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FINAL-Bench/Ourbox-35B-JGOS-GGUF

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FINAL-Bench/VKUE

reacted to SeaWolf-AI's post with ❤️ 4 days ago

🔵 VKUE — No GPU? Runs anyway. "Frontier models need a datacenter GPU" rests on a hidden assumption: that the model reads ALL its parameters every token. Decode is memory-bandwidth bound — sweep 34B params/token and an 8 GB card dies at 1–2 tok/s. So we ran ONE 34.7B reasoning model — Ourbox-35B-JGOS, a sparse Mixture-of-Experts — as the identical weights across the whole hardware spectrum. All measured: • B200: 18,057 tok/s (aggregate) • 1× A10G: 126 tok/s • 8 GB laptop (RTX 5060): 20 tok/s • GPU-less CPU: 17 tok/s Why it works: Ourbox holds 34.7B params but only ~3B are active per token (256 experts, top-8). Since decode is bandwidth-bound, a dense 34B moves ~16.7 GB/token while Ourbox moves ~1.45 GB — ~11× less traffic. Put the experts in system RAM, keep attention/router/shared on the GPU, and a 34.7B reasoner runs on an 8 GB laptop — or no GPU at all. Sparsity alone, proven (same laptop, same quant, ~same footprint): Ourbox-35B (A3B) 20.01 tok/s vs Qwen2.5-32B (dense) 5.36 → 3.7× from sparsity alone, ~2× the best dense-32B on any 8 GB machine. Not a toy: GPQA Diamond 86.4% (maj@8). Try it live (same prompt, GPU vs GPU-less CPU, live tok/s). Honest scope: one machine's measurements; the CPU path proves it RUNS without a GPU, not that it beats one. 📝 Article: https://huggingface.co/blog/FINAL-Bench/vkue 🔵 GPU vs CPU demo: https://final-bench-ourbox-35b-vkue-demo.hf.space/ 🔵 CPU-only demo: https://final-bench-ourbox-35b-vkue-cpu.hf.space 📊 VKUE leaderboard: https://huggingface.co/spaces/FINAL-Bench/VKUE 🤗 Model: https://huggingface.co/FINAL-Bench/Ourbox-35B-JGOS-GGUF ⚡ VKAE (speed): https://huggingface.co/spaces/VIDraft/vkae VKUE is the "runs anywhere" side of our serving line; VKAE the "fast on datacenter GPUs" side. VKAE is fast; VKUE is everywhere.

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FINAL-Bench/Ourbox-35B-JGOS-GGUF

Text Generation • 35B • Updated 2 days ago • 1.42k • 26

liked a Space 4 days ago

VKUE Efficiency Leaderboard

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Serve frontier-class models on minimal hardware — VKUE

reacted to SeaWolf-AI's post with ❤️ 4 days ago

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5320

🔵 VKUE — No GPU? Runs anyway.

"Frontier models need a datacenter GPU" rests on a hidden assumption: that the model reads ALL its parameters every token. Decode is memory-bandwidth bound — sweep 34B params/token and an 8 GB card dies at 1–2 tok/s.

So we ran ONE 34.7B reasoning model — Ourbox-35B-JGOS, a sparse Mixture-of-Experts — as the identical weights across the whole hardware spectrum. All measured:

• B200: 18,057 tok/s (aggregate)
• 1× A10G: 126 tok/s
• 8 GB laptop (RTX 5060): 20 tok/s
• GPU-less CPU: 17 tok/s

Why it works: Ourbox holds 34.7B params but only ~3B are active per token (256 experts, top-8). Since decode is bandwidth-bound, a dense 34B moves ~16.7 GB/token while Ourbox moves ~1.45 GB — ~11× less traffic. Put the experts in system RAM, keep attention/router/shared on the GPU, and a 34.7B reasoner runs on an 8 GB laptop — or no GPU at all.

Sparsity alone, proven (same laptop, same quant, ~same footprint): Ourbox-35B (A3B) 20.01 tok/s vs Qwen2.5-32B (dense) 5.36 → 3.7× from sparsity alone, ~2× the best dense-32B on any 8 GB machine. Not a toy: GPQA Diamond 86.4% (maj@8).

Try it live (same prompt, GPU vs GPU-less CPU, live tok/s). Honest scope: one machine's measurements; the CPU path proves it RUNS without a GPU, not that it beats one.

📝 Article: https://huggingface.co/blog/FINAL-Bench/vkue
🔵 GPU vs CPU demo: https://final-bench-ourbox-35b-vkue-demo.hf.space/
🔵 CPU-only demo: https://final-bench-ourbox-35b-vkue-cpu.hf.space
📊 VKUE leaderboard: FINAL-Bench/VKUE
🤗 Model: FINAL-Bench/Ourbox-35B-JGOS-GGUF
⚡ VKAE (speed): VIDraft/vkae

VKUE is the "runs anywhere" side of our serving line; VKAE the "fast on datacenter GPUs" side. VKAE is fast; VKUE is everywhere.

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reacted to SeaWolf-AI's post with 🔥 11 days ago

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5158

🔓 We ran genuine quantum key-recovery on 'real IBM quantum hardware' — and pushed the frontier well past the largest hardware demos we're aware of (which sat at N=4).

Using Simon's algorithm on ibm_kingston, we recovered the secret key of two symmetric-cipher structures:
• Even–Mansour — N=5 → N=10
• 3-round Feistel (DES-family) — block 6 → 8

Each verified against an 'independent control key', using error mitigation only (no QEC).

🧭 Honest scope: this is not a quantum speedup (the effective difficulty tracks the classical birthday bound ~2^{n/2}), not a break of real AES/RSA, and not 16-round DES (ours is 3-round). The recovery method is reserved for a forthcoming paper; formal record status is pending peer review.

📄 Write-up: https://huggingface.co/blog/FINAL-Bench/quantum
🕹️ Try it live in your browser: https://vidraft-quantumos.hf.space/crypto
🏆 Leaderboard: FINAL-Bench/quantum-bench-leaderboard

#quantum #cryptography #quantumcomputing