Rhythm Garg

@rhythmrg

Co-Founder, CTO @appliedcompute 🚂 prev: research @OpenAI @Stanford

Joined January 2021

522

Following

4,586

Followers

Replies Reposts Media Articles

Pinned
Rhythm Garg
@rhythmrg
Apr 8
The difference between smart agents and useful ones is institutional knowledge. We’ve been building the infrastructure for Specific Intelligence at @appliedcompute, and today we’re announcing $80M in new funding led by @kleinerperkins. We’re a team of 23 and growing - come
Applied Compute
@appliedcompute
Apr 8
Article
Applied Compute Raises $80M to Help Enterprises Advance from Generalized to Specific Intelligence
Models keep getting smarter, but there's a massive gap between raw intelligence and actual productivity on specific tasks inside companies. Delivering real value requires knowing how to perform those...
13
Rhythm Garg
@rhythmrg
Sep 4
Super fast, cost-effective search at frontier quality!
Applied Compute
@appliedcompute
Sep 4
A 35B open-weight model trained to search a precomputed index answers repo search questions at 100x lower cost than a frontier model. We partnered with @turbopuffer to train Qwen3.6-35B-A3B to find code across ~9,000 repositories. It tops the needle-in-a-haystack task outright
00:00
1
Rhythm Garg
@rhythmrg
Aug 28
Every redundant tensor, copy, checkpoint stall, or extra rollout node compounds into meaningful effects on the efficiency of our post-training stack. Excited that our work to support Kimi K3 is now making frontier open models much faster and cheaper to train on AC2 across model
Applied Compute
@appliedcompute
Aug 28
Kimi K3 full fine-tuning is live on AC2. Our memory optimizations reduced GPUs required per training replica by ~40%. At nearly 3T parameters, Kimi forced us to rethink how we manage memory, communication, rollouts, and checkpoints. The result is a much more efficient path to
00:00
1
Rhythm Garg
@rhythmrg
Aug 25
One misconception about Applied Compute is that we are primarily a forward-deployed post-training team. That is a big part of what we do, but much of the team builds the underlying training stack and platform, AC2. All of our customer work runs on AC2, which means the platform
Applied Compute
@appliedcompute
Aug 25
Today, we're introducing AC2, the Applied Compute Agent Cloud, to enable every team to train, serve, and improve their own frontier models.
00:00
4
Rhythm Garg
@rhythmrg
Aug 20
Harvey's product harness and domain expertise is real alpha. It gives them something especially valuable for model training: high-quality, product-specific evals grounded in legal expertise. For example, 20% of Harvey's inference spend was on closed-source models to power Review
Gabe Pereyra
@gabepereyra
Aug 20
Update on @harvey’s model training effort. We post-trained a model we are calling Tenet: - Achieves SOTA on LAB - Generalizes 3rd party legal benchmarks - Uses sub-agents for domain specific capabilities Tenet uses Kimi K3 as base and was post-trained in collaboration