LocalLLaMA User Weighs QAT Gemma 31B GGUF Quants for RTX 3060
r/LocalLLaMA top day3 days agoCommentaryA Reddit user with an RTX 3060 12GB and 32GB DDR3 RAM is evaluating new QAT-based Gemma 31B GGUF quantizations. They currently run an older Unsloth Gemma 31B IQ3_XXS build at long context, with some tensor and mmproj offloading to CPU. The post asks which Q2-Q3 quant to choose, whether QAT changes quality expectations, and whether MTP would help or hurt under tight VRAM limits.
New to Local LLMs: Overwhelmed by Tool Choices, Model Naming, and Quantization
r/LocalLLaMA top day4 days agoTutorialA first-time local LLM user installed ollama on Windows with gemma4 and qwen3.6, but quickly hit a wall of confusion around GUI tool selection, model size tradeoffs, and cryptic quantization naming like Q4_K_M and IQ4_XS. Despite owning high-end hardware (RTX 5090, 64GB DDR5, 9950X3D), the user lacks the foundational knowledge to make informed choices. The post highlights ongoing onboarding gaps in the local LLM ecosystem, where fragmented tooling and jargon-heavy documentation create steep barriers for newcomers.
Google's Official Gemma 4 QAT Q4_0 GGUFs Have Higher Precision Than Unsloth's Q4_K_XL
r/LocalLLaMA top day6 days agoCommentaryAn analysis of Gemma 4 QAT GGUF files reveals that Google's official 'Q4_0' releases actually employ a mixed-precision strategy. For smaller models like E2B and E4B, Google keeps critical token embeddings in Q6_K and certain projection weights in F16. This makes Google's Q4_0 files larger and more precise than Unsloth's 'Q4_K_XL' versions, which default to standard Q4_0 for almost all tensors.
User Shares Gemma 4 QAT Experience: Improved Quality and MTP Speedups
r/LocalLLaMA top day6 days agoOpinionA Reddit user shared their experience with the Gemma 4 31B QAT (Quantization-Aware Training) model. Compared to traditional GGUF quants like Q6_K_L, the QAT version delivers noticeable quality improvements in roleplay and long-context tasks. Additionally, combining the QAT model with Multi-Token Prediction (MTP) yielded massive speedups, boosting generation speeds from ~20 t/s to up to 50 t/s.
club-3090 Adds Experimental FP8 Support for Qwen3.6-27B
r/LocalLLaMA top day6 days agoNew ToolThe open-source project club-3090 has rolled out experimental FP8 quantization support for Qwen3.6-27B. This update is highly anticipated by dual RTX 3090 users, allowing them to run the model with significantly reduced VRAM requirements. According to reports, the official Qwen3.6-27B-FP8 model performs virtually identically to the original unquantized BF16 version.
Exploring 2-bit QAT: Can Ultra-Compressed Large Models Outperform 4-bit Models Half Their Size?
r/LocalLLaMA top day6 days agoCommentaryA popular Reddit thread on r/LocalLLaMA discusses the potential of 2-bit Quantization Aware Training (QAT) for large MoE models (120B to 400B). While current QAT efforts focus on 4-bit, users speculate whether a 2-bit QAT model could fit into consumer hardware (64GB/128GB RAM) and outperform a 4-bit model of half its size. This approach is proposed as a practical alternative to training ternary (1.58-bit) LLMs from scratch.
MTP and QAT: What is the Relation? Running Gemma 4 31B in llama.cpp
r/LocalLLaMA top day6 days agoCommentaryA popular Reddit thread addresses user confusion over running Gemma 4 31B locally. It distinguishes between MTP (Multi-Token Prediction for inference speedup) and QAT (Quantization-Aware Training for preserving 4-bit quality). It also confirms that llama.cpp's new MTP support requires updated GGUF files and a secondary draft model file for acceleration.
NVFP4 Support Merged in llama.cpp: How to Use 4-bit Blackwell Quantization
r/LocalLLaMA top day7 days agoCommentaryFollowing the merge of native NVFP4 (NVIDIA FP4) support in llama.cpp, users are exploring how to leverage this format on Blackwell GPUs (such as the RTX 50-series). The discussion focuses on converting NVFP4 safetensors (like Gemma 4 QAT) to GGUF format and whether importance matrices (imatrix) are required. This enablement promises significant performance gains for local LLM execution on next-gen hardware.
Qwen3.6 35B-A3B on a Laptop: A Local LLM "Zero to One" Milestone
r/LocalLLaMA top day7 days agoOpinionA Reddit user detailed running Qwen3.6 35B-A3B (IQ3_XXS quantization) on an ASUS Zenbook Pro 14 (RTX 4060 8GB VRAM, 64GB RAM). Using llama.cpp, they achieved 27 TPS at 32k context and 18 TPS at 256k context. This setup serves as a highly capable, fully private local agent for file operations, CLI execution, and brainstorming, bypassing cloud privacy concerns.
GGML 與 llama.cpp 正式加入 Hugging Face，攜手保障本地端 AI 的長期發展★ 95
Hugging Face Blog114 days agoBusinessA historic milestone has arrived in the open-source AI world: GGML and llama.cpp — the open-source projects founded by Georgi Gerganov that laid the…
Open LLM Leaderboard 碳排放與模型性能分析：效能與環保的權衡啟示
Hugging Face Blog521 days agoCommentaryHugging Face recently published an in-depth analysis of its well-known Open LLM Leaderboard, examining the carbon dioxide (CO₂) emissions generated during…
Meta 推出 Llama 3.1：405B、70B 與 8B 旗艦開源模型，支援多語言與 128K 超長上下文★ 95
Hugging Face Blog691 days agoReleaseMeta's Llama 3.1 represents a major milestone in the open-source AI landscape. The most notable model is the 405B (405 billion parameter) version — the first…