Model qualification
Establish whether one model runs correctly and usefully, then freeze compatibility, quality, latency, throughput, memory and power evidence.
Engineering divisionApexSilica
Production models, enabled
on accelerator platforms.
ApexSilica qualifies customer models on exact accelerator stacks, resolves graph, compiler, kernel, runtime and serving bottlenecks, and delivers reproducible reference deployments.
The problem
What has to work.
Good silicon is not enough. Important operators may be unsupported, models may not lower cleanly, quantization can damage quality, data movement can waste performance, and benchmark results may not translate into production behavior. ApexSilica closes the gaps between the model and the accelerator stack.
Who it is for
AI accelerator and ASIC companiesGPU, NPU and edge-chip vendorsClient-AI device manufacturersInference platforms and neocloudsOEMs building AI-powered edge productsSoftware teams adopting non-standard accelerators
What ApexFlo provides
What the engagement delivers.
Model enablement
Resolve export, graph, unsupported-operator, quantization, compiler, partitioning and runtime problems preventing deployment.
Performance optimization
Profile the complete workload and improve the model, compiler, operator, memory, scheduling, serving, energy or thermal bottleneck that actually matters.
Reference deployment
Package a reproducible environment, correctness suite, benchmark bundle, deployment configuration, qualification card and technical report.
What is delivered
✓Model–Accelerator Qualification Sprint
✓Accelerator Enablement Project
✓Continuous Model Enablement
✓Qualification cards and reproducible technical evidence
Qualification and enablement
Bring up → Measure → Diagnose → Improve → Qualify
Every engagement is defined around one model, one accelerator stack and one real workload. Every optimization must pass correctness, quality and end-to-end performance gates before it is retained.
01Bring up
02Measure
03Diagnose
04Improve
05Qualify
Engagement models
Start with one model.
Expand after it qualifies.
If an important customer model does not run, does not perform or cannot be demonstrated credibly on your hardware, that is where ApexSilica enters.
Model Qualification Sprint
One model, one platform and one frozen workload. Receive a compatibility map, baseline, bottleneck report and qualification plan.
Accelerator Enablement Project
Implement graph, compiler, operator, kernel or runtime improvements and deliver a qualified reference deployment.
Continuous Model Enablement
Ongoing support for new models, SDK releases, hardware revisions and performance regressions.
Automation-assisted qualification
ApexSilica uses coding and optimization agents to generate and evaluate more implementation candidates. Agents accelerate the search; they do not decide what is production-ready. Every candidate must pass numerical correctness, model-quality and end-to-end performance gates on the target hardware.
Model-aware
We optimize the complete workload, not isolated synthetic operations.
Hardware-aware
Every result is tied to an exact accelerator, SDK and runtime.
Evidence-controlled
Failed and rejected experiments are retained, not hidden.
Deployment-focused
The final output is a reproducible model deployment, not only a benchmark report.
We work at the deepest level supported by the platform’s interfaces, tooling and source access, with the target hardware and acceptance measures fixed at the start.
Delivery evidence
Proof from delivery.
Validation on real systems.
Intel Core Ultra 9 285H · Qwen3-8B INT4
Nearly doubled GPU throughput for bounded shared-prefix traffic and reduced NPU time to first token by 89% for a bounded interactive profile. An isolated RMSNorm speedup was rejected when it slowed the complete model.
NVIDIA Jetson Orin · Qwen3-8B INT4
Reduced batch-one generation latency by 17% and increased batch-four throughput from approximately 31.5 to 58.9 tokens/second after isolating an FP16 vocabulary projection in the INT4 path.
Axelera Metis · YOLO and RT-DETR
Sustained approximately 565 FPS on the qualified YOLO11n pipeline and reduced four-stream median latency by 28.6% without reducing throughput. Profiling also identified the compiler and quantization work required for the RT-DETR path.
Deployment considerations
- Engagements require access to the target hardware, compiler, runtime, profiler and usable model artifacts.
- Published results are tied to the recorded versions, inputs, precision, workload and measurement method.
Next engagement step