Time to first token
Time from request arrival until the first token reaches the user.
HOLYCORESLLM INFERENCE
Large-context inference,
engineered for production.
From the first token through sustained generation, from node validation to scale-out, HOLYCORES co-designs compute, memory, fabric, and scheduling as one inference system.
MEASURE THE EXPERIENCE
Inference performance is more than a peak number.
Real service quality is shaped by prompt processing, first-token response, sustained generation, concurrency, and stability. We build reproducible baselines around the actual model, context, precision, and service level.
Sustained generation
Track inter-token latency and interactive fluidity during decode.
Throughput & concurrency
Measure stable request capacity within the target latency envelope.
Cost per service unit
Include energy, capacity, utilization, and availability in inference economics.
Half the time to first token, 4× output speed.
For long-context inference and real-time agentic workloads, AETHER and HOLYFLOW optimize prefill and decode independently—parallelizing large inputs while sustaining generation across KV cache, on-chip memory, and fabric.
Time to first token
Parallelize input compute and overlap data placement with dataflow so long-context requests reach generation sooner.
Sustained output speed
Coordinate on-chip memory, device memory, and inter-node dataflow to raise continuous token generation at low latency.
Performance targets guide architecture and validation. Results vary by model, context, precision, batch, system scale, and software version, and remain subject to formal benchmark reports.
PREFILL + DECODE
Put the right resources behind each inference phase.
Prefill processes large inputs quickly; decode repeatedly accesses weights and KV cache at low latency. HOLYFLOW Runtime coordinates both phases and schedules resources around request length, concurrency, and service objectives.
Request shaping
Form batches by context length, priority, and service class.
REQUESTParallel prefill
Distribute input compute for scalable prompt processing.
CONTEXTPipelined decode
Coordinate on-chip memory, device memory, and fabric for steady output.
GENERATEService delivery
Stream results while recording latency, throughput, and faults.
OBSERVE
NETWORKED AI
From node to rack. Treat the cluster as one computer.
AETHER connects nodes through high-bandwidth fabric while HOLYFLOW Runtime coordinates model partitioning, routing, fault isolation, and telemetry. Scale means predictable service as the system grows—not simply adding devices.
- Tensor & pipeline parallelismFor models beyond a single node.
- Disaggregated prefill / decodeRight-size and scale each phase independently.
- Topology-aware schedulingReduce cross-node traffic and tail latency.
HOLYFLOW SOFTWARE
Make every layer contribute to the end-to-end result.
An open software path connects frameworks, compiler, operators, runtime, and production serving—with a fast path in and control at every layer.
Continuous batching
Refill batches dynamically to improve utilization and control queues.
KV cache management
Allocate, reuse, and reclaim cache over the session lifecycle.
Precision & quantization
Validate BF16, FP8, INT8, and other strategies against quality targets.
Speculative decoding
Use candidate tokens to reduce serial decode waits where appropriate.
Performance profiling
Trace operator, memory, fabric, and scheduling bottlenecks.
Production serving
Support versions, rollout, scaling, health checks, and observability.
RIGHT-SIZED DEPLOYMENT
One inference stack, across system boundaries.
NOVA
Flexible card-level inference capacity for enterprise servers and private AI.
View product →AETHER
From desktop development to rack-scale, supporting large models and highly concurrent production serving.
View product →VALIDATION PATH
Answer with evidence first. Then decide how to scale.
- 01
Define service objectives
Set model, precision, context, TTFT, TPOT, throughput, and availability targets.
- 02
Build a node baseline
Reproduce quality, performance, memory, and power with representative data.
- 03
Validate scaling efficiency
Add cards, nodes, and concurrency while tracking communication and tail latency.
- 04
Operate continuously
Integrate monitoring, capacity planning, versions, recovery, and cost analysis.
FAQ
Start with the real model.
Which models can be deployed?+
Model support depends on architecture, operators, precision, and context configuration. Bring the target model and service objectives so we can establish a clear compatibility and performance baseline.
Should we optimize TTFT or throughput first?+
It depends on the workload. Interactive assistants often prioritize TTFT and TPOT, while batch jobs favor throughput and unit economics. Production plans need explicit priorities and limits.
Can validation start on one card?+
Yes. Validate model quality and performance on a NOVA or AETHER node, then scale according to parallel strategy and fabric efficiency.
BRING YOUR MODEL