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DeepSeek-V4.1-Flash achieves 39 tok/s on code with user optimizations · 1 source tracked

By PulseAugur Editorial · [1 sources] ·

A user on Reddit shared performance benchmarks for the DeepSeek-V4.1-Flash model, specifically the UD-Q4_K_XL quantization, running on a powerful hardware setup including an AMD EPYC CPU and four RTX 3090 GPUs. The user detailed specific command-line optimizations and configurations, such as loading the entire model into RAM and utilizing DSpark for speculative decoding. These optimizations resulted in significant speed improvements, with prose generation reaching up to 24.6 tokens per second and code generation hitting 39.3 tokens per second. AI

IMPACT Demonstrates potential performance gains through user-driven optimization and hardware configuration for local LLM deployments.

RANK_REASON User-shared performance benchmarks and optimization tips for a specific model quantization on custom hardware.

Read on r/LocalLLaMA →

AI-generated summary · Google Gemini · from 1 sources. How we write summaries →

DeepSeek-V4.1-Flash achieves 39 tok/s on code with user optimizations · 1 source tracked

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User-shared performance benchmarks and optimization tips for a specific model quantization on custom hardware.
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COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/C0smo777 ·

    DeepSeek-V4.1-Flash (UD-Q4_K_XL) on 4x 3090 + EPYC: 12 → 25 tok/s prose, 39 tok/s code.

    <!-- SC_OFF --><div class="md"><p>Hi all, just wanted to share some benchmarks. I got Unsloth's UD-Q4_K_XL quant of DeepSeek-V4.1-Flash-1017 running and spent some time tuning it. Some of what I found isn't in the guide, so here are my notes.</p> <h2>Hardware</h2> <ul> <li>EPYC 9…

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