Choosing Hardware for Local AI: The Complete Guide to Running Small Language Models at Home

The Case for Local Intelligence

There’s something profoundly satisfying about running AI models on your own hardware. No API keys. No usage limits. No data leaving your machine. No monthly bills that scale with curiosity. Just you, your computer, and a model that responds as fast as your silicon allows.

Cloud AI services are convenient. They’re also expensive, privacy-concerning, and dependent on someone else’s infrastructure. When OpenAI has an outage, your workflow stops. When Anthropic changes pricing, your budget changes. When any provider decides to modify content policies, your capabilities change without your input.

Local AI changes this equation. The models are yours. The data stays yours. The capability persists regardless of internet connectivity or corporate decisions. The tradeoff is hardware investment—which brings us to the question this guide answers: what hardware do you actually need?

My British lilac cat has no opinion on local versus cloud AI. She operates on biological neural networks that require no external infrastructure—just regular feeding and occasional attention. Her inference speed is adequate for her use cases. Her context window is suspiciously short, particularly regarding previous scratching incidents. But she’s fully local, which counts for something.

This guide examines hardware requirements for running small language models locally. Not the massive models requiring data center infrastructure. The practical models—7B to 70B parameters—that run on consumer and prosumer hardware while providing genuinely useful capabilities.

Let’s figure out what you actually need.

How We Evaluated: The Methodology

Hardware recommendations require methodology. Marketing claims mean nothing without real-world validation.

Step One: Model Mapping. We identified the models people actually want to run locally: Llama variants, Mistral, Phi, Qwen, and their fine-tuned derivatives. Each has different resource requirements that hardware must meet.

Step Two: Performance Measurement. We measured tokens per second across hardware configurations. This metric matters—slow inference destroys usability. We established minimum acceptable speeds for interactive use.

Step Three: Memory Analysis. Model size determines VRAM requirements. We calculated actual memory needs for various quantization levels, establishing clear thresholds for different model classes.

Step Four: Cost-Benefit Calculation. Expensive hardware provides diminishing returns. We identified value inflection points where additional spending stops providing proportional benefit.

Step Five: Real-World Testing. Synthetic benchmarks lie. We ran actual inference workloads—coding assistance, writing help, data analysis—and measured practical utility, not just theoretical performance.

This process revealed clear hardware tiers that serve different needs and budgets. The recommendations that follow emerge from actual testing, not theoretical specifications.

Understanding the Bottlenecks

Before choosing hardware, understand what limits local AI performance.

VRAM: The Primary Constraint

Graphics card memory—VRAM—is the single most important factor for local LLM performance. Models must fit in VRAM for GPU acceleration. Models that exceed VRAM either fail to load or fall back to much slower CPU inference.

The math is straightforward:

Quantization	Memory per Billion Parameters
FP16 (full)	~2GB
Q8 (8-bit)	~1GB
Q4 (4-bit)	~0.5GB
Q2 (2-bit)	~0.25GB

A 7B parameter model at Q4 quantization needs approximately 4GB VRAM. A 70B model at Q4 needs approximately 40GB. These numbers guide hardware selection.

Memory Bandwidth: The Speed Determinant

VRAM capacity determines whether a model fits. Memory bandwidth determines how fast it runs. Tokens per second correlates directly with how quickly data moves between memory and processing units.

Consumer GPUs have surprisingly high bandwidth. An RTX 4090 provides 1TB/s. An M2 Ultra provides 800GB/s. Even mid-range options like RTX 4070 Ti provide 500GB/s+. These numbers translate directly into inference speed.

Context Length: The Hidden Cost

Longer context windows require more memory and computation. A model running comfortably with 2K context may struggle at 32K context. Hardware requirements scale non-linearly with context length.

Plan for your actual context needs. If you’re processing short queries, aggressive specifications aren’t necessary. If you’re analyzing long documents, budget for substantially more resources.

graph TD
    A[Model Selection] --> B{Size}
    B -->|7B| C[8GB VRAM Minimum]
    B -->|13B| D[12GB VRAM Minimum]
    B -->|34B| E[24GB VRAM Minimum]
    B -->|70B| F[48GB+ VRAM Required]
    C --> G[RTX 4060/4070]
    D --> H[RTX 4070 Ti/4080]
    E --> I[RTX 4090/A6000]
    F --> J[Multi-GPU or Apple Silicon]

The GPU Landscape: What Actually Works

GPUs provide the acceleration that makes local AI practical. Choosing correctly matters enormously.

