
Turn compute, memory, and power into more tokens
CHALLENGES

When reusable context is lost, GPUs repeat prefill instead of generating new tokens, wasting time, power, and money.

More users and longer context increase memory, compute, power, and cooling demand faster than token output.

GPU resources can sit underused while teams add separate infrastructure, increasing cost and footprint.
BENEFITS
Reduce repeated work and infrastructure overhead so more of every GPU cycle, watt, and dollar go towards producing tokens.
Reuse computed context so more GPU cycles go toward generating new tokens instead of repeating work.
Increase useful token output without scaling compute, memory, power, cooling, and infrastructure cost at the same rate.
Put underused GPU resources to work as part of the data layer instead of adding separate infrastructure.
Increase data performance and density so less rack space and power are consumed by the infrastructure supporting GPUs.
0x
Higher Token Throughput
0 seconds
Inference deployments took 5 minutes now occur in 15 seconds
0.0 EB
effective capacity per rack

“WEKA’s NeuralMesh platform with Augmented Memory Grid on OCI helps remove memory bottlenecks, delivering substantially more throughput and concurrent users from the same GPU footprint. For customers, that means higher ROI on infrastructure investments and a clearer path to cost-efficient AI at scale.”
FEATURES
WEKA® NeuralMesh™, WEKA Augmented Memory Grid™, NeuralMesh Axon™, and WEKApod™ improve how AI factories use compute, memory, data infrastructure, and physical datacenter capacity.
Store petabytes of key-value (KV) Cache in NeuralMesh and serve it back at memory speed, so GPUs spend less time repeating prefill and more time generating new tokens.

Turn idle compute into active throughput. Offload reusable context over RDMA to generate more tokens per rack.
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