Model-aware GPU fleet optimization layer
Your monitoring sees GPUs. It doesn't see models.
More GPUs isn't always the answer. Better utilization of the ones you have usually is.
Paralleliq identifies the recoverable capacity, recommends the fix, routes it through your team for approval, and keeps a signed record of every decision made.
Your model weights, inference data, and customer traffic never leave your environment. Operational metrics — GPU utilization, KV cache pressure, token throughput — flow to the platform to power recommendations, evaluated through a deterministic rules engine, not a black-box model — and nothing executes without a human approving it first.
Models change. Workloads shift. But your infrastructure has no idea.
Paralleliq addresses the three structural failures at the core of AI operations.
Models get updated. Traffic patterns shift. Token compression reduces context length. Customer load grows. The GPU you correctly sized six months ago may be wrong today — but nothing in your stack will tell you until OOM events start or your throughput collapses.
The result: Tier misplacement that could have been caught at deploy time becomes months of avoidable GPU spend, or a 3am incident.
High GPU utilization is a false signal. Your GPUs are busy — but busy doing the wrong things. Wrong batch sizes, wrong instance types, wrong concurrency settings. The meter is running. The throughput isn't keeping up.
The result: Your GPU bill grows faster than your throughput. That's not an infrastructure problem — it's a margin problem and a capacity problem. Every point of recoverable utilization is throughput you could be selling. Most teams respond by buying more hardware. The better answer is finding what's already there.
A customer deploys a model slightly too large for their chosen GPU tier. Under load, KV cache pressure builds and the container crashes. Your platform gets the support ticket — even though the root cause was a configuration the customer created. Without model-aware intelligence, there is no way to catch this before it happens.
The result: Repeated OOM events look like platform instability. Cold start latency looks like a slow platform. Your customer blames you for a problem you could have prevented.
Most infrastructure tools treat GPUs like CPUs. Paralleliq understands what's actually running on them.
The optimization engine is rules-based and deterministic — not model-driven. No AI making infrastructure decisions. Every recommended action shows you the blast radius and requires human approval before it touches your cluster.
Starts as a read-only one-time scan. Scales to a lightweight agent — one per node, reading from your existing Prometheus. Auto-discovers vLLM and Ray Serve workloads with no changes to your serving stack.
Know exactly what each deployment costs per hour and per request — and where tier mismatches, memory pressure, or idle capacity is burning budget.
Safety signals every 15 seconds — KV cache pressure, OOM risk, queue depth. Performance checks every 30 minutes. Structural tier analysis every 6 hours. Catch the problem before it becomes an incident.
Every recommendation approved by a human. Every action logged permanently. Full chain of custody for every change to your fleet.
Model weights, inference data, and customer traffic never leave your environment. Operational metrics — utilization, throughput, cache pressure — flow to the platform to power recommendations. Nothing that touches your customers' data moves.
Running on-prem hardware, reserved instances, or proprietary models? We configure the platform to your actual contracted costs, your model catalog, and your team's operational policies — so the waste we surface is specific to your fleet, not a generic estimate.
Every GPU inefficiency hits your P&L directly.
You host the models, own the infrastructure, and charge per token. A model on the wrong GPU tier isn't an ops problem — it's a margin problem. Paralleliq surfaces tier misplacement, dark capacity, and throughput suppression at the model level, with dollar impact per finding.
Your platform gets blamed for your customers' misconfigurations.
When a customer's model OOMs, your platform takes the ticket. Paralleliq watches every customer deployment — on your cloud or theirs — and catches configuration problems before they become support incidents or churn.
Cost control and governance for teams running their own inference.
You run your own models on your own infrastructure. Paralleliq gives you visibility into what's actually being spent, a governance layer your security team accepts, and an immutable audit trail for every infrastructure decision.
Works with your stack
Ecosystem partners
Deep dives into architecture, performance tuning, and operational excellence.
Two developments in the vLLM ecosystem — LMCache's cross-instance KV cache sharing and vLLM-Omni's multi-stage serving — point at where inference efficiency problems are heading next, and why a one-time configuration decision won't keep up.
Every GPU optimization tool will tell you what's wrong. Almost none of them tell you what happens next — between the moment an engineer agrees with a recommendation and the moment the fleet actually changes.
Token compression reduces what you pay per API call. Most teams stop there. The infrastructure math changes too — shorter contexts mean smaller KV cache requirements, which means a different GPU tier, more concurrency, and a lower GPU bill. Here is how to recalculate.
