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Gemma 4 26B-A4B

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Headlines · 3

  1. Developer prunes Gemma 4 26B-A4B to 15% for on-device use

    A Reddit developer reported pruning the MoE model Gemma 4 26B-A4B to 15% of its original size by retaining eight experts per layer. Because the model normally selects eight experts per token, this makes routing deterministic and produces a dense 3.81B-parameter model, or 2.75 GB in Q4_K_M. The pruned model achieved 92% agreement with the original on one narrow classification task, compared with 90% agreement between the 12B and 26B models on the same samples. On the developer's Mac, it ran at 79 tokens per second using 3.8 GB of memory, roughly twice the speed and half the memory of the 12B model. The developer said it is not a general-purpose model and that knowledge-dependent tasks suffer.

    r/LocalLLaMA · 🔥 0
  2. Local quantized models vs Claude on nine real CUDA-engine bug fixes

    A Reddit author ran nine real regression bugs from the private history of their C++/CUDA inference engine (capy-flash) through coding agents, with Claude models as fixed reference points. Local quantized models ran on the author's own engine on 2x RTX 5070 Ti 16GB, a Ryzen 9900X and 96GB DDR5, while Claude models ran in Claude Code and local models in OpenCode. Patches were scored automatically against a hidden key from the real fix and by Claude Opus 5 as a blind judge, which correlated at r = 0.79; Claude Opus 5.5 led at 0.74, Sonnet 5.5 scored 0.58, Qwen3.8-Flash-Next 3-bit scored 0.48, and Gemma 4 26B-A4B scored 0.07. The author reports that 3-bit quantization left scores largely unchanged (Flash-Next -0.02, 27B +0.05, inside the noise) while making the 27B generate about 40% more tokens per task.

    r/LocalLLaMA · 🔥 0
  3. ServiceNow previews Apriel Voice 27B speech-reasoning model

    ServiceNow announced Apriel Voice 27B Preview, an open-weight model built around a Gemma 4 26B-A4B backbone with a Whisper large-v3-turbo speech encoder. It accepts audio, text, and images, and is designed to produce text responses and structured tool calls when paired with an external speech synthesizer and application-managed turn-taking. The model card also reports speech-recognition, reasoning, tool-use, and latency benchmarks.

    r/LocalLLaMA · 🔥 0

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