Moreh

Solution

Maximize Tokens per Dollar

LLM inference cost dominates AI operations budgets — and it's growing as models scale and agentic workloads diversify. Moreh optimizes at every level to deliver the most tokens per dollar.

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Three Levers for Cost Reduction

These three levers work multiplicatively — chip optimization multiplied by cluster efficiency multiplied by infrastructure cost savings.

1

Chip-Level Optimization — Moreh vLLM

1.68× higher throughput than ROCm vLLM on DeepSeek R1 671B. Custom operations, precision optimization, and operator fusion extract maximum tokens per second from every GPU.

Custom GEMM/Attention/MoEOperation fusionQuantizationComm/compute overlapEP load balancing
2

Cluster-Level Optimization — MoAI Inference Framework

2.2× throughput on 40% fewer servers with prefix cache-aware routing. Prefill-decode disaggregation, smart routing, auto scaling, and SLO-driven optimization maximize utilization across the entire cluster.

PD disaggregationPrefix cache-aware routingSLO-driven optimization
3

Infrastructure Cost — Heterogeneous GPU Utilization

1.7× throughput by combining NVIDIA and AMD GPUs with cross-vendor prefill-decode disaggregation. Use cost-effective AMD GPUs, Tenstorrent accelerators, or existing older-generation hardware — every GPU contributes to cluster throughput.

Cross-vendor PD disaggregationModel-aware GPU placementLength-based routing

See the Numbers for Your Workload

Share your model, traffic pattern, and hardware — we'll run a custom benchmark and show you the cost savings.

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