Can I Run OpenAI gpt-oss 20B on RTX 5060 Ti 16GB? VRAM & Speed Analysis
Model weights, initial KV-cache, and CUDA context buffers.
Available safety margin for OS compositing and context expansion.
Throughput determined by memory bus bandwidth.
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.
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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Frequently Asked Questions
Why does gpt-oss 20B fail on an RTX 5070 12GB if it is a faster card?
The RTX 5070 has superior raw compute but is strictly limited to 12GB VRAM. Because gpt-oss 20B requires 14.01 GB, the model spills 2.01 GB into system RAM over PCIe, destroying throughput from 85 tok/s down to 4 tok/s.
Can the RTX 5060 Ti 16GB handle 32k context on gpt-oss 20B?
Yes! With FP8 KV-cache enabled, 32k context on gpt-oss 20B consumes only 1.25 GB, keeping total allocation under 15.2 GB on the 16GB buffer.
Calculate Exact Context Window Headroom
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