Rolv Heggenhougen
Rolv Heggenhougen @rolveitrem ยท
Imagine buying an #Intel CPU and getting better latency than a Blackwell #B200. ROLV.ai software acceleration just turned a standard CPU into a Maverick MoE monster: ๐Ÿ“‰ TTFT: 0.116s (Beats B200 Dense) โšก๏ธ Energy Savings: 98.5%๐Ÿ’ฐ Cost: 1/80th of a B200 Hardware tells you the speed limit, but ROLV finds the shortcut. ๐Ÿš€ #AIInference #TechTrends #Llama4 #ROLVSPARSE
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Rolv Heggenhougen
Rolv Heggenhougen @rolveitrem ยท
ROLV.ai just crushed the #Qwen2.5-72B-Instruct MoE expert FFN (8192ร—28672, batch 512) on a single #NVIDIA #B200: โ€ข 50.6ร— speedup (4962% faster) โ€ข 95.8% energy savings โ€ข Per-iter: 0.000080 s vs 0.004027 s โ€ข TFLOPS: 3,023.7 vs 59.7 โ€ข Energy: 31 J vs 748 J ROLV_norm_hash: 8dbe5f139fd946d4cd84e8cc612cd9f68cbc87e394457884acc0c5dad56dd8dd Real production MoE slice. No synthetic data. Full report + hashes: rolv.ai #ROLV #MoE #Qwen #AIInference #B200
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Rolv Heggenhougen
Rolv Heggenhougen @rolveitrem ยท
On a #B200, ROLV.AI turns a 70B FFN from 142k tokens/s into 7.2M tokens/s at 50% sparsityโ€”50ร— faster, 98% less energy, same answer.
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Rolv Heggenhougen
Rolv Heggenhougen @rolveitrem ยท
๐Ÿš€ ROLV Mistral-7B Wanda Benchmark โ€” NVIDIA #NVIDIA #B200 | 1000 iters Loading Mistral-7B-v0.1 ... Loadingโ€‡weights:โ€‡100% 291/291โ€‡[00:21<00:00,โ€‡18.20it/s,โ€‡Materializingโ€‡param=model.norm.weight] Layer shape: torch.Size([4096, 14336]) Applying Wanda-style pruning to 55% sparsity... Final sparsity: 55.00% โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• NUMERICAL VERIFICATION (ROLV vs Dense) โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• Max abs diff : 0.062527 Mean abs diff: 0.005341 Within tolerance (< 0.1) : โœ“ YES โ€” safe for production โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• FINAL BENCHMARK SUMMARY โ€” MISTRAL-7B WANDA (JASON v3.8 โ€” NVIDIA) โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• Matrix size : 4,096 ร— 14,336 Non-zeros / Sparsity : 26,424,092 / 55.0000% Build time (ROLV kernel) : 0.0015 s Dense (cuBLAS) time per iter : 0.009669 s Sparse (cuSPARSE) time per iter : 0.052953 s ROLV time per iter : 0.000247 s Vendor Best Baseline ( cuBLAS) : 0.009669 s Speedup vs Vendor Best : 39.1x (+3808%) Energy savings vs Vendor Best : 97.4% ROLV Hash : 6fa61d3bd3a1cf870ea44b59df5e7455523ac4f4ef23e5b4e965357261a02d71 === ROLV ULTIMATE FINAL HARNESS โ€” MI300X GPU === AMD ROCm detected โ†’ clean + exact versions Loading Mistral-7B-v0.1 on CPU (safe)... #AMD #MI300X Moving layer to MI300X GPU... โœ… Layer loaded on cuda:0 (MI300X GPU) Applying Wanda-style pruning to 55% sparsity... Final sparsity: 55.00% NUMERICAL VERIFICATION (ROLV vs Dense) Max abs diff : 0.058121 Mean abs diff: 0.005342 Within tolerance (< 0.1) : โœ“ YES โ€” safe for production FINAL BENCHMARK SUMMARY โ€” MISTRAL-7B WANDA (ROLV JASON v3.8 โ€” AMD MI300X) โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ• Matrix size : 4,096 ร— 14,336 Non-zeros / Sparsity : 26,424,092 / 55.0000% Build time (ROLV kernel) : 0.0294 s Dense (rocBLAS) time per iter : 0.007448 s Sparse (rocSPARSE) time per iter : 0.179283 s ROLV time per iter : 0.000470 s Vendor Best Baseline : 0.007448 s Speedup vs Vendor Best : 15.8x (+1484%) Energy savings vs Vendor Best : 93.7% ROLV Hash : 6fa61d3bd3a1cf870ea44b59df5e7455523ac4f4ef23e5b4e965357261a02d71 โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•โ•
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Simran Kaur
Simran Kaur @KaurSimran24381 ยท
เจฆเฉเจจเฉ€เจ† เจฆเฉ€เจ†เจ‚ เจธเจญ เจคเฉ‹เจ‚ เจฎเจนเจฟเฉฐเจ—เฉ€เจ†เจ‚ AI เจšเจฟเจชเจธ เจฆเจพ เจ–เฉเจฒเจพเจธเจพ NVIDIA เจฆเฉ€เจ†เจ‚ B200โ€“GB200 เจจเฉ‡ เจฎเจšเจพเจ‡เจ† เจคเจนเจฒเจ•เจพ dainiksavera.com/gb200-most-expโ€ฆ #NVIDIA #AIChips #B200 #GB200 #HighPerformanceComputing #TechNews #AIInnovation
เจฆเฉเจจเฉ€เจ† เจฆเฉ€เจ†เจ‚ เจธเจญ เจคเฉ‹เจ‚ เจฎเจนเจฟเฉฐเจ—เฉ€เจ†เจ‚ AI เจšเจฟเจชเจธ เจฆเจพ เจ–เฉเจฒเจพเจธเจพ NVIDIA เจฆเฉ€เจ†เจ‚ B200โ€“GB200 เจจเฉ‡ เจฎเจšเจพเจ‡เจ† เจคเจนเจฒเจ•เจพ | Dainik Savera...

เจฆเฉเจจเฉ€เจ† เจฆเฉ€เจ†เจ‚ เจธเจญ เจคเฉ‹เจ‚ เจฎเจนเจฟเฉฐเจ—เฉ€เจ†เจ‚ AI เจšเจฟเจชเจธ เจฆเจพ เจ–เฉเจฒเจพเจธเจพ NVIDIA เจฆเฉ€เจ†เจ‚ B200โ€“GB200 เจจเฉ‡ เจฎเจšเจพเจ‡เจ† เจคเจนเจฒเจ•เจพ

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