Try Open-Weight AI on Windows: Duck.ai Brings gpt-oss 120B Privately

DuckDuckGo’s Duck.ai giving users a free, anonymous window onto large open-weight models is a small but significant step in the evolving landscape of accessible generative AI — and it’s turned the question of “how to try big models without a GPU farm” into a practical reality for many Windows users. The headline claim — that Duck.ai now offers access to OpenAI’s gpt-oss:120b so anyone can try a 120‑billion‑parameter open model without Ollama or LMStudio — is compelling, but it’s also one that requires careful unpacking: the OpenAI gpt‑oss models are real and powerful, Duck.ai is a genuine privacy-minded gateway to third‑party models, and some of the functionality Windows Central’s reviewer described matches what many users will see — though the specific availability of gpt‑oss:120b on Duck.ai is not fully documented on DuckDuckGo’s public model lists at the time of reporting and should be treated with caution. (openai.com) (duckduckgo.com)

---

Background / overview​

OpenAI’s summer release of the gpt‑oss family — notably gpt‑oss‑120b and gpt‑oss‑20b — represents a strategic pivot toward open‑weight models that organizations and developers can download, inspect, modify, and run locally or via third‑party hosting. OpenAI’s official documentation lays out the architecture, licensing (Apache 2.0), and deployment guidance for both models, describing the 120‑b model as a large, sparse Mixture‑of‑Experts (MoE) system that activates a fraction of its parameters per token for efficiency. (openai.com)
Meanwhile, DuckDuckGo’s Duck.ai (often accessed at duck.ai or through DuckDuckGo’s browser/search interface) provides a privacy‑first chat gateway. Duck.ai anonymizes traffic between users and model providers, stores local copies of recent chats on-device (optional), and negotiates retention and training restrictions with partner providers so that conversational data is not used to train downstream models. That privacy posture is central to Duck.ai’s value proposition. (duckduckgo.com)
What’s new — and what’s being reported in outlets such as Windows Central — is the usability payoff: instead of wrestling with quantized weights, Triton kernels, or multi‑GPU device mapping, a user can open a web page, switch to a large‑model provider within Duck.ai’s model selector, and start interacting with a capable open‑weight LLM. The convenience is real, and the privacy promises are meaningful; the devil, as always, is in the implementation details. (duckduckgo.com)

---

What gpt‑oss actually is — technical snapshot​

OpenAI has publicly documented the gpt‑oss family with concrete technical claims that matter for Windows users and hobbyists who care about local inference tradeoffs:

• Two models: gpt‑oss‑120b (the large MoE model) and gpt‑oss‑20b (the smaller, edge‑targeted model). Both are released under Apache 2.0 licensing. (openai.com)
• Architecture: The 120b model uses a Mixture‑of‑Experts architecture to keep the active compute per token much smaller than the total parameter count, enabling more efficient inference at scale. OpenAI’s materials report ~117B total parameters with ~5.1B active parameters per token for the 120b variant. The 20b model is similarly efficient and tuned for edge use. (openai.com)
• Quantization and memory: OpenAI and downstream guides point to MXFP4 / 4‑bit quantization and memory/format optimizations that make the 120b model feasible on single data‑center GPUs or high‑end consumer cards when appropriate quantized runtimes and kernels are available. Practical VRAM guidance varies by backend and configuration, but authoritative docs and community references suggest ~60–80 GB VRAM as a realistic target for the 120b configuration, while the 20b model can be run on high‑end consumer GPUs with ~16 GB VRAM when using MXFP4. (openai.com)
• Context length and reasoning: The models support very long context windows (OpenAI documents cite support out to 128k tokens) and are explicitly designed to support chain‑of‑thought reasoning and tool use modes. (openai.com)

Why it matters for Windows readers: gpt‑oss‑20b is now realistically within reach of a single modern gaming/creator GPU with 16GB, while gpt‑oss‑120b remains a bigger ask — either a single 60–80GB class professional GPU or a multi‑GPU setup, or else hosted inference provided by a third party. If you want to run the full 120b model locally at reasonable speed, expect to need workstation‑class GPUs or to rely on hosted runners that make the same model available over the web. (huggingface.co)

---

Duck.ai: what it offers, and where the privacy advantage comes from​

Duck.ai’s pitch is straightforward: offer access to a menu of third‑party chat models while minimizing the personally identifiable signals those providers can use. That’s achieved by:

• Routing model calls through DuckDuckGo’s anonymizing proxy so providers do not receive device‑level IDs or persistent user identifiers.
• Negotiating retention limits and contractual terms with providers so chat data is not used for ongoing model training, and trimming metadata to reduce linkability.
• Providing a local “Recent Chats” sidebar saved on the user device (optional), with a quick “Fire Button” to delete local and transient entries. (duckduckgo.com)

