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๐
In a Training Loop
86.7
TFLOPS
Joel Wang
joelhenwang
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๐ต VKUE โ 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 โ sweep 34B params/token and an 8 GB card dies at 1โ2 tok/s. So we ran ONE 34.7B reasoning model โ Ourbox-35B-JGOS, a sparse Mixture-of-Experts โ as the identical weights across the whole hardware spectrum. All measured: โข B200: 18,057 tok/s (aggregate) โข 1ร A10G: 126 tok/s โข 8 GB laptop (RTX 5060): 20 tok/s โข 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 โ ~11ร 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 โ 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 โ 3.7ร from sparsity alone, ~2ร 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. ๐ Article: https://huggingface.co/blog/FINAL-Bench/vkue ๐ต GPU vs CPU demo: https://final-bench-ourbox-35b-vkue-demo.hf.space/ ๐ต CPU-only demo: https://final-bench-ourbox-35b-vkue-cpu.hf.space ๐ VKUE leaderboard: https://huggingface.co/spaces/FINAL-Bench/VKUE ๐ค Model: https://huggingface.co/FINAL-Bench/Ourbox-35B-JGOS-GGUF โก 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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LiquidAI/antidoom-mix-v1.0
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about 18 hours ago
mlabonne/lfm25-350m-struct
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joelhenwang/OdinNext-138M-Instruct
Text Generation
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0.1B
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Updated
Jun 9
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160
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6
joelhenwang/OdinNext-138M-Base
Text Generation
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0.1B
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Updated
Jun 9
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55
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2
joelhenwang/OdinNext-138M-Early-Checkpoint
Text Generation
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0.2B
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Updated
May 28
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9
datasets
1
joelhenwang/comt
Preview
โข
Updated
Feb 27
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27