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Run OpenAI's open-source models locally
Quick Answer: For most users, the RTX 4090 ($1,500-$1,800) offers the best balance of VRAM, speed, and value. Budget builders should consider the RTX 4070 Ti Super ($700-$850), while professionals should look at the 2x RTX 3090 (Multi-GPU).
Methodology and data
Rankings use measured compatibility, VRAM constraints, and benchmark-backed tradeoffs. See assumptions and formulas in methodology.
OpenAI's GPT-OSS brings GPT-class quality to local inference. The 20B variant is practical for consumer hardware, while the 120B model targets workstation and multi-GPU setups. VRAM and quantization choice determine your experience.
Compare all recommendations at a glance.
| GPU | VRAM | Price | Best For | |
|---|---|---|---|---|
RTX 4070 Ti SuperBudget Pick | 16GB | $700-$850 | GPT-OSS 20B at Q4, Fast inference on the smaller variant | Buy |
RTX 4090Editor's Choice | 24GB | $1,500-$1,800 | GPT-OSS 20B at Q8/Q6_K, High-quality 20B inference | Buy |
2x RTX 3090 (Multi-GPU)Performance King | 48GB | $1,200-$1,600 (used) | GPT-OSS 120B at Q2/Q3, GPT-OSS 20B at FP16 | Buy |
Detailed breakdown of each GPU option with pros and limitations.
16GB handles GPT-OSS 20B at Q4 with decent headroom. Strong Ada Lovelace performance for the price.
Best For
Limitations
24GB runs GPT-OSS 20B at Q8 comfortably. The fastest consumer option with headroom for higher-quality quantization.
Best For
Limitations
48GB across two GPUs opens up GPT-OSS 120B at aggressive quantization. Best value path to the larger model.
Best For
Limitations