Strata - Qwen 3.8 Flash Next - boosting decoding speed Tip

Reddit r/LocalLLaMA Tools

Summary

A user shares benchmarks showing that raising the --adapt-every parameter from 1 to 6 in Strata boosts token generation speed by 9-55% across multiple Qwen 3.8 Flash Next prompts, with the largest gains on long-context code descriptions.

I've got some nice boost by increasing --adapt-every value from calibrated 1 to 6. I tested on 4 consecutive prompts(Slovak language) in chat. Prompt 1-3 are simple questions, 4. is describing source code(20K tokens) Results: [prompt number / ~avg. output length] UD-Q4_K_XL: [1 / 996] 30.3 -> 36.1 t/s (+19%) [2 / 473] 31.6 -> 35.4 t/s (+12%) [3 / 1245] 32.6 -> 40.1 t/s (+23%) [4 / 4148] 27.8 -> 43.1 t/s (+55%) UD-IQ4_XS: [1 / 947] 57.9 -> 63.1 t/s (+9%) [2 / 565] 51.9 -> 64.3 t/s (+24%) [3 / 1162] 57.6 -> 71.6 t/s (+24%) [4 / 4881] 56.0 -> 65.3 t/s (+16%) PC: Intel [email protected], RTX 4070 ti super, 64GB DDR4, NVMe, Arch linux TTY
Original Article

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Strata with Qwen3.8 Flash Next UD-Q4_K_XL

Reddit r/LocalLLaMA

A user reports strong single-request decode speeds running Qwen3.8-Flash-Next in Unsloth's UD-Q4_K_XL quant on Strata with an RTX PRO 6000, showing roughly 2x faster throughput than vLLM on the same GPU across prose, counting, coding, and reasoning tasks.