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| <div class="wrap"> | |
| <p class="eyebrow">VIDRAFT · Unified Inference Engine</p> | |
| <h1 class="wordmark">VKIE <span class="nick">비키</span><small>VIDRAFT Kernel Inference Engine</small></h1> | |
| <p class="tagline"><b>VKAE</b> accelerates. <b>VKUE</b> saves. <b>VKIE</b> maximizes serving. One 34.7B model, from a datacenter GPU down to a free CPU — <b>every number measured</b>, every demo live.</p> | |
| <!-- PROBLEM --> | |
| <h2>The problem</h2> | |
| <p class="h2sub">Speed, cost, and scale pull against each other — win one, usually lose two.</p> | |
| <div class="band"> | |
| <div class="icard"><div class="ci" style="background:var(--vkae-soft);color:var(--vkae)">⚡</div><h4>Speed</h4><p>Slow inference is unusable — but going fast normally means an expensive datacenter GPU.</p></div> | |
| <div class="icard"><div class="ci" style="background:var(--vkue-soft);color:var(--vkue)">💸</div><h4>Cost</h4><p>Serving a large model means a multi-GPU cluster. With GPU shortages, you often can't even buy them.</p></div> | |
| <div class="icard"><div class="ci" style="background:var(--vkie-soft);color:var(--vkie)">📈</div><h4>Scale</h4><p>Model and hardware get locked in — no single engine spans datacenter down to edge / on-prem.</p></div> | |
| </div> | |
| <!-- 3 AXES --> | |
| <div class="axes"> | |
| <div class="axis vkae"> | |
| <div class="ico">🏎️</div><h3>VKAE</h3><p class="role">GPU acceleration · the sports car</p> | |
| <div class="big">~9× faster</div><div class="lab">single-stream, 1×B200 (24 → 220 tok/s)</div> | |
| </div> | |
| <div class="axis vkue"> | |
| <div class="ico">🚗</div><h3>VKUE</h3><p class="role">GPU savings · the compact car</p> | |
| <div class="big">runs on FREE CPU</div><div class="lab">34.7B at ~6-7 tok/s, zero GPU</div> | |
| </div> | |
| <div class="axis vkie"> | |
| <div class="ico">🚄</div><h3>VKIE (비키)</h3><p class="role">accel + savings = max serving · the train</p> | |
| <div class="big">18,057 tok/s</div><div class="lab">1×B200 serving capacity (aggregate)</div> | |
| </div> | |
| </div> | |
| <!-- BEFORE / AFTER --> | |
| <h2>Before → After · measured</h2> | |
| <p class="h2sub">Each axis, on the <b>same hardware</b>, same 34.7B model — what our engine changes.</p> | |
| <div class="ba"> | |
| <div class="bacard vkae"> | |
| <div class="t">🏎️ VKAE — acceleration</div> | |
| <div class="cond">single-stream · 1×B200</div> | |
| <div class="barow"> | |
| <div class="bacol before"><div class="k">Before</div><div class="v">24</div></div> | |
| <div class="arrow">→</div> | |
| <div class="bacol after"><div class="k">After</div><div class="v">220</div></div> | |
| </div> | |
| <div class="mult">≈ 9× faster</div> | |
| <div class="mnote">baseline serving → VIDRAFT optimized (tok/s)</div> | |
| </div> | |
| <div class="bacard vkue"> | |
| <div class="t">🚗 VKUE — savings</div> | |
| <div class="cond">same 8 GB laptop · dense → sparse</div> | |
| <div class="barow"> | |
| <div class="bacol before"><div class="k">Before</div><div class="v">5.4</div></div> | |
| <div class="arrow">→</div> | |
| <div class="bacol after"><div class="k">After</div><div class="v">20.0</div></div> | |
| </div> | |
| <div class="mult">≈ 3.7× faster</div> | |
| <div class="mnote">dense 32B → sparse A3B, identical hardware (tok/s)</div> | |
| </div> | |
| <div class="bacard vkie"> | |
| <div class="t">🚄 VKIE — serving capacity</div> | |
| <div class="cond">1×B200 · single → optimized concurrent</div> | |
| <div class="barow"> | |
| <div class="bacol before"><div class="k">Before</div><div class="v">24</div></div> | |
| <div class="arrow">→</div> | |
| <div class="bacol after"><div class="k">After</div><div class="v">18,057</div></div> | |
| </div> | |
| <div class="mult">≈ 750× serving capacity</div> | |