In practice, Duck.ai currently supports (publicly documented) models like Anthropic’s Claude, Meta’s Llama variants, Mistral and OpenAI’s lightweight options (e.g., GPT‑4o Mini) — and offers an optional paid tier that gives subscribers access to newer, more capable models. The company emphasizes no account required access for the free tier and advertises anonymized, ephemeral handling for privacy‑minded users. (duckduckgo.com)
Why that matters: for Windows users who want to experiment with large LLMs but do not want their queries fed into vendor training pipelines or stored by default, Duck.ai removes a lot of friction. Instead of installing Ollama, LMStudio, or managing Hugging Face CLI downloads and Triton kernels, you get a browser‑based experience with model selection, recent chat saving, and settings to tune response style — a familiar ChatGPT‑like UX without the account and with a privacy frame. (duckduckgo.com)

---

The Windows Central report: what was tested and the notable takeaways​

Windows Central’s review (the piece you shared) reports that the site’s author tried gpt‑oss:120b via Duck.ai, and highlights three practical user‑experience points:

• Performance & responsiveness: Duck.ai felt fast, comparable to local 20b performance on the reviewer’s high‑end RTX 5090 setup, because inference was running on DuckDuckGo’s hosted infrastructure rather than on the reviewer’s machine.
• No chain‑of‑thought visibility: When running gpt‑oss locally through tools like Ollama or LMStudio, many hobbyists and researchers value the ability to see the model’s chain‑of‑thought or “thought traces.” Duck.ai’s hosted interface returns only the final answer and does not expose the internal analysis stream — an intentional UX choice that some users will miss.
• No file uploads / limited agent I/O: At the time of the review, Duck.ai did not accept arbitrary file uploads or documents to augment a chat session, which limits workflows that rely on feeding private documents into the model via the chat UI. That’s consistent with Duck.ai’s conservative, privacy‑focused posture.

These practical observations match the expected tradeoffs of a hosted privacy gateway: convenience and anonymity in exchange for reduced exposure to internals and fewer integration hooks compared with a full local deployment. The reviewer’s surprise — and delight — at being able to try a 120b‑class model without a personal GPU farm is a useful illustration of the new access model for Windows users.

---

Verifying the key claims — what is confirmed, and what remains uncertain​

It’s essential to separate three things:

• The OpenAI gpt‑oss release and its technical specs are confirmed in OpenAI’s published materials, the Hugging Face model pages, and multiple reputable tech outlets. The model’s architecture (MoE), parameter counts, quantization options, and rough VRAM guidance are well documented. (openai.com)
• Duck.ai’s privacy model and supported third‑party chat models are documented on DuckDuckGo’s help pages and corroborated by TechCrunch, The Verge, and national press coverage. That includes the privacy claims (anonymization, no training on user chats, local recent chat storage) and the current roster of supported models such as Claude, Llama, Mistral, and OpenAI’s lighter variants. (duckduckgo.com)
• The specific claim that Duck.ai is serving OpenAI’s gpt‑oss:120b is plausible (Duck.ai is a third‑party gateway that can route to partner inference endpoints), but it is not explicitly confirmed on Duck.ai’s published model roster at the time of this reporting. Duck.ai’s public model list names several mainstream models but does not show gpt‑oss in the official “What AI chat models are available?” help page captured in searches. That means Windows Central’s account may reflect a live or experimental rollout not yet reflected in DuckDuckGo’s static documentation, or it may be a mis‑reading. Until Duck.ai explicitly lists gpt‑oss or OpenAI and DuckDuckGo publish a joint note, that particular availability claim should be treated as provisionally true but unverified. (duckduckgo.com)

Practical advice: if you want to confirm whether Duck.ai is currently hosting gpt‑oss:120b for public access, check Duck.ai’s in‑app model selector (no account required) or the DuckDuckGo help pages, and note any model names in the left sidebar. If gpt‑oss appears, you’ll be able to test it immediately; if not, Duck.ai may be offering other up‑to‑date OpenAI options (GPT‑4o mini, etc.) or it may have a subscriber tier that unlocks additional models. (duckduckgo.com)

---

Strengths and potential risks — critical analysis​

Strengths​

• Instant access with low friction. Duck.ai lowers the barrier to trying large models: no local setup, no large downloads, and no GPU wrangling. For Windows users who want to experiment, that convenience is compelling.
• Privacy‑forward design. DuckDuckGo’s anonymization and retention limits are meaningful differentiators in an ecosystem where many model providers use conversational data for retraining. For privacy‑sensitive tasks and exploratory use, that’s valuable. (duckduckgo.com)
• Access to open‑weight models without heavy hardware. The arrival of gpt‑oss makes powerful LLMs more available; platforms that host them let users evaluate capabilities without investing in workstation gear. OpenAI and ecosystem partners also provide optimized runtimes and clear VRAM guidance, so users can plan deployments. (openai.com)

