30 minutes to find out where your GPU budget is actually going.
The Inference Expert Audit is a free 30-minute session with a NeoX inference engineer. We look at your actual fleet — GPU count, models, traffic shape, serving config — and tell you where the capacity is going. You leave with a findings summary. No pitch deck, no pilot required.
Free. 30 minutes. No commitment, no procurement, no data leaves your environment.
Works with your existing vLLM / on-prem GPU stack·No migration·VPC and air-gapped supported
Going from GPU to tokens is harder than anyone budgeted for.
Buying GPUs is a purchase order. Turning them into reliable tokens is an operating problem — and it’s the part nobody staffed for. Inference is mission-critical, but in most organisations it has no clear operational owner and no unified control layer. Infra owns the hardware. ML owns the models. Nobody owns the tokens.
Fragmented Stack
vLLM here, a gateway there, Prometheus somewhere else, three dashboards and a Slack channel. No single place that answers “is inference healthy right now, and who is using it.”
Significant GPU Waste
Models loaded and running but not serving anything. Pools dedicated to one team and idle two-thirds of the day. Capacity you already paid for, doing nothing.
24/7 Firefighting
Latency spikes get diagnosed by whoever is awake. One noisy workload starves the copilot everyone in the company depends on. Root cause takes hours because the evidence lives in four systems.
Cost Blindness
You can see the GPU invoice. You cannot see cost per team, per model, or per task. So you cannot make a capacity decision with any confidence.
If two or more of these describe your week, the audit will be worth the 30 minutes.
What you get in 30 minutes.
This is an engineering conversation, not a discovery call. A NeoX inference engineer runs it. Here’s exactly what comes out of it.
- 01
An effective-utilization read on your fleet.
What fraction of your GPUs is actually converting into served tokens, and which of your pools are the worst offenders.
- 02
Named waste, not general advice.
The specific patterns we can identify from your setup: models running but unpublished, dedicated pools with no pooling, unrated workloads that can starve everything next to them.
- 03
A capacity-headroom estimate.
How much of your current traffic you could serve on less hardware — or how much more traffic your current hardware could take — based on your model set and traffic shape.
- 04
Latency and SLO gap review.
Where your TTFT and end-to-end latency are today versus what the workloads on top of them actually require.
- 05
A cost-attribution gap list.
The specific questions about spend you currently cannot answer, and what instrumentation closes each one.
- 06
A written findings summary.
Yours to keep and circulate internally, whether or not you ever talk to us again.
What this is not.
It’s not a benchmark run against your cluster — we don’t touch your environment during the audit. It’s not a security review. And it’s not a disguised demo: if what you need is a config change in vLLM rather than a control plane, we’ll tell you that and you’ll be done in 30 minutes.
What we need from you.
Nothing installed, nothing granted. Come with rough answers to these and the 30 minutes will be worth double.
Fleet shape. How many GPUs, what class (H100 / H200 / A100 / L40S / MI300X), on-prem or VPC.
What’s serving. Which open-source models are in production, and roughly how many distinct ones per pool.
Traffic shape. Approximate requests or tokens per day, and whether it’s interactive, batch, or both. A guess is fine.
Serving stack. vLLM, SGLang, or other — plus whatever sits in front of it as a gateway.
Who’s asking. The teams or apps consuming inference, and whether you can currently attribute cost to them.
The thing that’s actually bothering you. The latency complaint, the invoice, the incident last month. One sentence.
No credentials, no cluster access, no data. If you’d rather not name models or teams on the call, describe them generically — the analysis still works.
Who you’ll be talking to.
NeoX is built by repeat founders with two exits, both now NYSE-listed, by Unit 8200 alumni, and by former engineering leaders from Microsoft, Salesforce and Huawei. The person running your audit has operated GPU fleets in production, not read about them.
NeoX is the brain above the stack. It observes and steers. It does not replace your serving layer, and the audit doesn’t assume you’ll change it.
Questions people actually ask.
Is it actually free?
Yes. No cost, no commitment, no procurement step. We run these because the conversation tells us as much about the market as it tells you about your fleet.
Do you need access to our cluster?
No. The audit is a conversation. Nothing is installed and no credentials change hands.
We’re air-gapped. Does that rule us out?
No. A large share of the teams we talk to are VPC or air-gapped. NeoX deploys inside your boundary, and the audit itself requires no connectivity at all.
Will you tell us to replace vLLM?
No. NeoX sits above vLLM and SGLang. If we thought the answer was to replace your serving layer, we’d have built a different company.
Who should be on the call?
Whoever can describe the fleet and whoever feels the pain — usually an AI platform or infrastructure lead, sometimes with a FinOps or engineering leader. Two or three people is ideal. One is fine.
What happens after the audit?
You get the written findings summary. If there’s a fit, we’ll propose a scoped pilot — Day 1 visibility, Day 7 control, Day 30 autonomous operation. If there isn’t, we’ll say so and you’ll have the findings anyway.
Is this a sales call in disguise?
An inference engineer is on every call. If you want the honest version: we’d like you to become a customer, and we’ve found the fastest route to that is telling you the truth about your fleet for 30 minutes.
Find out where your GPU budget is going.
30 minutes with a NeoX inference engineer. A written findings summary afterwards. Nothing installed, nothing to sign.
Free · No commitment · Nothing leaves your environment
