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Show HN: Running Gemma-4 26B at 124 tokens/SEC on a CPU, no GPU
Demonstrating high-speed large language model (LLM) inference on commodity CPU hardware, focusing on output head compression for efficiency.
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Demonstrating high-speed large language model (LLM) inference on commodity CPU hardware, focusing on output head compression for efficiency.
This submission highlights a critical trend: optimizing LLM inference for CPU-only environments. Achieving 124 tokens/second on a desktop CPU for a 26B model significantly lowers the hardware barrier for deploying powerful AI. This directly addresses the high operational costs and specialized hardware dependencies of GPU-intensive LLMs. For B2B SaaS, this implies substantial opportunities for edge AI applications, enhanced data privacy through on-device processing, and reduced cloud infrastructure expenses for specific workloads. It empowers developers to integrate sophisticated AI capabilities into applications without requiring expensive, dedicated GPUs, broadening AI adoption in resource-constrained or privacy-sensitive enterprise environments.
I wanted to know how fast a 26B mixture-of-experts model could run on a desktop CPU with no GPU. Got ~40 tok/s single-stream (lossless) and ~124 batched. The surprising part was the byte budget: for this model you compress the output head (32% of per-token bytes), not the experts (16%). The writeup has the bandwidth roofline and the dead-ends; the repo has the reproducible recipe. Happy to answer questions.Repo: https://github.com/arun-prasath2005/gemma4-cpu-moe
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What is Running Gemma-4 26B at 124 tokens/SEC on a CPU, no GPU?
Running Gemma-4 26B at 124 tokens/SEC on a CPU, no GPU is analyzed by our AI as: Demonstrating high-speed large language model (LLM) inference on commodity CPU hardware, focusing on output head compression for efficiency.. It focuses on This submission highlights a critical trend: optimizing LLM inference for CPU-only environments. Achieving 124 tokens/second on a desktop CPU for a...
Where did Running Gemma-4 26B at 124 tokens/SEC on a CPU, no GPU originate?
Data for Running Gemma-4 26B at 124 tokens/SEC on a CPU, no GPU was aggregated directly from the Hacker News community ecosystem, representing raw developer and early-adopter sentiment.
When was Running Gemma-4 26B at 124 tokens/SEC on a CPU, no GPU publicly launched?
The initial public indexing or launch date for Running Gemma-4 26B at 124 tokens/SEC on a CPU, no GPU within our tracked developer communities was recorded on June 30, 2026.
How popular is Running Gemma-4 26B at 124 tokens/SEC on a CPU, no GPU?
Running Gemma-4 26B at 124 tokens/SEC on a CPU, no GPU has achieved measurable traction, logging over 10 traction score and facilitating 1 recorded discussions or engagements.
Which technical categories define Running Gemma-4 26B at 124 tokens/SEC on a CPU, no GPU?
Based on metadata extraction, Running Gemma-4 26B at 124 tokens/SEC on a CPU, no GPU is categorized under topics such as: Gemma-4 26B, mixture-of-experts model, CPU, GPU.
What are some commercial alternatives to Running Gemma-4 26B at 124 tokens/SEC on a CPU, no GPU?
Our semantic intelligence engine identifies potential commercial alternatives in the SaaS space, such as Google Gemma 4 12B, which offers overlapping value propositions.
How does the creator describe Running Gemma-4 26B at 124 tokens/SEC on a CPU, no GPU?
The original author or development team describes the product as follows: "I wanted to know how fast a 26B mixture-of-experts model could run on a desktop CPU with no GPU. Got ~40 tok/s single-stream (lossless) and ~124 batched. The surprising part was the byte budget: fo..."
Community Voice & Feedback
The output head byte budget is surprising. Did you try any tradeoff where the head is compressed more aggressively but experts stay mostly untouched?
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Hacker News Aggregated via automated community intelligence tracking.
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