Local LLM Integration Guide	If you do data science stuff, this shows you how to connect Jupyter notebooks and RStudio to your local models. Clear instructions that actually work.
Multi-GPU Performance Comparison	Real benchmarks with actual numbers, not marketing fluff. Compares throughput across different hardware setups. Methodology is solid and results match what I've seen in practice.

Local LLM Deployment: AI-Optimized Technical Reference

Executive Summary

Critical Hardware Requirements

VRAM Specifications (Production-Tested)

System Memory Requirements

Storage Performance Critical

GPU Platform Comparison

NVIDIA (Recommended)

AMD ROCm

Apple Silicon

Framework Performance Analysis

Ollama

llama.cpp

vLLM

Critical Installation Warnings

llama.cpp Compilation Issues

vLLM Installation Failures

Model Format Compatibility

Production Deployment Considerations

Performance Monitoring Critical Points

Quantization Trade-offs

Common Failure Scenarios

Memory Exhaustion

Performance Degradation

Model Loading Failures

Cost-Benefit Analysis

Hardware Investment Thresholds

Operational Costs

Break-even Calculation

Integration Specifications

API Compatibility

IDE Integration

Resource Links (Verified Quality)

Decision Matrix

Choose Ollama When:

Choose llama.cpp When:

Choose vLLM When:

Critical Success Factors

Useful Links for Further Investigation

Resources That Don't Suck

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