The Archive

Just wanted to share that I'm pretty happy about Qwen 35b a3b agentic coding performance. I'm running the model in q80 quant, kv cache both q8\_0 as well, with 262144 in 4090 + 5060 ti, via llama.cpp backend with claude code pointing to localhost. For demo/data analytics purposes, it works pretty well. I haven't used it for large codebases, but it definitely is better than gemma4 26b in my use case. One thing that surprises me is that it seems to get better outcome in agentic coding, than chat. When using it with just chat UI, i found the code qwen35b provide a bit too clunky. I wonder o...

Reddit user expresses hope for a 124B parameter Gemma model release.

User seeks faster inference alternatives to Ollama/LM Studio for local model serving (Gemma, Qwen, OpenBioLLM) on 64GB RAM.

G4-MeroMero-31B-Uncensored-Heretic finetune of Gemma 4 released for creative tasks with reduced refusal rate.

Community fine-tune of Gemma 4 31B optimized for creative writing and translations, released in Safetensors and GGUF formats.

Engineer deployed Gemma 4 E4B on Jetson Orin NX with 30+ sensors, 200ms TTFT, offline multimodal robotics stack using llama.cpp and Piper TTS.

Qwen 3.6 35B and Gemma 4 26B MoE models achieve 20–24.5 tok/s on GTX 1080 with 128k context via llama.cpp quantization.

Benchmark comparing Gemma 4 multi-token prediction vs. DFlash speculative decoding on H100 using vLLM and SPEED-Bench dataset.

User reports Qwen 3.6 35B outperforms Gemma 4 26B on speed and long-context, with better prompt adherence in local inference.

ExLlamaV3 adds Gemma 4 support, improved caching, and DFlash optimization for faster LLM inference on consumer hardware.

User reports Gemma-4-26B excels at single-shot code generation for three.js visualizations via informal benchmark.

Developer reflects on year-long MCP server project's growth; notes improved local model tool-calling capability with Gemma 4 and Qwen 3.6 on consumer hardware.

Linear probing and activation patching reveal latent planning representations in Qwen3, Gemma-3, Llama-3; future constraints encoded at layer boundaries.

Google launches The Small Brief, pairing ad industry figures with local businesses to create AI-assisted marketing campaigns using Gemma.

Z-lab releases Gemma-4-26B with DFlash inference optimization, claiming improved performance over MTP via stateful parallel block diffusion.

Gemma 4 26B achieves 578 tok/s on RTX 5090 using DFlash speculative decoding in vLLM, 2.5× faster than baseline.

Anthropic & Neuronpedia release Natural Language Autoencoders (NLA) to interpret Gemma 3 27B's internal activations via learned encoder-decoder LLM pairs.

Multi-Token Prediction optimization for LLaMA.cpp achieves 40% speedup on Gemma 4 quantized models via parallel token drafting.

SemEval-2026 Task 9 system fine-tunes Gemma 3 (12B/27B) per-language with LoRA and GPT-4o-mini synthetic data augmentation for 22-language polarization detection.

Up to 3x the speed with no loss of quality—is it too good to be true?

Community discussion comparing Gemma 4 and Qwen 3.6 model suitability across coding, benchmarks, and agentic workloads.

Benchmark comparison shows Gemma 4 31B trades inference speed for token efficiency vs Qwen 3.6/5 27B; Qwen optimizes for metrics, Gemma for throughput.

Google releases Gemma 4 multi-token prediction drafters in 4 quantized sizes for local deployment.

User reports running Gemma 26B efficiently on CPU-only hardware (i5-8500, 32GB RAM) without GPU acceleration.

Community demo comparing Talkie-1930 (13B retro LM) and Gemma 4 31B in side-by-side chat on Opper.ai platform.

GGUF quantizations of Google Gemma 4 updated with corrected chat template for local inference.

Developer deployed Gemma 4 E2B (2.4GB) on 8GB Android phone for structured JSON parsing and voice-to-task conversion with usable accuracy.

I built [hfviewer.com](http://hfviewer.com), a small tool for visually exploring Hugging Face model architectures. You can paste a Hugging Face URL and get an **interactive visualization** of the architecture, which can make it easier to understand how different models are structured and compare them at a glance. Here is the recent **Qwen3.6-27B** model as an example: [https://hfviewer.com/Qwen/Qwen3.6-27B](https://hfviewer.com/Qwen/Qwen3.6-27B) And here is a side-by-side view of the **Gemma 4** family: [https://hfviewer.com/family/gemma-4](https://hfviewer.com/family/gemma-4) Feel free t...

27B/31B vision model comparison: Qwen 3.6 benchmarks higher than Gemma 4 but underperforms in real-world tasks; suggests benchmark gaming.

User reports Qwen-3.6-27B-q8_k_xl outperforms Gemma 4 for local development tasks on RTX 6000 Pro.