A detailed PC build guide with full parts list, compatibility verification, performance expectations, upgrade paths, and step-by-step build commentary. Each component includes real-time pricing from Amazon.
The one spec that matters for running local LLMs is VRAM. Here's the best hardware at $900, $1,800, and $3,500+ to run 7B, 70B, and 100B+ models locally.
Running language models locally has changed from a niche research activity to something practical. Ollama, LM Studio, and vLLM have lowered the setup barrier dramatically, and current GPU hardware can run genuinely useful models on a desktop machine. The question isn't whether to do it — it's which hardware makes sense for which model sizes.
This guide cuts through the noise. VRAM is the bottleneck for local LLM inference, everything else is secondary. Here's what actually matters, and three builds at different price points.
Key Takeaways
VRAM determines which models you can run; CPU and system RAM barely matter for inference speed once the model fits in VRAM
A single RX 9070 XT (16GB) runs 7B–13B models at 30–60 tokens/second; a 70B model needs 40GB+ VRAM across two GPUs
Ollama, LM Studio, and KoboldCpp run on both Nvidia and AMD GPUs — ROCm support has improved substantially in 2025
Local models provide complete privacy: your prompts never leave your machine (Ollama, 2024)
Why Build a Local AI PC? Privacy, Speed, and Cost Savings
The practical case for local AI in 2026 is stronger than it was two years ago. API costs for heavy Claude or GPT-4 usage run $20–100+ per month for power users. A local build amortizes those costs in months while delivering:
Privacy: Every prompt runs on your hardware. Nothing goes to Anthropic, OpenAI, or Google servers. For legal documents, private code, medical notes, or anything you wouldn't want in a training dataset, local inference is the only safe option.
Speed: A well-sized local GPU runs 7B models at 40–80 tokens/second — faster than most API responses when you factor in network latency and rate limiting.
Customization: Local models can be fine-tuned on your own data, run with custom system prompts persistently, and integrated into local workflows without API restrictions.
The trade-off: local models at 7B–13B parameters are genuinely useful but not GPT-4 class. Llama 3.1 70B and Qwen 2.5 72B are competitive with older GPT-4 versions, but running them requires 40–80GB VRAM.
The One Spec That Matters Most: VRAM
For LLM inference, VRAM is everything. A model's VRAM requirement is roughly its parameter count × bytes per parameter. A 7B model in 4-bit quantization (Q4_K_M) needs ~4–5GB. A 13B model needs ~8–9GB. A 70B model in Q4 needs ~40–45GB.
CPU inference (llama.cpp on CPU) is viable for small models on systems without a compatible GPU, but it's 5–10× slower than GPU inference. System RAM for CPU inference needs to hold the full model weight — a 70B Q4 model needs 45GB+ of RAM to run on CPU.
Budget AI Build (~$900) — Run 7B–13B Models Locally
The RX 9070 XT with 16GB GDDR6 is the budget build's anchor. It runs every 13B model comfortably and handles some 20B models in aggressive quantization. ROCm support on Linux (and increasingly on Windows via the HIP SDK) has improved significantly — Ollama and LM Studio both support AMD GPUs natively in 2025.
What you can run on 16GB: Llama 3.1 8B (excellent quality), Mistral Nemo 12B, Phi-3 Medium 14B, and Code Llama 13B for coding assistance. These are genuinely capable models for most day-to-day AI tasks.
Why 4TB HDD: Model files are large. Llama 3.1 70B Q4 is ~42GB. If you're experimenting with multiple models, storage fills up fast. Keep active models on the NVMe, archive on the HDD.
Mid-Range AI Build (~$1,800) — 70B Models at Usable Speed
Two RX 9070 XT cards in a single system gives you 32GB combined VRAM, which runs 34B models comfortably and 70B models in aggressive quantization (Q2_K, with some quality loss). For 70B at Q4 quality, you're short by ~10GB — you'd need a third card or an upgrade to cards with more VRAM.
The more practical use case for 32GB: running 34B models (Qwen 2.5 34B, Yi-34B) at Q5/Q6 quantization, which delivers near-full-precision quality. These models are competitive with older GPT-4 class benchmarks (Hugging Face Open LLM Leaderboard, 2025).
