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DeepSeek-V4 Flash model optimized for faster prefill on consumer hardware

A user on Reddit's r/LocalLLaMA subreddit shared their experience running the DeepSeek-V4 Flash model on an M-5 Air with 32GB of RAM. They detailed optimizations for faster prefill speeds, achieving around 50 tokens per second, though decode speeds were significantly slower at approximately 1 token per second. The user also experimented with running fewer experts than the default during prefill, finding that it did not always catastrophically impact performance and could retain capabilities like needle-in-a-haystack accuracy. AI

IMPACT Demonstrates user-driven optimization techniques for running large models on limited hardware, potentially improving accessibility.

RANK_REASON User-level optimization and discussion of a model's performance on consumer hardware, rather than a formal release or benchmark.

Read on r/LocalLLaMA →

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

DeepSeek-V4 Flash model optimized for faster prefill on consumer hardware

COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/maddie-lovelace ·

    DeepSeek v4 Flash 0731 4bit ~50tps prefill, ~1tps decode on M5 Air 32gb

    <!-- SC_OFF --><div class="md"><p>Currently running some experiments using the streamed experts trick that's been floating around this sub as well as some of my own trickery to get prefill to run a bit faster. It's been quite a bit of fun so far - just getting a 300b model to run…