GPT-OSS:20b is a 20-billion-parameter large language model released by OpenAI in 2024 under the Apache 2.0 open-source license. Unlike the closed GPT-4o or o1 series, GPT-OSS:20b is fully open-weight: the model weights are publicly downloadable, the architecture is documented, and anyone can run it on their own hardware or on a managed hosting service.

The "20b" in the name refers to the 20 billion parameters that define the model's learned knowledge and reasoning ability. This size class — sometimes called "mid-size frontier" — delivers strong benchmark performance on reasoning, code, and multilingual tasks while remaining practical to serve on a single modern GPU (24 GB VRAM minimum for FP16 inference).

Apache 2.0 licensing means you can use GPT-OSS:20b commercially, modify it, and redistribute it — all without paying per-token royalties to OpenAI. This is the foundation of OpenClaw Hosted's pricing model: one flat fee covers unlimited inference because there is no third-party per-token cost to pass on.

Closed-source models like GPT-4, Claude 3, and Gemini 1.5 Pro share three properties: you cannot inspect their weights, you cannot audit their training data, and you pay per token — every prompt and every response costs money. For businesses with high message volumes this adds up fast: a customer support agent handling 10,000 conversations per day at 500 tokens average can cost $500–$2,000/day on closed-source APIs.

GPT-OSS:20b eliminates per-token billing entirely. Because the weights run on infrastructure you control (or that OpenClaw Hosted controls on your behalf), the marginal cost of each additional token approaches zero. You pay for GPU time, not for tokens.

Data privacy is the second advantage. Closed-source API providers retain the right to use your queries for safety monitoring and, in some cases, model improvement. With GPT-OSS:20b running in your dedicated container, your conversations never leave the server they were processed on. There is no external API call, no OpenAI telemetry, no training feedback loop involving your data.

Running GPT-OSS:20b yourself requires: provisioning a server with at least 24 GB VRAM (typically $50–200/month), downloading 40+ GB of model weights, installing and configuring an inference runtime (Ollama, vLLM, or llama.cpp), exposing a compatible API endpoint, and keeping everything updated. Most businesses do not need to own that stack.

OpenClaw Hosted abstracts all of that. When you subscribe, a dedicated Docker container is provisioned on LLM Resayil's managed GPU cluster. The container runs the upstream OpenClaw AI agent software, pre-wired to a GPT-OSS:20b inference endpoint. Your container is isolated: your model calls never share GPU time with another customer's workload.

The flat $49/month price covers the container, the GPU inference, the OpenClaw software license, and LLM Resayil's operational overhead — with a 5,000,000 token per day fair-use cap (approximately 3.75 million words, which covers 99% of real business workloads). There is no per-token billing inside your container.

Self-hosting GPT-OSS:20b gives you maximum control: choose your inference runtime, tune context length, pick your quantization level, run it air-gapped. If you have a team that manages GPU infrastructure and a specific reason to own the hardware, self-hosting is a legitimate choice.

For most businesses — particularly in the GCC where GPU servers are expensive to procure locally — managed hosting is the better trade-off. OpenClaw Hosted delivers all the privacy and cost advantages of an open-weight model without the infrastructure overhead. Your DevOps team does not need to know what a CUDA driver is.