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Consumer GPUs achieve high LLM speeds with 122B model running at 37 t/s

By PulseAugur Editorial · [1 sources] ·

A user on Reddit's r/LocalLLaMA subreddit shared impressive benchmarks for running large language models on consumer hardware. They achieved 206 tokens per second with a 35 billion parameter model (35b a3b) using an Nvidia RTX 4090 and an RTX 5060 Ti, with some layers offloaded to 64GB of system RAM. Even more remarkably, a 122 billion parameter model (barium-122) was run at 37 tokens per second under similar conditions, exceeding the user's optimistic expectations. AI

IMPACT Demonstrates significant advancements in running large language models on consumer-grade hardware, potentially lowering barriers to entry for AI experimentation.

RANK_REASON User-generated benchmark of consumer hardware running LLMs.

Read on r/LocalLLaMA →

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

Consumer GPUs achieve high LLM speeds with 122B model running at 37 t/s

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User-generated benchmark of consumer hardware running LLMs.
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COVERAGE [1]

  1. r/LocalLLaMA TIER_1 English(EN) · /u/Dry_Long3157 ·

    4090 + 5060 Ti + 64GB RAM: 206 t/s on a 35B-A3B, and a 122B at 37 t/s

    <!-- SC_OFF --><div class="md"><p>I've been benchmarking a two-card box for a few weeks and I still can't quite get over some of these numbers, so I'm dumping them here.</p> <p><strong>Box:</strong> RTX 4090 (24GB) + RTX 5060 Ti (16GB), i9-13900K, 64GB DDR5. WSL2 with 47GB alloca…

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