For teams running distributed AI training
One slow GPU slows your whole training job. Artemis finds it and fixes it.
Artemis watches your training jobs while they run. When a rank falls behind, it pinpoints the cause — a GPU, a link, or a host — applies a fix inside the policies you set, and verifies that throughput recovered.
In development · NVIDIA and AMD · Owned or rented clusters
- r0NV
- r1NV
- r2NV
- r3NV
- r4AMD
- r5AMD
- r6AMD
- r7AMD
Finding · window 37
- kind
- rank
- state
- flagged
- rank
- 2
- island
- nvidia
- ratio
- 1.19
- excess_ms
- +8.1 per step
- loss_pct
- 7.6
- cause
- clocks
Finding · window 37
- kind
- rank
- state
- flagged
- rank
- 2
- island
- nvidia
- ratio
- 1.19
- excess_ms
- +8.1 per step
- loss_pct
- 7.6
- cause
- clocks
- Compute
- Waiting at the barrier
- All-reduce
- The regressed rank
Rank 2 is running 19% slow. The other seven ranks wait for it at every all-reduce. (Illustration.)
- large training jobs (512–1,024 GPUs) hit a fail-slow slowdown in one month
- 16 of 27
- of allocated GPU-hours lost to stragglers, estimated from one production cluster's traces
- ~10%
- on average, and up to days, for engineers to manually find one faulty machine
- 30+ min
Figures from published production-cluster studies: FALCON (arXiv:2410.12588), Lin et al. (arXiv:2505.05713), Minder, ByteDance (NSDI '25).
Nothing crashes. Every step just gets slower.
Distributed training moves in lockstep: a step finishes when the slowest rank arrives. One degraded GPU, link, or host sets the pace for the entire job — while utilization still looks busy and nothing raises an error.
So teams find out late, or not at all. The run finishes hours behind, and the investigation starts from logs, across the infrastructure, network, storage, and hardware teams.
- Throughput drops a few percent mid-run — and stays down
- GPU utilization looks fine; the job is slow anyway
- No error names the GPU, link, or host that changed
From slowdown to verified recovery, in one loop.
- 1
Detect
Artemis times every rank at the collectives it already joins, and compares each rank against its peers and its own history. A persistent slowdown is flagged while the job is still running — not in a postmortem.
- 2
Confirm
Before anyone acts, Artemis re-tests under matched conditions: same workload, hardware, and topology. A one-off fluctuation doesn't become an incident, and an unconfirmed regression never becomes the new baseline. The same matched testing runs after driver, library, or configuration changes.
- 3
Localize
Evidence across the host and input pipeline, the GPU, the communication runtime, and the interconnect narrows the problem to the rank, node, link, or software change responsible — with the measurements behind the conclusion, including when the evidence isn't enough to call it.
- 4
Act
Within the policies you set: rebalance work away from the slow rank, recommend a targeted fix, or apply a supported correction. Anything that needs a restart, a repair, or your approval is escalated — never hidden.
- 5
Verify
Artemis re-measures useful training throughput after the action. If it didn't recover, you see that too — and reversible actions roll back.
The slowdowns that don't crash anything.
GPU degradation
Thermal throttling, power caps, clocks that stop boosting.
Communication
A degraded port, link, or switch path; one island's collectives slowing down.
Host and input
CPU contention, or a data-loader or storage stall holding one rank back off the GPU.
Change regressions
Slowdowns that arrive with a driver, communication-library, or configuration change.
Not another dashboard to watch.
Monitoring tells you something is slow. Artemis closes the loop: it confirms the slowdown is real, localizes it, acts within your policies, and proves the recovery. The output isn't a chart — it's an incident with a cause, the evidence for it, the action taken, and the measured result.
Recover GPU-hours you're already paying for
Measured against comparable workloads, not inferred from utilization.
Give engineers their time back
Investigations that take a team hours to days become one supervised workflow.
Keep the proof
Every fix verified against throughput; every conclusion traceable to measurements.
Built for the team that owns training performance.
AI infrastructure, research-computing, and GPU-operations teams running distributed training on owned or rented clusters. Vendor-independent by design: across NVIDIA and AMD, across GPU generations, not tied to one cloud's or one vendor's stack.
In development — built with the teams who'll run it.
Artemis is in active development. What exists today: GPUBridge, our open-source backend for mixed NVIDIA + AMD PyTorch jobs, and the detector prototypes described inside. We're talking with infrastructure teams about the slowdowns that cost them the most — those conversations set the roadmap.