Run voice agents, video models, LLMs and batch jobs on GPUs that start in under a second and scale with each request. Made for teams shipping to production.
Production GPUs,
minus the ops work
Containers restore from memory and GPU snapshots in seconds. Traffic spikes are absorbed by new replicas, not by longer queues or slower responses.
Thousands of GPUs across several clouds and regions. Workloads scale per request, so there is no capacity to plan, reserve or babysit.
dev@workstation adapters % axon run train.py::main --gpu H100:4
✓ Packed 3 files
✓ Created run: adapters-r7 (Compute: 4xH100)
✓ Uploaded archive (7.2 KB)
Logs
step 400/1500 [#######.............] 27% loss=1.51 02:10
step 800/1500 [###########.........] 53% loss=1.18 04:21
step 1200/1500 [################....] 80% loss=0.97 06:30
✓ Saved: /volume/ckpt/step-1200.pt
No rewrites, no decorators, no SDK lock-in. Point us at an entry file or a Dockerfile and we run it as written. Every deploy is versioned and repeatable.
Logs, metrics, scaling events and hardware use, live. OpenTelemetry export sends the same data to the monitoring you already run.
SOC 2 Type II, HIPAA, GDPR
Audited controls for regulated and sensitive data, reviewed every year by an outside firm.
Region pinning
Keep a workload inside one region or jurisdiction. Requests and stored files stay where you set them.
Sandboxed runtimes
Each workload runs in its own kernel sandbox, so neighbours cannot see or slow down your process.
99.99% uptime target
If a region or cloud fails, traffic moves to the next allowed region within your rules.
A voice agent that replies inside 600 ms
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Try now Transcribe a 2-hour recordingTwo hours of audio to text in under 3 minutes
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