| GPU | LLM Score | Price | VRAM | Est. tokens/sec | TDP | Model fit | Compare |
|---|
Baseline AI PC build
Local LLMs are GPU-first, but the rest of the PC still matters. A sensible starter build for one GPU is:
- CPU: modern 6-core or better Ryzen 5 / Core i5
- RAM: 32GB minimum, 64GB preferred for 30B+ models or multitasking
- Storage: 1TB NVMe so model downloads do not crowd Windows
- PSU: 650W for midrange GPUs, 850W+ for RTX 3090/4090 class cards
- Cooling: airflow case, especially for used 300W+ GPUs
How the score works
The StashGrid LLM score is intentionally different from a gaming benchmark. It weights VRAM/model fit first, then speed per dollar, power efficiency, and setup friendliness. A cheap 16GB card can beat a faster 12GB card when the model you want will not comfortably fit.
Formula: 45% model fit, 30% speed per dollar, 15% watts, 10% software setup. It is a buying guide, not a lab benchmark.
Affiliate opportunities to set up
This page has strong buying intent. Start with broad programs that cover PC parts and prebuilt systems, then add direct retailer links only after approval. Keep affiliate links in this recommendation section and disclose them clearly.
Should local LLM hardware be its own page?
Yes. PC parts value and local LLM value are related, but the search intent is different. PC parts shoppers ask for benchmark-per-dollar. Local AI shoppers ask whether a model will run, how much VRAM they need, which GPU gives the most tokens per second per dollar, and whether NVIDIA is worth the premium. A dedicated page can target searches like best GPU for local LLM, AI PC build, Ollama GPU requirements, and local AI hardware.
The main pitfall is maintenance. Model sizes, quantization formats, GPU prices, and software support change quickly. For trust, every recommendation should show a last-updated date and link to primary project docs or benchmark sources.
Quick recommendations
Best low-risk starter: RTX 4060 Ti 16GB if buying new and power matters. Best used value: RTX 3090 24GB if you accept used-card risk, heat, and power draw. Best no-compromise consumer card: RTX 4090 or newer 32GB-class cards when budget is secondary. Best AMD caveat: RX 7900 XTX has excellent VRAM per dollar, but local AI setup can require more checking than CUDA-based NVIDIA paths.
Sources and trust notes
- Ollama model library for local model families and model size context.
- llama.cpp for local inference backend support including CUDA, Metal, Vulkan, and CPU paths.
- LM Studio docs for consumer local LLM workflow context.
- Compute Market LLM inference benchmark roundup and GPU Hunter LLM GPU guide for public speed/value reference points. Treat tokens/sec as estimates, not guarantees.
- Retail and affiliate program pages linked above for monetization setup; exact commissions and eligibility can change.
FAQ
Is VRAM more important than raw GPU speed?
For local LLMs, yes in many cases. If the model does not fit in VRAM, performance drops sharply or the model will not run comfortably. Once it fits, speed and memory bandwidth matter more.
Can two GPUs combine VRAM?
Some tools and backends can split a model across multiple GPUs, but it is more complex than a single large-VRAM card. For most buyers, one 24GB card is easier than two smaller cards.
Should I buy a prebuilt AI PC?
Prebuilt systems can be a good affiliate path if they include a real GPU, adequate PSU, 32GB to 64GB RAM, and clear cooling. Avoid vague listings that say "AI ready" without VRAM, PSU, and GPU model details.