Submit benchmark runs, track throughput & latency trends, get instant AI optimization suggestions, and catch regressions before they ship — all in one place.
Throughput
12,480tok/s
P99 Latency
48.2ms
VRAM Used
62.4GB
Give every agent a budget enforced before the money leaves — across both your self-hosted GPUs and your LLM API calls. GPUOPs pauses the instance or blocks the call before you're billed. One pane, both cost surfaces, hard limits.
$460.00 of $460.00 — next call refused, not billed.
10ms
Avg. alert detection
∞
Run history retained
4×
Faster regression debugging
Built for ML engineers who care about performance — from solo researchers to entire AI infrastructure teams.
Define your entire GPU fleet — A100, H100, RTX, and custom hardware — with VRAM, TDP, bandwidth, and compute specs.
Structure model configs with precision (FP32/FP16/BF16/INT8), batch size, sequence length, and framework. Save as reusable templates.
Submit throughput, latency (P50/P99), VRAM usage, power draw, and energy efficiency. All runs stored and searchable forever.
Get instant AI-powered recommendations after every run — precision changes, batch tuning, memory optimization, and more.
Compare up to 4 runs side-by-side with delta analysis. Spot regressions and improvements across your history.
Automatic alerts fire when VRAM exceeds thresholds, latency spikes, or occupancy drops — catch issues before they reach production.
Define parametric experiments across batch sizes or sequence lengths. Submit runs and watch the efficiency frontier chart update live as results come in.
AI-guided wizard to connect your cloud GPU provider (RunPod, Lambda, AWS, GCP…), generate a benchmark runbook, and get a ready-to-run script in minutes.
Try it nowNo infrastructure to set up. No integrations to configure.
Create a hardware profile for each GPU in your fleet.
Set up model name, precision, batch size, and framework once. Reuse forever.
Paste in your metrics. Get instant AI analysis, alerts, and trend tracking.
Our AI-powered wizard connects to your cloud GPU provider of choice — RunPod, Lambda Labs, AWS, GCP, Azure, and more. It generates a ready-to-run benchmark script, a step-by-step runbook, and automatically wires results back into GPUOPs. You own the compute, we handle the setup.
Generated Runbook Preview
Connect RunPod account — A100 80GB selected
Framework: PyTorch · Precision: FP16 · Batch: 8
LLM Inference script generated & ready to copy
Run on instance → paste metrics → AI analyzes
Run structured accuracy tests across GPT, Claude, Gemini, Llama, Mistral, and your own private models. Every response is scored by an AI judge — giving you objective accuracy, token efficiency, and latency rankings in minutes.
Live Score Comparison
Private LLM support: test your fine-tuned or on-prem models via custom endpoint config.
Our Vertical AI Agents analyze your benchmark runs, cross-reference community data, and generate a "Golden Template" — the exact configuration that minimizes cost per token for your specific workload and industry.
$1.87
55% less
48ms
77% faster
89%
2.2× better
$1,640
$2,160 saved
Illustrative results from community RAG benchmark data
Every agent plugs into the same Universal Optimizer Core. Each one inherits your benchmarking data and adds vertical-specific intelligence via MCP — so adding new industries is just a new blueprint, not new code.
9+
Vertical Agents
∞
Custom Blueprints
1
Unified Core
Optimizes for ultra-low TTFT and reliability in patient-facing AI deployments.
Fetches real-time cloud billing data via MCP to ensure recommendations stay within monthly GPU budget.
Maximizes throughput and concurrency for high-volume log processing and predictive maintenance AI.
Balances accuracy and latency for contract analysis, document review, and compliance AI workflows.
Tunes for real-time student interaction workloads with cost-efficient batch processing at scale.
Identifies retrieval bottlenecks and recommends optimal embedding model + GPU pairings for RAG stacks.
Optimizes multi-step agentic loops — minimizes token overhead while sustaining chain-of-thought throughput.
Optimizes property valuation and market analysis AI workloads for cost-efficient batch inference.
Define a custom agent blueprint for any industry or use case. Add a persona, target metric, and MCP endpoint.
Adjust the sliders to match your team's profile and see the estimated monthly savings from catching regressions early and optimizing GPU utilization.
See how your GPU performance stacks up against the community. Filter by hardware, model, and task.
View RankingsAuto-run benchmarks on every PR. Block merges when throughput drops. One YAML file to set up.
See How It WorksOur mission, the team behind GPUOPs, and how we're building the GPU benchmarking standard.
Learn MoreStart free for 3 days. Cancel anytime.