As of October 7, 2026, Liquid AI has not documented a way to download or run its newly announced d1 model locally. The October 5 announcement describes d1 as available through Liquid AI’s API, with text access also offered through Vercel and OpenRouter. It gives no d1-specific RAM, VRAM, storage, runtime, or supported-device requirements, so there is no evidence-based laptop, desktop, or phone configuration to recommend for local d1 inference. Liquid AI’s d1 announcement
Is d1 a download or an API?
The d1 described in Liquid AI’s October 5, 2026 announcement is API-served. The post says developers can access it through Liquid AI’s API; it also names Vercel and OpenRouter as text-access options at launch, with vision support on those services to follow. Those are routes to a hosted service, not evidence of a local model package. Liquid AI’s announcement
The announcement says Liquid AI plans to release open weights for upcoming models, but it gives no release date and does not state that a particular d1 version will be included. That prospective statement is not proof that d1 weights are currently public.
What does d1 do?
Liquid AI describes d1 as a decision model: it takes unstructured text, images, or both, along with one or more questions, and returns answer probabilities in a single forward pass without generating tokens. The answer formats described include yes/no (“Noul”), choosing among labels, and scoring along a scale. A request can ask several questions about the same input. Liquid AI’s d1 announcement
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That API design can suit classification or scoring tasks, but it does not change the local-running question: the announcement does not provide d1 weights or local deployment specifications.
What consumer hardware does d1 require?
No specific consumer hardware requirement for running d1 locally is established in the announcement. It does not state a minimum RAM or VRAM amount, storage footprint, supported operating system, local runtime, or list of compatible laptops, desktops, phones, CPUs, GPUs, or NPUs. Without those details—and a documented local package—there is no sound basis for claiming a particular device can run d1.
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Liquid AI’s broader statements about its Liquid Foundation Models (LFMs) are separate. The company says LFMs are designed for CPU, GPU, and NPU deployment, including phones and laptops; its general portfolio materials describe variants from hundreds of millions to a few billion parameters, with some under 1 GB. These are family-level claims, not d1 specifications. Liquid AI’s LFM catalog Liquid AI’s pricing page
What are the trade-offs between d1 API access and a local LFM?
| Consideration | d1 through an API | A separate local LFM |
|---|---|---|
| Access and weights | API access is described in the October 5 announcement; a downloadable d1 model is not documented. Liquid AI announcement | Liquid AI describes open-weight availability and local deployment for the broader LFM portfolio; check the specific model’s availability. LFM catalog Pricing FAQ |
| Device and runtime fit | Local requirements and compatible devices are not stated. | The company describes CPU, GPU, and NPU deployment generally, but the exact model’s runtime and actual memory footprint need to be checked individually. LFM catalog Pricing |
| Images | The announcement describes image input through the API, including a base64 data URL example. Liquid AI announcement | Image capability depends on the particular LFM; the general deployment claims do not establish d1-equivalent image support. |
| Cost and resources | Liquid AI describes input-token billing, with no output-token charge; each question is billed as its own prompt. Liquid AI announcement | Local execution uses the chosen device’s resources; the materials cited here do not establish a specific model’s resource use or cost for a given workload. |
| Offline use and control | The announcement establishes API access, not offline or on-device operation. | Local execution can be relevant where offline availability or local data handling matters, but verify that the exact model and runtime meet the need. Pricing FAQ |
Choose d1 API access if you want to use the announced decision model and can send requests to the service. If local execution is essential, select a downloadable LFM and verify that particular model’s bundle, runtime, quantization, device compatibility, and task fit. A local LFM is not automatically a d1 substitute for every decision-model task.
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How does d1 API billing handle image inputs?
For d1 API requests, Liquid AI says billing is based on input tokens, with no output-token charge. It counts images at 1.5 tokens per 32×32-pixel patch and gives a 1024×1024 image as an example costing 1,536 input tokens. Each question is billed as its own prompt, including its text and all images. These figures describe the API billing model, not the resource cost of local inference. Liquid AI’s d1 announcement
Independent reader supportYour contribution helps us test, update, and keep practical guides available for everyone.How should you interpret the announced speed?
Liquid AI reports 200–300 milliseconds for a text decision. That is a vendor-reported figure, not a consumer-device test result and not evidence that d1 runs locally. The same announcement describes company-run comparisons across six applications, with each application run once per model on October 5, 2026. Those results should be read as the company’s reported measurements, not independent hardware benchmarks. Liquid AI’s d1 announcement
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Can you use d1 privately or offline?
The announcement establishes API access; it does not document d1 on-device execution or promise offline use. Liquid AI’s broader LFM materials discuss local deployment and related benefits, but those family-level statements do not establish that d1 handles data locally. If a requirement depends on offline access or keeping inference on a device, confirm support for the exact model and deployment path before relying on it.
Quick Recap
What to check if you need a Liquid model running locally
- Choose a specific downloadable LFM. The company’s LFM catalog and related materials discuss the portfolio, but do not treat it as a d1 download. LFM catalog Pricing FAQ
- Confirm the model package and license. Check that the exact weights are available for your intended use, rather than relying on general statements about open weights.
- Verify runtime and device requirements. Use the selected model’s own documentation for supported runtimes, quantization, operating systems, and memory needs.
- Test the task fit. Confirm that the LFM supports the input types and decision behavior your application needs; general local-deployment support does not mean it performs every d1 task.
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