Dedicated per service
Peak reservations isolate idle capacity.
AI is dynamic,your GPUs should be too.
Atom rebalances GPU compute across inference services in real time, so your existing fleet carries more work.
Built for the stack you already run
- vLLM
- SGLang
- TensorRT-LLM
- Dynamo
- llm-d
- Kubernetes
- Anything custom
You pay for the entire fleet, but use a fraction of it.
Production GPU fleets typically run at 20-40% utilization because latency reserve sits idle between peaks.
Paid for and never used, per year
$3.92M
of the $5.61M you spend running 128 GPUs.
30% used70% idle
Each square is one percent of the fleet.
8 GPUs2,048 GPUs
5%95%
B200 at $5 per hour, 8,760 hours a year. The fleet is billed around the clock whether it is busy or not.
in usebought and idle
avg 29% utilized
Service A43% busy
Service B27% busy
Service C3% busy
27% of the fleet working · the rest is billed anywayTime →
Models can't fill it
Inference phases leave different GPU resources idle.
Schedule-time allocation
Schedulers cannot reshape live GPUs.
One shared pool, allocated in real time.
Unchanged services draw from one pool that expands and contracts with demand.
- 01
Observe workload demand
Atom learns each workload's live resource and latency patterns.
- 02
Reallocate GPU resources
GPU resources follow demand in seconds, without drains or restarts.
- 03
Maximize performance
Throughput rises while latency targets and priorities hold.
- 04
Reduce GPU costs
Run 2-3x more work on every GPU.
Service AService BService Cused capacity
avg 85% utilized
One poolcapacity follows demand
74% of the fleet free for other work, or off the billTime →
More work per GPU, with SLAs held.
Every workload keeps its latency targets while the fleet does more.
- 2-3x
- more work per GPU
- Versus a dedicated-GPU baseline.
- 99.9%
- SLA attainment
- Latency targets held while GPUs are shared.
- ~0%
- added overhead
- No measurable cost when a GPU runs alone.
Separate reservations, one shared pool
Workloads that each reserved capacity of their own used a fraction of it. Drawing from one pool instead, the same work runs on a fraction of the hardware at the same throughput, because their peaks rarely arrive together.
Benchmark your own workloadseparate reservationsone pool
Same GPUs. More AI in production.
Five production patterns for fine-grained co-location on Kubernetes.
Agents
Co-locate agent models on 2-3x fewer GPUs.
Multi-model platforms
Pack dozens of models into one shared fleet.
Voice and media generation
Share GPUs while holding the same latency SLAs.
LoRA personalization
Share base models while capacity follows traffic.
Batch inference
Fill spare live-service capacity with offline work.
Another pattern
Benchmark your workload on your own hardware.
Get early accessEasy deployment with your existing stack.
Atom installs beneath your existing stack, with no changes above it.
- No application changes
- No model changes
- No container changes
Enterprise-ready
- SOC 2 certified
- ISO 27001 certified
Measure your GPU savings.
We benchmark Atom against your workloads and baseline on your hardware.
Questions? founders@runatom.ai