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NInfer boosts local LLM performance to 220 tokens/sec on 5090 GPU

A user on Reddit's r/LocalLLaMA subreddit shared their positive experience using NInfer with a 5090 GPU to run a 27 billion parameter model. They reported achieving significantly higher throughput, with speeds averaging 170-220 tokens per second, which they noted was more than double what they experienced with llama.cpp. The user detailed their specific setup, including the command used and various parameters for model serving, context length, and quantization. AI

IMPACT Demonstrates significant performance gains for running large language models locally, potentially improving accessibility and usability for individuals.

RANK_REASON User-reported performance improvement for a specific local LLM setup.

Read on r/LocalLLaMA →

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

NInfer boosts local LLM performance to 220 tokens/sec on 5090 GPU

How we ranked this

Signal score
4 / 100
Composite score across the factors below. Higher = stronger signal that this story matters right now.
Newsworthiness bucket
Tool
User-reported performance improvement for a specific local LLM setup.
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
infra, product
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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 English(EN) · /u/Rollingsound514 ·

    Ninfer and a 5090 with 3.8 27B is making me cry tears of joy it's so good.

    <!-- SC_OFF --><div class="md"><p>Built the latest and I'm getting as much as 220 tokens per second and averaging in the 170s, I can't get over it.</p> <p>If anyone on here is on that project, fuckkkin' chapeau man, really incredible job. I can't believe I was able to like double…