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Shapelearn Qwen 3.8 27B (13.1 GB VRAM)

ByteShape released full ShapeLearn GGUF quants of Qwen 3.8 27B; its GPU-5 IQ4_XS reaches 99.63% of BF16 quality at 13.1 GB VRAM.

ByteShape released its full ShapeLearn GGUF quantization set for Qwen 3.8 27B (base model released August 14, 2026), following the earlier ShapeLearn-Lite quants published four days after launch. Five quants spanning IQ2_XXS 2.56bpw to IQ4_XS 3.84bpw were benchmarked on six GPUs against Unsloth Dynamic v3, ISTA-DASLab, Bartowski, and AtomicChat; GPU-5 reaches 99.63% of the BF16 aggregate score at roughly 90 tok/s on RTX Pro 6000 and RTX 5090. Each GGUF bundles an MTP draft head, and a separate 1.1 GB DFlash2 draft model enables faster text-only speculative decoding via llama.cpp.

Hacker News · AIupdated · 5h agofirst · 12h agoAI tools & infra 3 sourcesHN 43↑ · 4 comments

[AINews] not much happened today

Latent Space AI news digest covers Anthropic's Claude Code Projects, Google's managed agent APIs, TypeSafe's Jev classifier, and OpenAI's Astra for Law launch.

The 9/16-9/17/2026 AI news roundup highlights Anthropic's Claude Code Projects enabling one conversation to spawn parallel cloud sessions, and Google's Gemini managed agents adding a Credentials API, Files API, and claims of 30% lower costs. It also covers TypeSafe's Jev, a fast constrained-output classifier being used for routing, judgment, and structured decisions, with open reproductions such as openjev-s on Qwen3.6-35B-A3B. OpenAI launched Astra for Law with 26 partner-built and 47 community plugins via Trusted Access, with reports it beats generic GPT-6 Astra plus web search on Vals' legal benchmark. Research items include DeepMind's Stellar Colosseum multi-agent math harness (Codeforces 4263, 71.0% on TCS-Bench) and NVIDIA-associated Agora using Git commits as shared memory.

Latent Space · 7h agoAI industry 2 sources

Nunchux AI Introduces VC-Attention: A Training-Free Low-Bit Attention Kernel That Speeds Up Video Diffusion Transformers

Nunchux AI introduces VC-Attention, a training-free low-bit attention kernel that speeds up video diffusion transformers up to 3.58x.

Nunchux AI unveiled VC-Attention, a training-free attention kernel for video Diffusion Transformers combining V-Smooth (k-means value-token grouping with block-mean residual quantization) and ExpCast-FP8 (single multiply-add softmax exponentiation). Benchmarks on Wan2.2-T2V-A14B, LongCat-Video, HunyuanVideo-1.5, and MiniMax-H3 show 1.59x attention speedup on B200 at 8-bit and 3.58x on RTX 5090 at 4-bit, with end-to-end gains up to 1.70x. It beats SageAttention2 by 2.3 dB PSNR on Wan2.2 at 8-bit and SageAttention3 by up to 3.6 dB at 4-bit. No public kernel release yet; a proprietary extension runs in Nunchux's stack.

MarkTechPost · 1d agoAI research 2 sources1

prism-ml/Ternary-Bonsai-2-27B-gguf — new model trending #29 on Hugging Face

Prism ML released Ternary-Bonsai-2-27B, a 27B ternary-weight model derived from Qwen3.8-27B that runs full reasoning in ~5.95 GB GGUF.

Prism ML published Ternary-Bonsai-2-27B, an Apache 2.0 open-weights model derived from Qwen3.8-27B (27.36B parameters, hybrid attention ~75% linear, 262K-token context). Weights are end-to-end ternary {−1, 0, +1} with g128 FP16 scales at 1.72 bits/weight, shrinking the model from ~54 GB FP16 to 5.95 GB (PTQ1_0) or 7.21 GB (PQ2_0) while reportedly retaining 98.2% of FP16 quality (84.78 average across 14 thinking-mode benchmarks). It ships as GGUF for llama.cpp (CUDA, Metal, CPU) with custom ternary kernels, plus an MLX 2-bit build for Apple Silicon and an optional Q8_0 vision tower pack.

