--- layout: page title: "Testimonials" permalink: /testimonials/ description: "What developers and partners are saying about FastFlowLM on NPU-first architectures." ---

Testimonials

# What developers, partners, and silicon teams are saying

FastFlowLM combines deep knowledge of LLM architecture with world-class optimization for hardware-level parallelism. The result is the industry’s first—and only—runtime truly engineered for NPU-accelerated AI inference. Our debut product, deployed on AMD’s Ryzen™ AI platform, is revolutionizing on-device intelligence with unprecedented speed, efficiency, and responsiveness. These stories highlight how an NPU-first runtime lands in the real world—from beta cohorts and AMD’s own AI engineering org to third-party benchmarking labs.

User Adoption

### Overwhelming user trials and responses

> “Within hours of the beta release, thousands of builders pulled FastFlowLM from GitHub and ran it on their own Ryzen™ AI laptops.”

- Within hours of the beta release: Thousands of users pulled FastFlowLM from GitHub and ran it on their own hardware.
- Community content: Early users independently produced videos and walkthroughs showing AMD NPUs are far from useless with the right runtime.
- Developer competitions: The winning team in a global AI PC developer contest chose FastFlowLM as their NPU runtime.
- Customer feedback: One early customer wrote that our solution “seems to be the most elegant so far for AMD NPUs.”

FastFlowLM beta cohort First 72 hours post-launch
AMD AI Team

### Feedback from AMD AI engineering leaders

> “We’re interested in FLM. I spent considerable effort in getting Copilot up on our AIE/NPU and it is a difficult beast. Your kernels and model implementations appear to be closed source but your perf numbers seem impressive.”

- Kernel fidelity: Tile-optimized operators map directly to AMD’s AIE architecture.
- Model coverage: Flagship reasoning, multimodal, and MoE models stay inside Ryzen™ AI silicon limits.
- Confidence to ship: AMD’s own field teams reference FastFlowLM in partner enablement sessions.

Senior AMD AI team leaders Ryzen™ AI Architecture (AIE/NPU)
Performance Engineering

### Proof from independent benchmarking labs

> “Real-time NPU inference is not just possible, but practical for everyday users.”

> “Gaming? No time for that. How about running Llama3.1 8B on the AMD Ryzen AI Z2 Extreme NPU in the ROG Xbox Ally X Via FastFlowLM instead?" ”

- Llama 3.2:3B: Demonstrated on an AMD Ryzen™ AI device with steady token streaming.
- Thermal headroom: Runs stay under 2W, extending battery life versus GPU-bound stacks.
- Agent workflows: Deterministic latency keeps step-by-step chains responsive in demos.

Client performance director Global benchmarking firm

Share your FastFlowLM story

We’re continuing to collect stories from developers, OEM partners, and researchers running FastFlowLM on real Ryzen™ AI hardware.

- Developer workflows: How FastFlowLM fits into your local dev loop, CI, or production agents.
- NPU performance wins: Concrete improvements in latency, throughput, or power draw vs. GPU-first stacks.
- Use cases: From local assistants and multimodal copilots to privacy-preserving RAG and on-device analytics.

If you’d like to be featured here, reach out at info@fastflowlm.com or in the FastFlowLM Discord.