<section class="section section--alt" id="benchmarks">
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            <p class="kicker">Benchmarks</p>
            <h2 class="headline-md">Proof on silicon, not slides</h2>
            <p class="copy-md">
                FastFlowLM is tuned on real Ryzen™ AI hardware with synthetic and application-level workloads.
                Expect steady 40–80 tok/s on 7B models at < 10 W, plus deterministic latency for agentic chains. </p>
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                                <strong>Full-stack telemetry</strong>
                                <p>Counters for NPU, CPU, and memory let you see exactly where cycles go.</p>
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                                <strong>Scenario-driven suites</strong>
                                <p>Instruction tuning, RAG, chat, and multimodal tests mirror real workloads.</p>
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                    <div class="btn-row">
                        <a class="btn btn--ghost" href="{{ '/benchmarks/' | relative_url }}">Benchmark details</a>
                        <a class="btn btn--ghost" href="{{ '/docs/benchmarks/' | relative_url }}">View raw results</a>
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        <div class="section-block section-block--panel">
            <div class="metric-card">
                <p class="metric-card__label">Llama3.2 3B @ 4-bit</p>
                <p class="metric-card__value">72 tok/s</p>
                <p class="metric-card__desc">Ryzen™ AI 9 HX 370 · 8 ms median latency</p>
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            <div class="metric-card">
                <p class="metric-card__label">Gemma 3 4B Vision</p>
                <p class="metric-card__value">18 fps</p>
                <p class="metric-card__desc">Vision + text pipeline on XDNA2 with shared memory.</p>
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            <div class="metric-card">
                <p class="metric-card__label">Power draw</p>
                <p class="metric-card__value">9.6 W</p>
                <p class="metric-card__desc">Full assistant stack vs ~45 W GPU baseline.</p>
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