alt Hacker NewsI was able to pretty easily reproduce parts of this paper at Neuralwatt Cloud's SoA models where we do all our optimizations and pricing by energy.
Accuracy uses the paper's strict "#### N" parser and lenient fallback. acc is strict/lenient percent. len_cap is the number of responses truncated at the generation-length limit, not an energy cap. Each language has 50 responses.
nw-flash (DeepSeek V4.1 Flash 1017)
Language J/req xEN tok/req J/tok acc len_cap
English 23.4 1.00 174 0.134 100/100 0
Chinese 16.8 0.72 389 0.043 96/96 0
Russian 18.8 0.80 352 0.054 96/96 0
French 25.1 1.07 272 0.092 98/98 0
S. Pashto 69.2 2.96 752 0.092 92/92 1
Tigrinya 65.3 2.79 2299 0.028 0/76 8
Shan 98.5 4.21 2823 0.035 48/54 14
Tibetan 42.3 1.81 2227 0.019 0/76 9
nw-small (Qwen3.8-27B-FP8)
Language J/req xEN tok/req J/tok acc len_cap
English 62.2 1.00 300 0.207 100/100 0
Chinese 154.1 2.48 605 0.255 94/94 1
Russian 147.7 2.37 576 0.257 96/96 1
French 163.3 2.62 323 0.506 98/98 0
S. Pashto 777.2 12.49 1510 0.515 84/84 6
Tigrinya 823.8 13.24 2458 0.335 0/76 10
Shan 1407.4 22.62 3422 0.411 48/52 20
Tibetan 1455.5 23.39 3199 0.455 0/56 20
nw-large (Kimi K3.1 Pro)
Language J/req xEN tok/req J/tok acc len_cap
English 169.5 1.00 391 0.434 100/100 0
Chinese 113.8 0.67 491 0.232 98/98 0
Russian 78.7 0.46 527 0.150 98/98 0
French 298.3 1.76 406 0.735 98/98 0
S. Pashto 119.7 0.71 903 0.133 96/96 0
Tigrinya 1096.6 6.47 1870 0.586 38/86 2
Shan 503.6 2.97 2895 0.174 52/52 12
Tibetan 593.2 3.50 2298 0.258 0/82 4
Endpoint Output tokens Energy/request J/output token
nw-flash 6.83x 3.27x 0.48x
nw-small 5.87x 8.46x 1.44x
nw-large 4.39x 3.50x 0.80xReplies
scottcha • today at 3:39 PM
I actually think beyond the large increase in energy noted in the paper the decrease in energy for Russian and Chinese was a bit unexpected. I would have actually expected the Chinese DBCS to use more energy but at least for Chinese there could be a training impact here given these are Chinese models.
Thanks for this table! Do you happen to have the fraction of accepted speculative decoding outputs as well? I know it is asking much, but do you possibly measure differences in different across speculation techniques (dspark vs dflash) and differences in framework/code used (unless you only use in house code for these and cannot run sglang or vllm)? Your table is more interesting than the blog for sure.