See every GPU.
Tune every token.
Govern every model.
Marvean joins low-level hardware telemetry with LLM-level observability, so teams running large language models, vision pipelines and multi-agent systems can cut GPU waste, control costs and secure their AI workloads. Connect with the Marvean SDK in minutes.
Enterprise AI is hard to see inside
Dense GPU clusters and layered software stacks create waste and risk that standard IT monitoring cannot see.
GPU underutilization & memory bottlenecks
Poor batching, inefficient data pipelines and VRAM fragmentation leave high-end GPUs idle while costs keep running.
Black-box model execution
CPU and RAM dashboards miss Time-To-First-Token, inter-agent handoff delays, model drift and embedding search latency.
Unpredictable compute costs
Bursting inference and distributed training across clouds hides which models and workloads consume the most compute.
Security & operational risk
Prompt injection, data leaks in context buffers and failures at peak inference load need AI-specific detection.
One intelligence layer, four jobs
Each layer of the Marvean platform is paired with the NVIDIA technology that powers it.
Hardware & telemetry ingestion
Real-time, high-frequency metrics from GPU clusters, NVLink and InfiniBand interconnects, thermal monitors and memory controllers.
AI application & LLM observability
Tracks inference pipelines, prompt handling, token throughput and multi-agent topologies. Finds queue time and batching gaps.
Autonomous compute optimization
Balances workloads, tunes batch sizes, adjusts quantization and reroutes requests across GPU nodes.
Security, guardrails & anomaly detection
Scans payloads, model traffic and system events for threats, prompt injection and data leakage.
NVIDIA technology integration
| Technology | Function | In Marvean |
|---|---|---|
| NVIDIA NVML | GPU management & telemetry | Captures real-time GPU utilization, VRAM usage, temperature and power metrics. |
| NVIDIA Triton | Model serving & performance metrics | Tracks concurrent model execution, request latency and dynamic batching efficiency. |
| NVIDIA TensorRT-LLM | Inference optimization | Analyzes runtime profiles to tune LLM serving configurations and token efficiency. |
| NVIDIA RAPIDS | GPU data analytics | Speeds up log analytics, time-series telemetry processing and cluster health scoring. |
| NVIDIA Morpheus | Operational cybersecurity | Inspects AI workload data streams for security anomalies, prompt injections and data leaks. |
| NVIDIA NeMo | LLM guardrails & governance | Tracks LLM safety compliance and response quality through framework integrations. |
Deployment architecture
H100 / A100 GPUs power fleet analytics and benchmarking. L4 / L40S GPUs run local telemetry and dashboards.
Marvean SDK
Add the Marvean SDK to your serving code and start streaming GPU and LLM metrics. Use the REST API when you prefer to send data from your own agents.
- Python SDK for training jobs and inference services
- Triton collector that reads model instance metrics
- Token, TTFT and queue-time tracing for every request
- Enterprise single sign-on for dashboards and API keys
# pip install marvean-sdk
# connect the SDK to your fleet
from marvean import Client, Triton
mv = Client(api_key="MV_API_KEY")
mv.attach(Triton(url="http://triton:8000"))
mv.gpu.watch(nodes="all", interval="1s")
# trace one LLM request
with mv.trace("support-agent") as t:
answer = llm.generate(prompt)
t.record(ttft=True, tokens=True)Technical roadmap
NVML & RAPIDS engine launch
GPU-accelerated telemetry ingestion and real-time cluster health scoring.
Triton performance profiler
Real-time inference latency and queue monitoring across multi-tenant deployments.
TensorRT-LLM auto-tuner
Automated runtime recommendations for LLM quantization and batch sizing.
Morpheus security integration
GPU-driven anomaly detection for prompt injection and data leak prevention.
Multi-cloud fleet governance
Unified cross-cloud GPU scheduling and automated cost optimization.
Run your AI fleet with full visibility
Request access to the dashboards, API endpoints and SDK demo environment. Access is provided through enterprise single sign-on.