Maple Silicon Inc.

Accelerating
AI Compute
and securing airspace.

SparseFlow accelerates AI inference. Maple Shield enables passive drone detection for contested and remote environments.

Built by one GPU systems team for two different missions.

SPARSEFLOW / INFERENCE ACCELERATION MAPLE SHIELD / PASSIVE DETECTION DEPLOYMENT / CANADA EVALUATION / AVAILABLE
REQUEST EVALUATION
About Founder and company background Review Maple Silicon, Gourav Kumar, and the engineering background behind the company. Maple Shield Passive drone detection system See why deployment density matters, how CAIRN is structured, and where Maple Shield fits operationally. SparseFlow Benchmark-first GPU efficiency Explore the current benchmark framing, validated hardware, and product surface for sparse inference.

01 — The Problem

Inference cost grows
faster than model quality.

LLM teams are under pressure to ship lower latency and lower cost at the same time. The default answer is more GPUs, which makes margin worse and deployment slower.


SparseFlow focuses on the buyer question that matters: throughput per GPU. It improves execution efficiency on supported NVIDIA hardware without forcing a full model-stack redesign first.

1.4×
average speedup on validated production benchmark shapes
1.6-1.7×
peak tested speedup on FFN-heavy inference paths
Up to 40%
inference cost reduction on FFN-heavy workloads
Zero
model changes required to start evaluating

02 - Systems

Two systems,
kept separate.

SparseFlow is the compute product. Maple Shield is the passive drone detection system. CAIRN is the detection and intelligence engine inside Maple Shield.

Live — v2.0

SparseFlow

Model inference acceleration for NVIDIA workloads. SparseFlow is for teams trying to cut latency and GPU cost without rebuilding the whole stack first.

  • A100 validated; RTX 4090 benchmarking in progress
  • Up to 1.6-1.7× on FFN-heavy inference paths
  • No retraining required to evaluate value
  • Built for throughput, latency, and cost-sensitive deployment
Explore SparseFlow →
Active Product

Maple Shield

Zero-RF passive drone detection, persistent tracking, and operator-facing airspace awareness for contested and remote environments.

  • CAIRN 5-layer detection and intelligence engine
  • Passive vision detection - zero RF emissions
  • Integrates with existing Axis, Bosch, and Hanwha camera infrastructure
  • 10 km vision target for fixed critical-site coverage
  • Evaluation conversations available
Explore Maple Shield ->

03 — Engineering Signals

Numbers behind the
product.

31.47 TFLOPS
dense GEMM kernel on RTX 3090,
150% of cuBLAS baseline
A100
validated production
benchmark platform
v2.0
current SparseFlow
release, open source
NATO IC26
Maple Shield submission
(2026)

04 — Evaluation Path

Built to test fit before
you commit budget.

No Long Integration Project

Start from the existing workload or sensing geometry before anyone commits to a heavy deployment motion.

Use The Current Stack

SparseFlow benchmarks against existing PyTorch and Hugging Face style paths. Maple Shield evaluations start from current site coverage assumptions.

Baseline Versus Wedge

Compare dense baseline versus SparseFlow savings, or map passive coverage density versus traditional counter-UAS system economics.

Decision-Ready Readout

For the right setup, the goal is a fast answer on whether the system deserves a deeper benchmark review or deployment conversation.

05 — About

About Maple Silicon

Built by an engineering-led company for teams who need serious technical products, not presentation-layer promises.

Maple Silicon Inc. is a Canadian company building systems products for AI compute efficiency and edge awareness. We care about operational reality: constrained hardware, measurable gains, and deployment paths that stand up outside a demo.

SparseFlow reflects that mindset in GPU inference. Maple Shield reflects it at the sensing edge. Across both, the company bias is the same: make the system useful in the environment where customers actually operate.

Incorporated in Canada A100 validated; RTX 4090 benchmarking in progress SparseFlow and Maple Shield product surfaces live on this site Direct founder contact: gourav.kumar@maplesilicon.co
Gourav Kumar
Founder & Lead Engineer
gourav.kumar@maplesilicon.co

Background in CUDA kernel development, GPU systems engineering, and product-minded technical execution. Built dense GEMM kernels reaching 31.47 TFLOPS on RTX 3090. Currently leading Maple Silicon product development and customer engagements end-to-end.

GitHub

"The next performance win is not just a better model. It's better execution on the GPUs teams already pay for."

06 — Work With Us

Pick the system that matches the mission.

SparseFlow is for inference acceleration. Maple Shield is for passive drone detection and airspace awareness. Start with the right page, then request evaluation if the fit looks real.

Explore SparseFlow Explore Maple Shield Request Evaluation

Founder contact for product conversations: gourav.kumar@maplesilicon.co. General inquiries: info@maplesilicon.co.