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Models2026-06-22 · source-backed
Unsloth's changelog shows GGUF quantizations compressing GLM-5.2's 1.51TB BF16 weights to 217GB at 1-bit, bringing the strongest text-only open model into reach of a single high-memory local rig. The same update adds Gemma 4 MTP with auto speculative decoding for roughly 2x faster inference. For builders who want frontier-open quality without an API dependency, this is the practical "can I actually run it" answer, and it's the same local-first thread running through today's Top 5.
Each link below shares sources, entities, or timing with this story.
Google DeepMind shipped Quantization-Aware Training checkpoints for every Gemma 4 size, and the headline number is genuinely useful: the smallest model goes from 11.4GB to 1.1GB. That's 0.84GB if you go text-only. Up to ~72% lower VRAM and 2x faster inference on mobile NPUs, w...
Google DeepMind released Gemma 4 on April 2 with four model sizes (E2B, E4B, 26B MoE, 31B Dense) under Apache 2.0. Multimodal (text, vision, audio). 256K context. Native thinking and tool-calling optimized for agentic workflows. Day-zero ecosystem support across vLLM, llama.cp...
New batching algorithms enable ~7x, up to 12x+, longer-context GRPO training with no accuracy or speed penalty versus optimized FA3 and chunked-loss setups (Unsloth Docs). Qwen3-8B GRPO reaches 110K context on one 80GB H100 via vLLM plus QLoRA. For solo builders doing reasonin...
Gemma 4 12B dropped June 3, and the spec sheet is the kind of thing I read twice to make sure I wasn't misreading it. 11.95 billion params, Apache 2.0, reads text, image, audio, and video. No separate vision encoder. No separate audio encoder. The model handles all of it nativ...
Google released open-source Multi-Token Prediction (MTP) drafters for the Gemma 4 model family. The concept: pair a heavy target model (Gemma 4 31B) with a lightweight drafter that predicts several future tokens in parallel. The target model verifies the predictions in a singl...
GLM-5.1 from Zhipu AI scored 58.4% on SWE-bench Pro. GPT-5.4 scored 57.7%. Claude Opus 4.6 scored 57.3%. That's the first time an open-weight model has ever topped a major coding benchmark against the best proprietary models. The specs matter. GLM-5.1 is a 754B-parameter mixtu...
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