World Cup Opening Day Reportedly Followed Qwen’s Script
量子位 QbitAI21 hours agoCommentaryBased only on the title, this QbitAI item appears to be a light commentary piece about Qwen and sports prediction. It suggests that the first day of the World Cup unfolded in a way that matched a prior “script” or forecast associated with Qianwen/Qwen. Without the article body, the specific match, prediction method, prompt, result, and evidence cannot be verified.
AINews: Fable and Mythos Access Suspended Over Cybersecurity Risk★ 76
Latent SpaceyesterdayIncidentAnthropic’s Claude Fable 5 and Mythos 5 were abruptly suspended after a US export-control directive tied to a possible jailbreak and national cybersecurity risk. The roundup frames the event as a new “model sovereignty” warning for teams relying on closed frontier APIs. It also covers Kimi-K2.7-Code, MiniMax M3, DeepSWE replacing SWE-Bench Pro, agent-inference benchmarks, sandboxing, and Gemini-SQL2.
qwen3.6-27b Users Report Repeated Tool Call Loops
r/LocalLLaMA top day3 days agoIncidentA Reddit user on r/LocalLLaMA says qwen3.6-27b can fall into repeated tool-call loops during use. They report spending two days adjusting parameters such as temperature and top-k without resolving the issue. The post is a troubleshooting question rather than a confirmed bug report, asking whether other local model users have seen similar behavior.
Benchmarking Google Eloquent Exposes Major On-Device Dictation Reliability Issues
r/LocalLLaMA top day3 days agoBenchmarkA LocalLLaMA user tried to benchmark Google’s new fully local dictation app, Eloquent, against open ASR models such as Qwen3-ASR and NVIDIA Parakeet V3. The tester reported that roughly half of dictations returned only fragments, even during manual use. When Eloquent produced complete transcripts, its word error rate was competitive, but the missing-output behavior made the app unreliable for evaluation and practical use.
llama.cpp Merges MTP Optimization Removing Padding and Extra D2D Copies
r/LocalLLaMA top day4 days agoReleasellama.cpp merged PR #24086, which changes ggml_gated_delta_net so MTP passes snapshot count K as an operation parameter instead of deriving it from tensor shape. The change removes a padding workaround and copies emitted snapshots into the recurrent cache with a single strided ggml_cpy. Benchmarks on DGX Spark with Qwen3.6-35B-A3B-UD-Q4_K_M.gguf showed about a 4% throughput gain, with wall time falling from 21.71s to 20.91s.
Qwen3.6-MTP-27B on Tesla V100: llama.cpp Throughput Tuning Question
r/LocalLLaMA top day4 days agoBenchmarkA Reddit user is running Qwen3.6-MTP-27B-MTP in Q4_K_M GGUF format with llama.cpp server on a 32GB Tesla V100. They report one peak of 55 tokens per second, but typical throughput is closer to 44-48 TPS. The post asks whether flags such as parallelism, speculative MTP draft settings, KV cache quantization, flash attention, and a 262K context window are limiting performance without improving output quality.
How Useful Is qwopus Compared With Qwen3.6 27B for Coding?
r/LocalLLaMA top day4 days agoOpinionA Reddit user on r/LocalLLaMA asks for practical comparisons between qwopus and Qwen3.6 27B, specifically for coding work. They note conflicting community opinions, with some users calling qwopus worse and others saying it is much better. In their own simple tests, they did not notice clear differences and want feedback from people using these models for agentic coding.
Without Open Source LLMs, US AI Companies Could Have Monopolized the Technology
r/LocalLLaMA top day4 days agoOpinionThis r/LocalLLaMA post argues that open-source LLMs are an ethical duty because AI has broad social impact. The author worries that without open models, US AI companies could have monopolized access and potentially limited availability to US firms. They also frame China’s release of powerful open-source LLMs as a contribution to humanity, despite political disagreements.
OSCAR RotationZoo - Offline Spectral Covariance-Aware Rotation for 2-bit KV Cache Quantization
r/LocalLLaMA top day4 days agoPaperOSCAR applies offline-precomputed rotation matrices—derived from spectral covariance analysis—to reshape KV tensor distributions before 2-bit quantization, suppressing outliers and reducing rounding error. The rotation adds negligible inference overhead since it requires no runtime learning. GGUF downloads for Gemma-4-12B-it, Qwen3-32B, and Qwen3-4B-Thinking are available, with llama.cpp and sglang integrations and an arXiv paper.
Cohere North Mini Code 1.0
r/LocalLLaMA top day5 days agoReleaseCohereLabs’ North Mini Code 1.0 appears to have moved from early access to final release, with weights available on Hugging Face. The Reddit post describes it as a 30B A3B coding model. Its Artificial Analysis overall score of 28 trails Qwen 3.6 35B at 43, but its coding index score of 33 is close to Qwen’s 35 and above Gemma 4 26B’s 22.
