Log inSign up
Ora Frontier
13 posts
Ora Frontier profile banner
@OraFrontier

Ora Frontier

@OraFrontier
Democratizing AI by putting frontier intelligence on your consumer hardware, locally.
Global
orafrontier.com
Joined June 2026
2
Following
12
Followers
RepliesRepliesRepostsRepostsMediaMedia

Log in or sign up for X

See what’s happening and join the conversation

Continue with phone
or
Log in with username or email
Terms·Privacy·Cookies·Accessibility·Ads Info·© 2026 X Corp.
  • Pinned
    @OraFrontier
    Ora Frontier
    @OraFrontier
    Jul 21
    Introducing Ora Frontier: a new shift in what's possible with local and private AI on the machine you already own A laptop that once could only run a 7B parameter model may now run up to 30B+ parameters comfortably, at 99% accuracy and industrial-standard speeds. We're
  • @OraFrontier
    Ora Frontier
    @OraFrontier
    Aug 19
    Introducing Ora Nanolite V2, a 4B parameter model addition to our family of local task models trained for faster reasoning on consumer devices. Based on the Qwen3.5-4B model, Nanolite V2 achieved up to 15.3x speedup in output generation while outperforming its base model across
  • @OraFrontier
    Ora Frontier
    @OraFrontier
    Aug 15
    Introducing Task Models, our solution for enterprises and teams looking to own personalized intelligence. Most day-to-day tasks don’t require the capabilities of large frontier AI and expensive hardware. Task Models are smaller local models trained to be the best at the one
    00:00
    1
  • @OraFrontier
    Ora Frontier
    @OraFrontier
    Aug 12
    The future of AI is privacy and ownership, not larger datacenters for cloud models. We compress open-source AI so you can permanently run, control, and truly own intelligence on-device, even with the world's best models. Every API call is a transaction that introduces data
  • @OraFrontier
    Ora Frontier
    @OraFrontier
    Aug 7
    Own the power of an AI data center, without needing all the server racks and costs. We compile open-weight models to compress that level of frontier performance directly onto your local machine. Historically, running high-accuracy models locally meant investing in extremely