Startup profile · funding coverage
Liquid AI funding, valuation and investors
MIT-affiliated developer of compact Liquid Foundation Models for on-device and enterprise AI.
Verified company research · funding, investors and latest developments
Keep track of Liquid AI
Save this profile to your VCT watchlist for a quick return.
Funding, valuation & investors
Answer-first snapshotLatest funding
Series A
$250M · December 13, 2024
Latest known valuation
Not publicly disclosed
Total disclosed equity funding
$250M
Excludes debt, grants, acquisitions, secondaries, and IPO proceeds.
Current status
private
series a · Cambridge, Massachusetts, United States
Investors in latest funding
Lead: AMD Ventures
Other: Duke Capital Partners , OSS Capital , PagsGroup
Overview
Liquid AI is an MIT-origin AI company founded in 2023. It develops compact Liquid Foundation Models and deployment software for enterprise and edge systems. The company announced a $250M Series A led by AMD on December 13, 2024, including a strategic effort to optimize models on AMD hardware. Its product line predates NCM, and there is no public basis to classify its financing as an NCM-specific round.
Why Liquid AI is interesting
A significant venture-backed alternative-model architecture business. Its efficiency proposition concerns lower-memory deployment and hardware flexibility, not Stanford NCM or diffusion text generation.
Product & use cases
Lightweight language and multimodal Liquid Foundation Models, with on-device and enterprise deployment tooling.
- Edge and on-device inference
- Private enterprise small-model workloads
- Hardware-aware deployment on AMD platforms
Key facts
- $250M Series A announced December 13, 2024
- Led by AMD with strategic hardware collaboration
- Distinct model architecture, not NCM
Funding history (newest first)
Series A
2024-12-13 $250M- AMD Ventures (lead)
- Duke Capital Partners (participant)
- OSS Capital (participant)
- PagsGroup (participant)
Source: Read source · liquid.ai
Competitive landscape
Edge: Architecture research, hardware partnership and a deployment focus that does not depend on frontier GPU clusters.
Competes with open small language models and compression firms. Performance and cost should be compared for the same task and hardware; not a parallel diffusion generator.
Market / IPO context
Editorial / static context — not a live quote.
FAQs about Liquid AI
Practical answers founders, operators, and investors typically search for.
Last updated:
Editorial note: AI tools assisted with research, structure, or drafting. Venture Capital Tracker retains human editorial responsibility for factual accuracy, relevance, and source quality before publication.