Important note on multi-GPU AMD: ROCm multi-GPU inference is functional but less mature than Nvidia's NVLink/SLI stack. Performance scaling between two AMD consumer GPUs via PCIe is typically 1.6–1.8× (not 2×) due to PCIe bandwidth constraints. For serious multi-GPU LLM work, Nvidia's ecosystem is more tested.
High-End AI Build ($3,500+) — Dual GPU for 100B+ Models
At this tier, you're running Mixtral 8×22B (141B MoE architecture, ~90GB for full precision, ~47GB Q4), Llama 3 70B at full Q5 quality, and emerging 100B+ dense models at aggressive quantization.
Two RTX 5080 cards at $900 each get you 32GB VRAM total via NVLink (if both cards support it) or PCIe for tensor parallel inference. Nvidia's NVLink 4.0 bandwidth (900 GB/s bidirectional) dramatically outperforms PCIe for multi-GPU inference — this matters at 70B+ model sizes where inter-GPU communication is a bottleneck.
For most users, this build is overkill. The sweet spot for local AI in 2026 is the 13B–34B model tier, which runs on 16–32GB VRAM. The 100B+ tier is for researchers, commercial fine-tuning workflows, or people who simply want the best.
CPU, RAM, and Storage — What Actually Matters for LLM Inference
Once your model fits in GPU VRAM, CPU and RAM contribute minimally to tokens-per-second. They matter in these specific scenarios:
CPU matters for: Initial model loading (decompressing quantized weights), tokenization speed, and CPU-offloaded inference (when the model doesn't fully fit in VRAM and some layers run on CPU).
RAM matters for: Storing the operating system, model management software, and any CPU-offloaded layers. 32GB is fine for pure GPU inference. 64GB+ makes sense if you're running large models with partial CPU offload.
Storage speed matters: NVMe load times for a 13B model are 5–8 seconds vs 30–45 seconds on a SATA SSD. For frequent model switching, NVMe is worth it. For a single always-loaded model, even a SATA SSD is fine.
Software Setup: Ollama, LM Studio, and vLLM
Ollama is the easiest starting point. Install it, run ollama pull llama3.1, and you have a local model accessible via API at localhost:11434. It handles AMD and Nvidia GPUs automatically on Linux; Windows support has improved but occasionally requires ROCm tweaks for AMD.
LM Studio is the GUI option — excellent for testing models, adjusting context window size, and running models without touching the command line. It supports GGUF model format (the standard for quantized local models from Hugging Face). Works on Windows, Mac, and Linux.
vLLM is the production-grade option. If you're serving a model to multiple users or running a local API endpoint at scale, vLLM handles batching, continuous batching, and throughput optimization. It's primarily Linux/Nvidia-focused, though AMD ROCm support has improved.
For most users: start with Ollama or LM Studio. Graduate to vLLM if you need to serve requests to multiple applications or optimize throughput.
Frequently Asked Questions
Can I run local AI on a gaming PC I already own?
Yes, if it has enough VRAM. An RTX 4070 (12GB) runs 13B models at decent speed. An RTX 3080 (10GB) handles 7B models. The GPU you already have determines your starting point — download Ollama and try it before buying anything.
Is AMD or Nvidia better for local AI?
Nvidia wins for software ecosystem maturity. CUDA acceleration works with every LLM framework, NVLink enables efficient multi-GPU inference, and driver stability is excellent. AMD's ROCm is functional and improving but requires more troubleshooting, especially on Windows. For a dedicated AI build, Nvidia is the lower-friction choice. For a dual-purpose gaming + AI build, either works.
What's the best model to start with locally?
Llama 3.1 8B (Meta's open model) is the standard recommendation: excellent instruction-following, 128K context window, runs on 8GB+ VRAM at Q4. For coding specifically, Qwen 2.5 Coder 7B or Code Llama 13B are strong. Try a few models through LM Studio before committing to one workflow.
How does local AI privacy actually work?
Your prompts and the model weights stay on your machine. No network traffic to AI companies occurs during inference (only during model downloads). For complete airgap privacy, download models on a connected machine and transfer to an offline one — Ollama supports this workflow.
Ready to build a local AI workstation? Start your build on PlanMyPC to configure components with live pricing and automatic compatibility checks.