# Can I Run OpenAI gpt-oss 20B on RTX 5060 Ti 16GB? VRAM & Speed Analysis

> **Direct Compatibility Verdict**: Yes, OpenAI gpt-oss 20B runs with outstanding stability on the 16GB RTX 5060 Ti. At Q4_K_M (or native MXFP4), weights require 11.76 GB and 8k context requires 0.75 GB KV-cache, creating a total footprint of 14.01 GB (leaving +1.99 GB headroom). Activating only 3.6B parameters per token achieves 85+ tok/s, whereas 12GB cards like the RTX 5070 suffer catastrophic OOM crashes.

## Key Memory Physics
| Metric | Value |
|---|---|
| Target AI Model | OpenAI gpt-oss 20B (MoE) |
| Target Hardware | NVIDIA GeForce RTX 5060 Ti (16GB) |
| Q4_K_M VRAM Required | 14.01 GB |
| Available Headroom | +1.99 GB |
| Estimated Generation Speed | ~85 tok/s |

## Hardware Physics & Bottleneck Analysis
OpenAI gpt-oss 20B utilizes 24 transformer layers with 12 sliding layers (128-token window) and 12 global attention layers. Because it only activates 3.6B parameters per token from its 20.9B parameter pool, decoding is exceptionally fast. The RTX 5060 Ti's 28 Gbps GDDR7 memory bus (448 GB/s) streams active weights with zero pipeline stalls.

## Tier 1 Free Software Optimization ($0)
When running on Windows 11, configure `-ngl 24` and `-fa on` in llama.cpp to ensure all 24 layers remain in dedicated GDDR7 memory. If Windows DWM consumes >1.2 GB, use `IQ4_XS` quantization to drop weight footprint to 10.9 GB.

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*Interactive Sizing Calculator available at: https://www.gpugarden.com/can-i-run/gpt-oss-20b-on-rtx-5060-ti-16gb/*