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DeepSeek V4.1 Flash 1017 EXL3 2.9bpw benchmarked against APEX 2025

A user has benchmarked the DeepSeek V4.1 Flash 1017 EXL3 2.9bpw model, finding its performance comparable to state-of-the-art models from six months prior. Using the APEX 2025 dataset as a benchmark, the user achieved approximately 1000 tokens per second for prefill and 75 tokens per second for decoding on a system with six 3090 GPUs. The user shared these results to assist others in benchmarking their own setups. AI

IMPACT Provides performance insights for a specific model quantization, aiding users in evaluating its capabilities.

RANK_REASON User-generated benchmark results for a specific model variant. [lever_c_demoted from research: ic=1 ai=1.0]

Read on r/LocalLLaMA →

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

DeepSeek V4.1 Flash 1017 EXL3 2.9bpw benchmarked against APEX 2025

How we ranked this

Signal score
18 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
User-generated benchmark results for a specific model variant. [lever_c_demoted from research: ic=1 ai=1.0]
Source corroboration
Single-source cluster
Only one publisher covered this so far. Single-source stories can still rank when the publisher is high-authority, but they lack cross-source corroboration.
Topics
model release, product
Editorial topic classification. Feeds into how the story surfaces on /topic/<slug> hub pages and into the per-entity coverage mix.
AI-industry relevance
High
Clearly on-topic for AI-industry coverage.
Story freshness
Breaking (< 6h)
Fresh story with cross-source coverage still developing. Ranking may shift as more sources report.

Full methodology in our editorial standards.

COVERAGE [1]

  1. r/LocalLLaMA TIER_1 Nederlands(NL) · /u/hp1337 ·

    DeepSeek V4.1 Flash 1017 exl3 2.9bpw APEX 2025 benchmark results

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