One of the biggest questions we get at GenX AI Tools is “what hardware do I need to run AI models locally?” The answer depends entirely on your budget and use case. Here’s our comprehensive guide covering everything from entry-level consumer GPUs to enterprise multi-GPU clusters.
Budget Entry ($500-$1,000)
Used RTX 3090 (24GB): Still the king of budget local LLM hardware. At around $800-1,050 used, you get 24GB of VRAM capable of running models up to ~30B parameters comfortably.
New RTX 5060 Ti (16GB): The new budget champion at ~$500. Blackwell architecture with FP4 support, handles 14B models at Q8 easily.
Mid-Range ($1,000-$3,000)
RTX 4090 (24GB): The established workhorse. Handles 70B quantized models with partial offloading. Still available new for $1,600-2,000.
AMD RX 9070 XT (16GB): At ~$500 MSRP, this is AMD’s strongest offering yet with official ROCm support from day one. Linux-only recommendation though.
High-End ($3,000-$8,000)
RTX 5090 (32GB): The current consumer ceiling at ~$3,000-5,000. Fits 34B models entirely in VRAM with room to spare.
Mac Studio M3 Ultra (192GB unified memory): Unique proposition – massive unified memory lets you run 70B models that no consumer GPU can handle. Slower than dedicated GPUs but unmatched for model size.
Enterprise ($8,000+)
RTX PRO 6000 Blackwell (96GB): ~$8,565. Handles 70B models at full precision without any offloading.
H100/H200 SXM: The production standard for teams serving multiple users simultaneously.
Our Recommendation?
For most people getting started with local AI, a used RTX 3090 or new RTX 5060 Ti offers the best balance of cost and capability. You can run Qwen3 models up to 14B parameters comfortably, which covers most use cases.
