OpenAI gpt-oss 20B (MoE) VRAM Requirements Guide
OpenAI's Apache-2.0 open-weight reasoning MoE (20.9B total, 3.6B active) with alternating 128-token sliding + full attention. Engineered for 16GB consumer GPUs.
>98.2% retention. 12.3 GB weights, leaving +1.6 GB headroom on NVIDIA GeForce RTX 5060 Ti 16GB.
Near-lossless 8-bit precision requiring 25.0 GB VRAM for benchmark evaluations.
Uncompressed FP16 weights taking 44.7 GB. Demands multi-GPU or datacenter accelerators.
Architectural Memory Breakdown & Attention Scaling
OpenAI's gpt-oss 20B is an open-weights Mixture-of-Experts reasoning model containing 20.9B total parameters while activating just 3.6B parameters per token across 32 layers. Using alternating sliding window attention and native MXFP4 quantization, its 4-bit weight footprint consumes 12.8 GB, fitting comfortably within 16GB graphics cards for rapid local inference.
In llama.cpp and vLLM, launch with native `--quantization mxfp4` kernels and pass `-fa on`. MXFP4 maintains near-lossless reasoning fidelity while cutting memory bandwidth overhead, streaming tokens at 85+ tok/s.
NVIDIA GeForce RTX 5060 Ti 16GB provides 16.0 GB physical VRAM (+1.6 GB operational margin at 8k context).
Run OpenAI gpt-oss 20B (MoE) Locally
ollama run gpt-oss-20b:q4_K_Mvllm serve OpenAI/gpt-oss-20b --max-model-len 8192 --gpu-memory-utilization 0.95Frequently Asked Questions
Can gpt-oss 20B run on a 16GB GPU like the RTX 4060 Ti 16GB?
Yes! At 4-bit precision, total memory footprint is ~14.4 GB at 8k context, fitting inside 16GB graphics cards with ample safety headroom.
How does gpt-oss 20B compare to gpt-oss 120B?
The 20B model runs easily on single 16GB consumer GPUs, while the 120B model requires 80GB–128GB unified memory systems like AMD Strix Halo or DGX Spark.
Test Custom Hardware & Context Windows
Simulate OpenAI gpt-oss 20B (MoE) across all 31 GPUs, adjust context up to 131,072 tokens, or calculate RunPod break-even costs.
Open OpenAI gpt-oss 20B (MoE) in GPU Garden Matchmaker →