Hugging Face trending models · 1d agoModel release 2 sources1

[AINews] Reality Checks on AI News (Yegge shuts down Gas Town, Databricks’ +60% Astra cost)

Latent Space AI news roundup: Steve Yegge shuts down Gas Town, Databricks reports 60% higher coding spend on GPT-6 Astra, OpenAI launches misalignment disclosure framework.

Latent Space's AI News digest for September 15-16, 2026 leads with Steve Yegge shutting down his Gas Town orchestrator despite spending thousands monthly on coding-agent subscriptions. Databricks rolled out GPT-6 Astra to roughly 3,500 engineers, reporting superior long-horizon performance over Opus 5 and Sol 5.6 but a ~60% increase in coding spend. OpenAI published a formal framework for disclosing model misalignment incidents with six case reports, while Microsoft and Google Research released safety papers on 'capability laundering' and the Fuse motive-inference benchmark. Xiaomi shared live RL training telemetry for MiMo-V2.6, estimated at $493k/day for the 1T-class Pro run.

Latent Space · 1d agoAI industry1

rMuscle: Robotic Muscle Memory for Efficient Vision-Language-Action Model Inference

rMuscle, a caching-based inference framework for vision-language-action models, achieves 1.29-1.42x speedups on RTX 4090 and Jetson Thor while preserving success rates.

rMuscle is a real-time inference framework for Vision-Language-Action (VLA) models that exploits cross-execution similarity in repetitive robot tasks via a dual-phase muscle-memory cache. The Context Cache reuses visual-token outputs to reduce computation, while the Action Cache reuses neuron activation patterns to reduce weight accesses, with online recomputation and sliding-window retrieval keeping overhead low. It achieves 1.29-1.42x speedups on RTX 4090 and Jetson Thor across LIBERO, RoboTwin, and real-world manipulation tasks while maintaining original success rates.

arXiv cs.AI / cs.LG / cs.CL · 1d agoAI tools & infra1

Knowledgator Releases GLiFormer: A 575M-Parameter Encoder That Hits 91.10 F1 on Nested JSON Extraction Without Generating Tokens

Knowledgator released GLiFormer, an Apache-2.0 encoder (264M/575M) handling NER, classification, relations, and nested JSON extraction, scoring 91.10 F1.

Knowledgator Engineering released GLiFormer, a schema-conditioned encoder that performs NER, classification, relation extraction, nested JSON structuring, and embeddings without generating output tokens. GLiFormer Large v1 has 575.6M parameters and scores 91.10 F1 on nested JSON extraction, close to GPT-5.6-luna's 91.96; both checkpoints are Apache 2.0 on Hugging Face. Reported median latency is 69 ms on GPU for the base model, though relation extraction (21.33 micro-F1) still trails GLiNER-Relex and larger LLMs.

MarkTechPost · 1d agoModel release1

Prior Labs Releases TabPFN-3.5: A Tabular Foundation Model That Beats the Winning Otto Kaggle Solution With Default Settings

Prior Labs releases TabPFN-3.5, a 220M-parameter open-weights tabular foundation model that beats the 2015 Otto Kaggle winning score with default settings.

Prior Labs released TabPFN-3.5, a tabular foundation model that predicts in a single forward pass without per-dataset training or tuning. The base model grew from 53M to 220M parameters with a single multitask checkpoint, learned Fourier features, and in-context ECDF rank encodings. It scores 0.375 on the 2015 Otto Kaggle private leaderboard versus the winning 0.382 and claims first place on seven tabular benchmarks including TabArena and BeyondArena. Open weights cover the base, Fast (84M), and Thinking variants, but production use requires the Prior Labs API or a commercial license.

MarkTechPost · 2d agoModel release1