NVIDIA: The Default Choice

NVIDIA dominates local AI for good reason: CUDA support is universal. Every framework, every tool, every optimization targets NVIDIA first. The ecosystem advantage is substantial.

RTX 4060 (8GB): Entry point for serious local AI. Runs 7B models comfortably at Q4. Context length limitations and no room for larger models. Adequate for experimentation and light use.

RTX 4070 Ti Super (16GB): The sweet spot for many users. Runs 7B models at higher quantization, 13B models at Q4, and provides headroom for longer contexts. Price-to-capability ratio is excellent.

RTX 4080 Super (16GB): Faster than 4070 Ti but same VRAM. Worth considering if you find good pricing; not worth premium over 4070 Ti for most use cases.

RTX 4090 (24GB): The consumer king. Runs 34B models comfortably. 70B models at aggressive quantization work but push limits. The performance is remarkable; the price is substantial.

RTX 3090/3090 Ti (24GB): Previous generation but still excellent value used. VRAM matches 4090 at lower cost. Performance is slower but often adequate. The used market provides significant savings.

AMD: The Underdog

AMD GPUs work for local AI but face ecosystem challenges. ROCm support lags CUDA. Not all frameworks optimize for AMD. Compatibility issues appear more frequently.

RX 7900 XTX (24GB): Impressive specifications at compelling price. When software works, performance competes with NVIDIA. When software doesn’t work, troubleshooting consumes hours.

RX 7900 XT (20GB): Unusual VRAM amount provides interesting middle ground. Same compatibility caveats apply.

For users comfortable with Linux and willing to troubleshoot, AMD provides value. For those wanting simplest path to working local AI, NVIDIA remains easier.

Apple Silicon: The Unified Memory Advantage

Apple Silicon changes local AI economics through unified memory. The entire system memory pool—up to 192GB on M2 Ultra—is available for model loading. No VRAM limitations in the traditional sense.

M1/M2/M3 (8-24GB): Base configurations handle 7B models adequately. Performance is reasonable if not exceptional. The existing hardware you own may already be sufficient.

M2/M3 Pro (18-36GB): Expanded memory enables 13B+ models. The sweet spot for Mac users who want capable local AI without extreme investment.

M2/M3 Max (32-128GB): Serious capability for serious models. 70B models become practical. The cost is substantial but so is the capability.

M2 Ultra (64-192GB): The extreme option. Multiple 70B models simultaneously. Memory that exceeds what most users could possibly need. For those with unlimited budgets and maximum ambitions.

The Apple tax is real. Equivalent capabilities cost more than PC alternatives. But for users already in the Apple ecosystem, the integration quality and unified memory architecture provide genuine advantages.

My cat approves of Mac hardware for reasons unrelated to AI performance. The aluminum surfaces stay cool. The fan noise is minimal. The laptop form factor provides acceptable warming capabilities during operation. Her hardware preferences prioritize lap compatibility over tokens per second.

CPU Options: When GPU Isn’t Primary

Some scenarios favor CPU inference. Understanding when helps with hardware decisions.

When CPU Makes Sense

• Very long context: CPU RAM scales more affordably than VRAM
• Occasional use: GPU investment doesn’t justify infrequent inference
• Specific model architectures: Some models optimize better for CPU
• Budget constraints: CPUs provide capability at lower entry cost

Intel vs AMD

Both vendors provide capable AI inference. AMD currently leads in core count and performance per dollar at high end. Intel provides strong options at mainstream price points.

AMD Ryzen 9 7950X: 16 cores provide parallel processing capability. Excellent for CPU-only inference or hybrid GPU+CPU workloads.

AMD Threadripper: When you need maximum cores and memory channels. Enterprise pricing but enterprise capability.

Intel Core i9-14900K: Competitive single-thread performance. Hybrid architecture provides efficiency options. Strong mainstream choice.

