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Open Weight Labs · Private company · Deployment

PrismML

✓ Verified

PrismML is a Caltech-rooted model lab focused on intelligence density, publishing downloadable low-bit multimodal models that move capable inference onto consumer and edge hardware.

Pasadena, USFounded 2026No public API trackedWeekly review

Editorial intelligence brief

What matters now

weekly watch

Released the Apache-2.0 Ternary Bonsai 2 27B on September 17, 2026, a 5.9 GB multimodal model based on Qwen3.8 27B with a 262K-token context window.

Focus: Low-bit multimodal foundation models for efficient local, edge, and datacenter deployment

Latest funding

Not tracked

No structured event

Tracked funding

Not tracked

No structured events

Latest valuation

Not tracked

No source-backed record

Latest revenue

Not tracked

Deployment lifecycle position

Source-backed financial history

Funding and financial history

Funding, valuation, revenue, and debt are kept separate so unlike measurements are never presented as one trend.

Research pending

A structured financial history has not been completed for PrismML.

The profile will not manufacture a trend from incomplete or unsourced figures. Each amount must be dated, typed, and linked to public evidence first.

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Chronological activity

What changed

View the complete market log →

Released the Apache-2.0 Ternary Bonsai 2 27B on September 17, 2026, a 5.9 GB multimodal model based on Qwen3.8 27B with a 262K-token context window.

A structured event record is still being assembled for this company.

Product surface

Products and model families

Recent models

Bonsai 2 27BBonsai 27BTernary Bonsai 8B1-bit Bonsai 8B

Products

Bonsai model familyPrismML model downloadsLow-bit inference kernels

Open weights

Yes

API access

No

Review cadence

Weekly

Market position

Lifecycle and strategic signals

Research

Preview

Growth

Deployment

Deployment: Production & at scale

Focus

Low-bit multimodal foundation models for efficient local, edge, and datacenter deployment

Signals

Open weightsOn-device AIModel efficiency

Taxonomy

Media Modelsopen-modelson-devicemodel-efficiency

Competitive context

Liquid AIMistral AIQwen

Infrastructure & distribution

Where PrismML sits in the AI stack

Model / product operator
01

Model layer

4 tracked model families

02

Access layer

Self-hostable weights

03

Market layer

Open Weight Labs

Public product surface

Bonsai model familyPrismML model downloadsLow-bit inference kernels

Stack signals

open-modelson-devicemodel-efficiencyOpen weightsOn-device AIModel efficiency

This section reflects publicly visible product and access signals. Undisclosed GPU, cloud, training, and data-center contracts are not inferred.

Evidence trail

Sources and corrections

Primary and public sources used to verify the structured company profile.

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