Turbo-fieldfare: Open-source engine running Gemma 4 26B in 2 GB RAM on Apple Silicon
Its a custom Swift/Metal inference engine that runs Gemma 4 26B-A4B-IT on M-series Macs with very low RAM. It uses ~2GB instead of ~14 GB. The…
How to use Google microbenchmarks for evaluating TPU performance
Google's open-source TPU microbenchmark suite provides developers with granular performance metrics across Network, Compute, HBM, Host Transfer, and Attention components to validate real-world hardware capabilities. By…
Intentional Control of Internal States in Gemma 3 27B
This research was done as my capstone project during ARBOx4.Epistemic Status: I'm relatively sure the results I obtained and my interpretations are correct. I'm unsure if…
Show HN: Open-source engine running Gemma 4 26B in 2 GB RAM on any M-series Mac
Hi HN,I built a specialized inference engine for running 4-bit Gemma 4 26B-A4B-IT on any M-series Mac using about 2 GB of RAM. It is called…
Gemma 4 26B/31B Q4 QAT vs Q4/Q5/Q6/Q8
What are your experiences with Gemma 4's QAT versions compared to their regular ones? So far I have mostly heard about regressions, but if you have…
Appreciation for Gemma 4 26b A4b
I really love this model, I have been using the q4_k_l by Bartowski (I have heard QAT is quite the downgrade in some aspects) and it…
Testing Gemma 4 & Qwen 3.6 MoE on AMD 6800H (iGPU/UMA) – Performance Breakdown
I’ve been doing some benchmarking on my mini-PC setup (AMD Ryzen 7 6800H) to see how it handles the new Gemma 4 and Qwen 3.6 MoE…
Do you want new Gemma?
https://x.com/i/status/2081398564345802934 submitted by /u/jacek2023 [link] [comments]
Now that Google signed the letter, would be a good time for them to release the 100B Gemma 4 model, show they really mean it :)
Now that Google signed the letter, would be a good time for them to release the 100B Gemma 4 model, show they really mean it :)
RT Christopher Nguyen ⽗: The Gemma series is an amazing set of highly performant open-weight models. They have proven extremely effective in industri…
RT Christopher Nguyen ⽗The Gemma series is an amazing set of highly performant open-weight models. They have proven extremely effective in industrial settings where site-deployed agents…
Gemma 4 26B A4B running on iPhone 17 Pro via model paging
Hi everyone, Before I begin, I should mention that the system I'm showcasing was developed by the team at Noema, which I founded. I wanted to…
Do Active SAE Feature Planes Carry More Holonomy? A Preregistered Reversal in Gemma
arXiv:2607.20522v1 Announce Type: new Abstract: This paper tests whether holonomy concentrates on active sparse-autoencoder (SAE) feature planes in Gemma 2 2B, a concrete operationalization of the…
Run Ray on TPU, Part 2: Ray AI libraries
This second installment explores how Ray’s higher-level libraries—Serve, Data, and Train—abstract the complexities of running AI workloads on Google's TPU slices. Ray Serve uses a simple…
Tested (the updated) Gemma 4 locally on coding with OpenCode
Gemma 4 was updated (mostly chat templates) and I took it for a test. On a local llama.cpp server running on M5 Pro with 48GB, 26B…
RT Google Gemma: Gemma 4 just crossed 300 million downloads. Thank you to the developers, researchers, and open-source community building with us. You…
RT Google GemmaGemma 4 just crossed 300 million downloads.Thank you to the developers, researchers, and open-source community building with us. Your work and feedback drive this…
NASA Puts Google’s Gemma Large Language Model in Orbit
The viability of orbital data centers hosting the largest and most capable large language models (LLMs) remains hotly contested. But enormous deployments that require thousands of…
Cactus Hybrid: We taught Gemma 4 to know when it’s wrong
Hey HN, Henry & Roman here from Cactus. A small, on-device model is fast and private, but sometimes wrong, but frontier models are getting expensive pretty…
Show HN: Cactus Hybrid: We taught Gemma 4 to know when it's wrong
Hey HN, Henry & Roman here from Cactus.A small, on-device model is fast and private, but sometimes wrong, but frontier models are getting expensive pretty fast.…
Scaling Agentic RL: High-Throughput Agentic Training with Tunix
Tunix is Google’s new JAX-native post-training library designed to eliminate TPU idling bottlenecks when training multi-turn, tool-using LLM reasoning agents. It maximizes hardware throughput by combining…
RT Google Gemma: Voice AI without the wait! ⏱️ Thanks to Hugging Face and Cerebras, developers can now use the Gemma 4 31B model as the brain for vo…
RT Google GemmaVoice AI without the wait! ⏱️Thanks to Hugging Face and Cerebras, developers can now use the Gemma 4 31B model as the brain for…
Gemma 4 is still lazy
Please tell me I'm doing something wrong. My config: [*] flash-attn = on jinja = true fit = true offline = true mmproj-offload = false mmap…
Best Local LLMs You Can Run on a Single 24GB GPU in 2026: Qwen, Gemma, Mistral, DeepSeek Compared
A single 24GB GPU is the practical floor for serious local inference. This guide compares six open-weight models that fit one card at Q4_K_M. It covers…
Run Ray on TPU, Part 1: The foundations
Ray 2.55 introduces official, first-class support for Google Cloud TPUs, enabling developers to run distributed Python workloads on Google's accelerators using the familiar Ray task-and-actor APIs.…
Why gemma 4 26B does not support audio?
Gemma 4 26b is really good model and with recent update it's even better than before but why google did not add audio capability to this…