| <div class="mnote">naive single request → VKIE concurrent serving (aggregate tok/s)</div> | |
| </div> | |
| </div> | |
| <!-- COMPARISON --> | |
| <h2>Three axes at a glance</h2> | |
| <p class="h2sub">Same 34.7B model, same principles — three optimization targets.</p> | |
| <div class="tablecard"><div class="scroll"><table> | |
| <thead><tr><th></th><th>🏎️ VKAE</th><th>🚗 VKUE</th><th>🚄 VKIE</th></tr></thead> | |
| <tbody> | |
| <tr><td>Focus</td><td>Speed</td><td>Savings</td><td>Unified · throughput</td></tr> | |
| <tr><td>Best hardware</td><td>Datacenter GPU</td><td>CPU ~ small GPU</td><td>Full range</td></tr> | |
| <tr><td>Strength</td><td>Top single-stream speed</td><td>Lowest cost · accessibility</td><td>Max tok/s · cost-efficiency</td></tr> | |
| <tr><td>Measured before→after</td><td class="num">24→220 (9×)</td><td class="num">5.4→20 (3.7×)</td><td class="num">24→18,057 (750×)</td></tr> | |
| <tr class="peak"><td>In a phrase</td><td>fastest</td><td>cheapest</td><td>most</td></tr> | |
| </tbody> | |
| </table></div></div> | |
| <!-- SPECTRUM --> | |
| <h2>One model, the whole spectrum · measured</h2> | |
| <p class="h2sub"><b>Ourbox-35B-JGOS</b> — 34.7B total / ~3B active MoE. Same weights, only the hardware changes.</p> | |
| <div class="tablecard"><div class="scroll"><table> | |
| <thead><tr><th>Hardware</th><th class="r">Measured tok/s</th><th>Axis</th></tr></thead> | |
| <tbody> | |
| <tr class="peak"><td>1× B200 (datacenter)</td><td class="r num">18,057</td><td>VKIE ceiling · aggregate (256 concurrent)</td></tr> | |
| <tr><td>1× A10G (cloud GPU)</td><td class="r num">126</td><td>VKUE · single-stream</td></tr> | |
| <tr><td>8 GB gaming laptop (RTX 5060)</td><td class="r num">20.0</td><td>VKUE · 3.7× a dense 32B</td></tr> | |
| <tr><td>CPU-Upgrade (8 vCPU, no GPU)</td><td class="r num">~17</td><td>VKUE</td></tr> | |
| <tr class="free"><td>FREE CPU space (2 vCPU, no GPU)</td><td class="r num">~6-7</td><td>VKUE floor · zero cost</td></tr> | |
| </tbody> | |
| </table></div></div> | |
| <p class="h2sub" style="margin-top:14px">Quality holds across every tier — <b>GPQA Diamond 86.4%</b> (Ourbox-35B, maj@8) up to <b>90.9%</b> (Darwin-398B). Multimodal: Janus-Pro-1B image generation on a low-cost T4 in <b>~28 s/image</b> (fp16, 2.1× our first cut).</p> | |
| <!-- WHY IT MATTERS --> | |
| <h2>Why it matters</h2> | |
| <p class="h2sub">Breaking the rule that "big AI needs big money."</p> | |
| <div class="band"> | |
| <div class="icard"><div class="ci" style="background:var(--vkie-soft);color:var(--vkie)">🏛️</div><h4>Sovereign AI</h4><p>Public sector, defense, healthcare, finance — data that can't touch the cloud. Frontier reasoning on an air-gapped on-prem CPU.</p></div> | |
| <div class="icard"><div class="ci" style="background:var(--vkue-soft);color:var(--vkue)">💰</div><h4>Cost collapse</h4><p>From a multi-hundred-thousand-dollar GPU cluster to a ~$1,600 card — or a free CPU. Entry cost drops by orders of magnitude.</p></div> | |
| <div class="icard"><div class="ci" style="background:var(--vkae-soft);color:var(--vkae)">🌍</div><h4>Accessibility</h4><p>Individuals, startups, SMBs, public bodies — anyone. Ready for the surge in on-device and edge demand.</p></div> | |
| </div> | |
| <!-- LIVE TESTS (reuse existing spaces) --> | |
| <h2>Live tests · try it yourself</h2> | |
| <p class="h2sub">These run on their <b>real hardware</b> (not this page). Click a card to open, or load one on screen below.</p> | |
| <div class="tests"> | |
| <a class="tcard" href="https://huggingface.co/spaces/FINAL-Bench/Ourbox-35B-VKUE-Demo" target="_blank" rel="noopener"><div class="em">🟢</div><div class="tt">34.7B on one A10G</div><div class="dd">Normally an H100 job — VKUE runs it on a 24 GB A10G, live tok/s.</div><div class="go">Open ↗</div></a> | |