Memory Configuration for CPU Inference

CPU inference performance depends heavily on RAM configuration:

• Capacity: Minimum 32GB for 7B models, 64GB+ for larger
• Speed: DDR5-6000 or faster improves throughput
• Channels: Dual channel minimum, quad channel preferred
• Latency: Lower CAS latency improves inference speed

The math differs from GPU. CPU inference is slower but scales with system RAM that’s cheaper than equivalent VRAM. For budget builds, CPU-only approaches remain viable.

Complete System Configurations

Theory becomes practice through complete builds. Here are proven configurations at various price points.

Budget Build: $800-1200

Goal: Run 7B models adequately for experimentation and light production use.

Components:

• CPU: AMD Ryzen 5 7600 or Intel Core i5-13400F
• GPU: RTX 4060 (8GB) or used RTX 3070 (8GB)
• RAM: 32GB DDR5-5600
• Storage: 1TB NVMe SSD
• PSU: 550W 80+ Bronze

Capability: 7B models at Q4-Q8 quantization. ~20-30 tokens/second. Context length limited by VRAM. Adequate for coding assistance, short conversations, and experimentation.

Limitation: Cannot run larger models effectively. VRAM constrains flexibility.

Mid-Range Build: $1800-2500

Goal: Run 7B-13B models comfortably with room for growth.

Components:

• CPU: AMD Ryzen 7 7800X3D or Intel Core i7-14700K
• GPU: RTX 4070 Ti Super (16GB) or used RTX 3090 (24GB)
• RAM: 64GB DDR5-6000
• Storage: 2TB NVMe SSD
• PSU: 750W 80+ Gold

Capability: 7B models at full precision. 13B models at Q4-Q8. Comfortable context lengths. ~40-60 tokens/second on 7B models.

Advantage: The used RTX 3090 path provides 24GB VRAM at significantly lower cost than RTX 4090. For inference (not training), the performance difference is acceptable.

High-End Build: $4000-6000

Goal: Run up to 34B models locally with excellent performance.

Components:

• CPU: AMD Ryzen 9 7950X or Intel Core i9-14900K
• GPU: RTX 4090 (24GB)
• RAM: 128GB DDR5-6000
• Storage: 4TB NVMe SSD
• PSU: 1000W 80+ Platinum

Capability: 7B-34B models at various quantizations. 70B models at aggressive Q2-Q3 quantization with limitations. ~80-100 tokens/second on 7B models.

Note: This configuration handles most practical local AI needs. The jump to larger models requires multi-GPU or Apple Silicon.

Apple Silicon Path: $3000-8000+

Goal: Unified memory advantage for maximum model flexibility.

M3 Max MacBook Pro (64GB): Portable capability. Runs 34B models. Laptop form factor with genuine power. ~$4000

M2 Ultra Mac Studio (192GB): Desktop powerhouse. Runs multiple 70B models simultaneously. Maximum unified memory available. ~$8000+

The Apple premium is undeniable. But for users valuing integration, silence, efficiency, and the ability to run very large models through unified memory, the investment has logic.

Software Configuration: Making Hardware Work

Hardware without software is expensive silence. Configuration matters.

Ollama: The Simplest Path

curl -fsSL https://ollama.ai/install.sh | sh
ollama run llama3:8b

Ollama provides single-command model deployment. Download, quantization, and inference handled automatically. For beginners and those wanting minimal friction, Ollama removes barriers.

LM Studio: The GUI Option

For users preferring graphical interfaces, LM Studio provides point-and-click model management. Download models from Hugging Face. Configure parameters visually. Run inference through clean interface.

The capability matches command-line alternatives. The accessibility suits different user preferences.

llama.cpp: Maximum Control

For those wanting maximum performance and control, llama.cpp provides the foundation. Most other tools build on it. Direct use enables fine-grained optimization.

./main -m models/llama-7b-q4.gguf -p "Your prompt here" -n 256

The learning curve is steeper. The control is absolute. For power users, llama.cpp rewards investment.

vLLM: Production Deployment

When local AI serves applications rather than individuals, vLLM provides production-grade serving:

python -m vllm.entrypoints.openai.api_server --model mistralai/Mistral-7B-v0.1

The OpenAI-compatible API enables drop-in replacement for cloud services. Your applications don’t need to know the model runs locally.