Risks and limitations​

• Availability ambiguity. Hosted gateways can add or remove models rapidly. The public help pages do not always reflect real‑time changes, so claims about which exact model is served (for example, gpt‑oss:120b) require verification at the moment of access. Treat platform model lists as the authoritative source. (duckduckgo.com)
• Opaque hosted execution. Duck.ai’s UX hides the model internals (e.g., chain‑of‑thought traces) — which is fine for casual use, but problematic for debugging, auditing, fine‑tuning insight, or detailed analysis where seeing intermediate reasoning is important. Local deployments (Ollama, LMStudio) still win for transparency.
• Limited integration hooks. Duck.ai’s current interface favors conversational interaction and may not accept arbitrary file/document uploads or advanced agent tooling that developers may want to chain into a model for custom workflows. That reduces utility for some developer or enterprise use cases unless Duck.ai introduces new upload APIs or integrated tooling.
• Third‑party trust and contractual constraints. Duck.ai’s privacy protections rely on contractual terms with model providers and proper implementation. While the company’s stated policies are strong, legal and technical enforcement matters; users handling regulated data should prefer on‑device or private‑cloud deployments under direct control. (duckduckgo.com)

---

How Windows users should think about trying gpt‑oss models today​

• If you want full transparency and the ability to see chain‑of‑thought: run gpt‑oss locally with Ollama, LMStudio, or Foundry Local on Windows AI Foundry (if you have the hardware). OpenAI’s repo, Hugging Face pages, and the LM Studio / Ollama docs include the commands and steps to download and run the models. Expect to install Triton kernels or platform‑specific runtimes for best performance. (huggingface.co)
• If you want to try a 120b‑class model with zero hardware: use a hosted gateway (Duck.ai, Vercel’s AI Gateway, cloud provider managed inference). Hosted runners give you immediate access but vary in model visibility and integration features; verify the exact model name in the UI. Consider whether you need file uploads, tool calling, or chain‑of‑thought traces before choosing a hosted route. (vercel.com)
• If data privacy is paramount: prefer local deployment or carefully read Duck.ai / provider privacy pages to ensure contractual deletion and non‑training promises meet your compliance needs. For regulated workloads, a private cloud instance or on‑premise Foundry Local deployment is still the gold standard. (azure.microsoft.com)
• If you’re experiment‑minded and low on hardware: try gpt‑oss‑20b locally (if you have a 16GB+ GPU) or via Duck.ai’s lighter OpenAI options to get a feel for the model family. Deploying 20b locally lets you test chain‑of‑thought modes while keeping costs and setup complexity reasonable. (huggingface.co)

---

Practical checklist for Windows readers before jumping in​

• Confirm the model name in the Duck.ai model selector and whether it’s on the free or subscriber tier. (duckduckgo.com)
• If using Duck.ai for sensitive prompts, verify the current retention policy and whether chats are ever stored on provider backends for service quality reasons. (Duck.ai’s public help pages and recent reporting indicate retention windows and anonymization, but contractual details can change.) (duckduckgo.com)
• For local runs, check VRAM requirements and whether your GPU supports MXFP4/quantized runtime kernels (modern RTX 50xx and H100/GB200 cards are referenced by OpenAI guides). If you have <16 GB VRAM, expect to use smaller models or cloud inference. (huggingface.co)
• If you need chain‑of‑thought traces, verification, or custom tool integration, prioritize a local or private‑cloud deployment rather than a web gateway.

---

The larger context: what this means for Windows and the LLM ecosystem​

The arrival of OpenAI’s gpt‑oss models combined with gateways like Duck.ai marks a practical democratization step:

• It accelerates the “try before you buy” moment for hobbyists, enterprises, and educators. Users who lack hardware can still meaningfully assess a model’s capabilities. (wired.com)
• It increases competition among inference providers and tool vendors (Vercel, Hugging Face, Azure, LM Studio, Ollama, and more), which should drive down the friction and cost of running large models. (cloud.google.com)
• It makes privacy design a headline differentiator: services that can combine access to leading models with non‑tracking, contractually enforced deletion and minimal metadata exposure will attract users who are otherwise wary of cloud AI. Duck.ai is positioned to play that role, even if its exact model lineup evolves over time. (duckduckgo.com)

At the same time, the landscape will bifurcate: hosted convenience vs local control. Windows users will need to choose where they stand on that continuum based on technical needs and privacy posture.

---

Conclusion​

The Windows Central write‑up you shared captures an important shift: you no longer always need a GPU farm or complicated local stacks to try near‑top‑tier open models. Hosted gateways like Duck.ai make that trial run quick and private in many cases, and OpenAI’s gpt‑oss family provides the open‑weight muscle behind the headlines. That’s a win for accessibility and experimentation on Windows.
However, the exact claim that Duck.ai exposes gpt‑oss:120b to all users should be treated as provisionally accurate pending direct verification from Duck.ai’s live interface or an official statement. Duck.ai’s privacy features and the arrival of gpt‑oss are both validated independently in OpenAI and platform documentation, but live model rosters can change quickly — check the Duck.ai model selector or the DuckDuckGo help pages for the current list before assuming a particular model is available. (openai.com)
For Windows enthusiasts, the near‑term playbook is clear: experiment via hosted gateways if you want convenience and anonymity; go local with LMStudio, Ollama, or Windows AI Foundry when you need transparency, tool integration, or absolute control over data and reasoning traces. The era of “big models only in the cloud” is over; the era of “big models for every Windows user” is beginning — with important tradeoffs to weigh before you choose your path.

---

Source: Windows Central You can now try OpenAI's gpt-oss:120b for free and privately without using Ollama or LMStudio