| <a class="tcard" href="https://huggingface.co/spaces/FINAL-Bench/Ourbox-35B-VKUE-CPU" target="_blank" rel="noopener"><div class="em">🔵</div><div class="tt">CPU only · no GPU</div><div class="dd">34.7B on 8 vCPU, zero GPU.</div><div class="go">Open ↗</div></a> | |
| <a class="tcard" href="https://huggingface.co/spaces/FINAL-Bench/Ourbox-35B-VKUE-FreeCPU" target="_blank" rel="noopener"><div class="em">🆓</div><div class="tt">FREE CPU (~6-7 tok/s)</div><div class="dd">34.7B on HuggingFace's free CPU tier.</div><div class="go">Open ↗</div></a> | |
| <a class="tcard" href="https://huggingface.co/spaces/FINAL-Bench/Janus-Pro-Image-T4" target="_blank" rel="noopener"><div class="em">🖼️</div><div class="tt">Image gen (T4)</div><div class="dd">Autoregressive image on a low-cost GPU.</div><div class="go">Open ↗</div></a> | |
| <a class="tcard" href="https://huggingface.co/spaces/VIDraft/vkae" target="_blank" rel="noopener"><div class="em">🏎️</div><div class="tt">VKAE — speed</div><div class="dd">Datacenter throughput leaderboard.</div><div class="go">Open ↗</div></a> | |
| <a class="tcard" href="https://huggingface.co/spaces/FINAL-Bench/VKUE" target="_blank" rel="noopener"><div class="em">🚗</div><div class="tt">VKUE — efficiency</div><div class="dd">Minimal-hardware leaderboard.</div><div class="go">Open ↗</div></a> | |
| </div> | |
| <div class="embed"> | |
| <div class="eh"><b>🆓 Load VKUE on screen — 34.7B on a FREE CPU (no GPU)</b><button class="loadbtn" id="b1">▶ Load live</button></div> | |
| <div class="ph" id="p1">Click "Load live" to embed the free-CPU demo here (34.7B, ~6-7 tok/s, zero GPU).</div> | |
| </div> | |
| <div class="embed"> | |
| <div class="eh"><b>🟢 Load VKUE on screen — 34.7B on one A10G GPU</b><button class="loadbtn" id="b2">▶ Load live</button></div> | |
| <div class="ph" id="p2">Click "Load live" to embed the A10G demo — 34.7B, which normally needs an H100 (wakes the A10G, ~1 min).</div> | |
| </div> | |
| <!-- BADGES --> | |
| <h2>All links</h2> | |
| <div class="badges"> | |
| <a class="badge" href="https://huggingface.co/spaces/VIDraft/vkae" target="_blank" rel="noopener">🏎️ <b>VKAE</b> Speed</a> | |
| <a class="badge" href="https://huggingface.co/spaces/FINAL-Bench/VKUE" target="_blank" rel="noopener">🚗 <b>VKUE</b> Efficiency</a> | |
| <a class="badge" href="https://huggingface.co/spaces/FINAL-Bench/Ourbox-35B-VKUE-Demo" target="_blank" rel="noopener">🟢 A10G · 34.7B</a> | |
| <a class="badge" href="https://huggingface.co/spaces/FINAL-Bench/Ourbox-35B-VKUE-CPU" target="_blank" rel="noopener">🔵 CPU-only</a> | |
| <a class="badge" href="https://huggingface.co/spaces/FINAL-Bench/Ourbox-35B-VKUE-FreeCPU" target="_blank" rel="noopener">🆓 FREE CPU</a> | |
| <a class="badge" href="https://huggingface.co/spaces/FINAL-Bench/Janus-Pro-Image-T4" target="_blank" rel="noopener">🖼️ Image (T4)</a> | |
| <a class="badge" href="https://huggingface.co/FINAL-Bench/Ourbox-35B-JGOS-GGUF" target="_blank" rel="noopener">🤗 Model</a> | |
| <a class="badge" href="https://huggingface.co/blog/FINAL-Bench/vkue" target="_blank" rel="noopener">📝 Blog</a> | |
| </div> | |
| <div class="note"> | |
| <b>Honest scope.</b> Every figure above is our own measurement, reproducible on the linked demos. The <i>"before"</i> baselines use standard open tooling; the <i>"after"</i> numbers come from VIDRAFT's optimized serving — the resulting speed and hardware are public, the engine internals are proprietary. VKAE/VKIE serving numbers are on a single B200; VKUE numbers are on the exact consumer/CPU hardware named. No claim that a CPU beats a GPU — VKUE's point is that the model <i>runs</i> where a GPU isn't available. | |
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| <span>FINAL-Bench · VIDRAFT</span> | |
| <span>VKIE (비키) · VKAE fast · VKUE anywhere · VKIE most</span> | |
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