Model Selection: Matching Software to Hardware

Hardware determines what models run. Understanding the landscape helps choose wisely.

The 7B Class

Llama 3 8B: Meta’s latest iteration. Strong general capability. Excellent instruction following. The default choice for 7B class.

Mistral 7B: Punches above its weight. Particularly strong for coding. Efficient inference characteristics.

Phi-3 Mini: Microsoft’s small model series. Impressive capability relative to size. Good for constrained environments.

Qwen2 7B: Strong multilingual capability. Competitive with Western alternatives. Expanding ecosystem.

These models run on any recommended configuration. The 8GB VRAM minimum handles all of them at Q4 quantization.

The 13B-34B Class

Llama 3 70B (quantized): The full Llama 3 experience, compressed. Q4 quantization fits in 40GB. Remarkable capability at the cost of some precision.

CodeLlama 34B: Specialized for programming. Runs on 24GB VRAM at Q4. Strong for development workflows.

Mixtral 8x7B: Mixture of experts architecture. Runs like a 12B model, thinks like a 45B model. Excellent efficiency.

These models require at least 16GB VRAM, with 24GB preferred. The mid-range and high-end configurations handle them well.

The 70B Class

Llama 3 70B: Full-size Llama experience. Requires 40GB+ VRAM at Q4 or 80GB+ at Q8. Multi-GPU or Apple Silicon territory.

Qwen2 72B: Competitive with Llama 3 70B. Strong reasoning capability. Same hardware requirements.

Running 70B models locally requires serious investment. The capability is remarkable—approaching GPT-4 for many tasks. The hardware cost reflects this.

flowchart TD
    A[Budget?] --> B{< $1500}
    B -->|Yes| C[7B Models Focus]
    B -->|No| D{< $3000}
    D -->|Yes| E[13B-34B Capable]
    D -->|No| F{< $6000}
    F -->|Yes| G[34B Comfortable]
    F -->|No| H[70B Territory]
    C --> I[RTX 4060/4070]
    E --> J[RTX 4070 Ti/3090]
    G --> K[RTX 4090]
    H --> L[Multi-GPU/Apple Silicon]

The Generative Engine Optimization Connection

Here’s something hardware guides rarely address: how local AI capability connects to Generative Engine Optimization.

GEO concerns making content and systems discoverable by AI. Running AI locally changes this dynamic in interesting ways.

Consider content creation workflows. Local AI enables private iteration. Draft content locally, refine without cloud exposure, publish only final versions. The intermediate steps—potentially revealing strategy, process, or competitive intelligence—never leave your machine.

Consider data processing. AI analyzing proprietary documents locally keeps that data private. Cloud AI services necessarily see what you send them. Local processing maintains confidentiality while enabling AI capability.

Consider response time. Local AI responds immediately. No network latency. No queue waiting. For workflows involving frequent AI interaction, the responsiveness improvement compounds into significant productivity gains.

Consider cost structure. Cloud AI charges per token. Local AI charges nothing after hardware investment. For heavy users, the economics favor local approaches. The hardware cost amortizes across usage volume.

My cat doesn’t understand GEO any better than she understands transformer architectures. But she understands local resources. Her food bowl is local. Her sleeping spots are local. Her attention-demanding behaviors target local humans. The principle translates: local capability provides control that remote dependency cannot.

Power and Cooling Considerations

AI inference generates heat. Planning prevents problems.

Power Requirements

GPU	TDP	System Total
RTX 4060	115W	~350W
RTX 4070 Ti Super	285W	~550W
RTX 4090	450W	~750W
Multi-GPU	900W+	~1200W+

PSU selection should exceed these numbers by 20-30% for headroom and efficiency. Quality matters—cheap PSUs risk system stability during sustained loads.

Cooling Strategies

Inference workloads create sustained heat. Unlike gaming’s variable loads, AI inference runs continuously at high utilization.

