Excited to announce we’re building an Applied Team focused on post-training. Come explore what's possible with our new (and still unreleased) LTM2 models and their 100M token context window. Apply here:
Magic
Software Development
Build aligned and more complete AI to accelerate humanity’s progress on the world’s most important problems
About us
Magic is working on frontier-scale code models to build a coworker, not just a copilot. Come join us: http://magic.dev
- Website
-
https://magic.dev/
External link for Magic
- Industry
- Software Development
- Company size
- 2-10 employees
- Headquarters
- San Francisco
- Type
- Privately Held
- Founded
- 2022
Locations
-
Primary
Get directions
San Francisco, US
Employees at Magic
Updates
-
LTM-2-Mini is our first model with a 100M token context window. That’s 10 million lines of code, or 750 novels. Full blog: https://lnkd.in/g5-pKWvi Our LTM (Long Term Memory) mechanism needs >1,000x less compute and memory than Llama 3.1 405B’s attention. Llama 3.1 would need 638 H100s *per user* to store a 100M token KV cache. LTM needs a small fraction of one. SSMs, RNNs, and RAG all exploit weaknesses in evals like Needle In a Haystack, so we made a new eval, HashHop: 1) Incompressible 2) Multi-hop 3) No semantic hints 4) No recency bias With context solved, we now focus on unbounded inference-time compute as the next (and potentially last) breakthrough we believe is needed to build reliable AGI. Imagine if you could spend $100 and 10 minutes on one task and reliably get a great pull request for an entire feature. That’s our goal. We are 23 people (+ 8000 H100s) working on a single project: co-designing for long context, inference-time compute, and end-to-end RL to automate coding and research. Ben Chess (fmr. OpenAI supercomputing lead) just joined to help us scale and we’re hiring more engineers and researchers across ML, CUDA, infra, security, and more: https://magic.dev/careers