How much VRAM does gpt-oss 20B need at Q4_K_M? OpenAI's first open-weight model since GPT-2
gpt-oss 20B at Q4_K_M is the model to download if you've never run a local LLM before in 2026. Apache 2.0, runs on anything, decent reasoning for the size, and OpenAI lineage. Not as strong as Qwen3 30B-A3B at the same memory footprint, but the brand recognition matters and the licensing is the cleanest in the open-weights ecosystem.
gpt-oss 20B at Q4_K_M with native 128K context needs about 19.4 GB of VRAM with all experts resident. It's an MoE with 3.6B active parameters per token, 24 layers, an unusual hidden size of 2944, head_dim 64, and 128K native context. The 20B variant of OpenAI's first Apache 2.0 release is the smallest model in the family and is intentionally sized for laptops and consumer GPUs.
By TechCompare · Updated
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Estimated VRAM required
19.4 GB
20B params at Q4_K_M, 131,072 token context, batch 1, inference.
Estimate accuracy: Weights within ~2%. KV cache within ~5% for standard GQA models, ~10% for MLA or hybrid state estimates. Real VRAM may vary with framework (vLLM vs llama.cpp vs Transformers), Flash Attention, and driver overhead.
Hardware that fits
Apple entries show installed unified memory separately from a model budget based on an 8 GB reserve. That budget is a planning heuristic, not a fixed hardware limit.
How this is calculated
11.2 GB of weights at Q4_K_M plus a 6.4 GB KV cache (at the full 128K context window) and ~1.8 GB overhead, for about 19.4 GB total.
Verdict
The total stacks to 19.4 GB resident at the full 128K context: 11.2 GB of Q4 weights plus 6.4 GB of FP16 KV cache and 1.8 GB of activation overhead. A 24 GB consumer card (RTX 3090/4090/5090) handles resident mode comfortably. Smaller cards can move expert weights to system RAM, but the host then has to hold them and every token gets slower. Apache 2.0 plus OpenAI lineage is the differentiator against Qwen3 30B-A3B at the same footprint, which is otherwise the stronger-raw-quality alternative.
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Frequently asked questions
What does '3.6B active' actually mean for inference speed?
Should I pick gpt-oss 20B or Qwen3 30B-A3B?
What GPU do I need to run gpt-oss 20B all-resident?
Does the 3.6B active count lower the VRAM math?
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