Latentforce handles model optimization, serving, scaling, and observability — so your engineering team focuses on models, not infrastructure.
Our inference engine applies dynamic batching, KV-cache optimization, kernel fusion, and INT8/FP8 quantization automatically to every model you deploy. No manual tuning required — the engine profiles your model on deployment and selects the optimal serving configuration to meet your latency targets.
Deploy and manage any combination of LLMs, vision models, embedding models, and custom ONNX-format models through a single Latentforce endpoint. Our routing layer intelligently distributes requests based on model capacity, queue depth, and latency SLAs — with automatic failover when any model instance is degraded.
Our autoscaler monitors queue depth, token-per-second throughput, and p95 latency in real time and pre-emptively provisions GPU capacity before SLAs are breached. Scale from zero to hundreds of A100 instances in under 60 seconds — and scale back immediately to eliminate idle compute cost.
Full-stack observability across every model deployment — from raw GPU utilization and memory pressure down to per-request token cost and latency percentile breakdowns. Integrate with Datadog, Grafana, Prometheus, or use the built-in Latentforce dashboard for a zero-configuration monitoring solution.
Latentforce ships with optimized serving configurations for the models your team already uses.
Meta AI
Mistral AI
Alibaba
Microsoft
Any format
Start with the Starter plan or contact our team to set up a custom enterprise evaluation with your own models.