• Air cooling: Adequate for single-GPU configurations with good case airflow
• AIO liquid cooling: Preferred for high-TDP GPUs and sustained workloads
• Custom loop: For multi-GPU or maximum silence requirements
• Ambient temperature: Room temperature matters; consider HVAC implications

Noise Considerations

GPU fans at full speed create significant noise. For workstation environments, consider:

• Undervolting: Reduce power/heat without major performance loss
• Custom fan curves: Balance temperature and noise
• Sound dampening: Cases with acoustic panels
• Physical isolation: Locate hardware in separate space if noise sensitivity is high

The Upgrade Path: Future-Proofing Decisions

Hardware decisions have longevity implications. Planning for upgrades reduces long-term cost.

VRAM Prioritization

VRAM requirements only increase. Models grow larger. Techniques improve slowly. Choosing maximum affordable VRAM extends useful hardware life.

The RTX 4070 Ti’s 16GB may feel limiting within two years. The RTX 4090’s 24GB provides more runway. The used RTX 3090’s 24GB offers the same buffer at lower cost.

Platform Longevity

Motherboard and CPU platforms have lifecycle limitations. AMD’s AM5 platform promises multi-generation support. Intel’s platforms historically offer shorter upgrade windows.

Investing in platforms with longer support enables CPU upgrades without rebuilding entire systems.

Multi-GPU Considerations

Current multi-GPU support for inference is improving. Systems supporting multiple GPUs may become more valuable as software catches up.

SLI/NVLink isn’t necessary for inference—simple multi-GPU configurations work. Motherboards with multiple PCIe x16 slots and sufficient power delivery enable future expansion.

Common Mistakes to Avoid

Experience reveals patterns. These mistakes waste money.

Prioritizing CPU over GPU: For AI inference, GPU matters more. An expensive CPU with weak GPU underperforms a modest CPU with strong GPU.

Insufficient VRAM: 8GB seems adequate until you try 13B models. Stretching for 16GB or 24GB prevents early upgrade pressure.

Ignoring quantization: Not understanding quantization leads to confusion about actual requirements. Learn the relationship between model size, quantization level, and VRAM needs.

Overbuying immediately: The field evolves rapidly. Hardware that’s optimal today may be poor value next year. Match purchase timing to actual need.

Neglecting software research: Hardware without compatible software is useless. Verify software support before purchasing, especially for AMD GPUs.

Forgetting total cost: PSU, cooling, case, storage—these costs add up. Budget for complete systems, not just GPU.

The Decision Framework

Practical decisions require clear frameworks. Here’s the summary:

For experimentation and light use: Budget build with RTX 4060. Adequate for learning and occasional assistance. Upgrade path clear when needs grow.

For regular production use: Mid-range build with RTX 4070 Ti Super or used RTX 3090. Handles most practical models. Good value inflection point.

For power users: High-end build with RTX 4090. Runs everything up to 34B comfortably. 70B with limitations. Maximum consumer capability.

For unlimited budgets: Apple Silicon Mac Studio with maximum memory or multi-GPU workstation. Runs anything. Costs accordingly.

For existing Mac users: Evaluate current hardware first. Recent Apple Silicon may already suffice. Upgrade within Apple ecosystem if needed.

Final Thoughts: The Local AI Future

Local AI is becoming practical for regular users. Hardware that was enterprise-only five years ago is consumer-accessible today. Models that required cloud infrastructure run on desktop machines.

This trend continues. Hardware improves. Models become more efficient. The threshold for useful local AI keeps dropping. What requires RTX 4090 today may run on entry-level hardware in two years.

But waiting has costs. The capability is useful now. The privacy is valuable now. The independence from cloud providers matters now. For those with current needs, appropriate hardware investment delivers immediate returns.

My British lilac cat runs entirely on local hardware. Her neural networks require no cloud connectivity. Her inference happens in real-time without API latency. Her operating costs are food and veterinary care, not token-based billing. She’s been running locally for years, demonstrating that the best AI doesn’t always require the newest hardware—sometimes it just requires commitment to local infrastructure.

Your local AI journey starts with honest assessment of needs, realistic budgeting, and clear understanding of tradeoffs. The hardware options exist across all price points. The software ecosystem supports multiple approaches. The models are available and improving.

The only question is what capability you need and what investment you’ll make to achieve it. The answers are personal. The resources to help you decide are here.

Now stop researching and start computing. Your local AI isn’t going to run itself.