Jetson Orin NX Build for Hermes Agent + Benchmarking
r/LocalLLaMA top day5 days agoHardwareThe post describes turning an unused Jetson Orin NX into a compact local LLM server for Hermes Agent testing. The goals were low noise, over 10 tok/s generation, 300 tok/s prompt processing, at least 65K context, and a custom case. After testing Gemma 4, Qwen 3.6, and many quant variants, the author reports Gemma 4 26B A4B UD Q2_K_XL reaching 66K context and 10.21 tok/s near 60K context.
ggml-webgpu improves prefill speeds for k-quants in llama.cpp PR
r/LocalLLaMA top day5 days agoBenchmarkllama.cpp PR #24225 improves ggml-webgpu matrix multiplication performance for k-quants and refactors matmul paths for Q4/Q5/Q8 and k-quants. In pp512 tests on an M2 Pro, reported speedups range from about 1.33x to 3.78x across Q2_K, Q3_K, Q4_K, Q5_K, and Q6_K. The largest gains appear on Q3_K models, including Qwen and Gemma examples.
Packed twin inference doubles Qwen3.6-27B throughput on one MI50
r/LocalLLaMA top day5 days agoBenchmarkA LocalLLaMA user shared an early packed-twin-inference experiment for local LLM acceleration. The idea resembles speculative decoding, but uses the same quantized model side-by-side instead of a smaller draft model. On a single AMD MI50, the author reports Qwen3.6-27B improving from 19.4 to 38.1 tk/s, with Q8-or-lower quantization as the main target.
JetBrains Mellum 2: a really good and performant model
r/LocalLLaMA top day5 days agoBenchmarkA r/LocalLLaMA user shared informal impressions of JetBrains Mellum 2, focusing on local coding-style tasks and tool calls. On an AMD Radeon RX 7900 XT with llama.cpp Vulkan and 131K context, the model reportedly generated around 111 tokens/s and stayed above 100 tokens/s near full context. The author stresses this is not a scientific benchmark, but a practical workflow-oriented test.
Omi Med STT v1: Open-Weight Medical ASR Fine-Tuned from Parakeet 0.6B★ 72
r/LocalLLaMA top day5 days agoReleaseOmi Health’s founder says he fine-tuned NVIDIA Parakeet TDT 0.6B v2 for clinical speech and released Omi Med STT v1 under CC-BY-4.0. The runtime supports Mac, Windows, and Linux, auto-selecting MLX, NeMo, or GGUF/parakeet.cpp backends. In the author’s held-out medical benchmark, it reports 2.37% medical-WER and 145× realtime on local A10 compute.
Pipeline parallelism in llama.cpp may be wasting your VRAM
r/LocalLLaMA top day5 days agoBenchmarkThe author compared three llama.cpp Vulkan builds: default 4 sched copies, 1 sched copy, and no pipeline parallelism. In their Qwen GGUF test, input and output throughput were nearly identical across all configurations. However, the default setting used about 1.5GB more VRAM for compute buffers and reduced usable context from roughly 113K tokens to around 88K, though parallel-request benefits were not tested.
Arguing with an AI bot posting outdated Llama 3.1 takes
r/LocalLLaMA top day5 days agoCommentaryA r/LocalLLaMA post jokes about arguing with an AI bot that posted outdated commentary involving Llama 3.1. The author says such bots should enable web search instead of relying on stale knowledge. The post also mocks exaggerated model testimonial posts, using Qwen3.6 27B as a sarcastic example, making it more of a community quality complaint than technical news.
Qwen3.6-35B-A3B Tool Calling Benchmark: ByteShape vs Unsloth GGUFs
r/LocalLLaMA top day5 days agoBenchmarkThe post benchmarks eight Qwen3.6-35B-A3B GGUF quants from ByteShape and Unsloth using llama.cpp and tool-eval-bench. It compares f16, q8_0, and q4_0 KV cache quantization under short and long-context pressure, totaling 144 runs and roughly 300 GPU-hours. The author reports no clear ByteShape versus Unsloth winner, q8_0 as close to a free lunch, q4_0 as weaker, and long context as a major tool-calling degradation factor.
LocalLLaMA post tier list
r/LocalLLaMA top day6 days agoOpinionThe author proposes a tier list for r/LocalLLaMA posts in response to complaints about declining post quality. Top-tier posts include new local model releases with GGUF/MLX or benchmark data, meaningful optimizations, complete hardware performance reports, and well-analyzed research. Low-tier posts include repeated toy benchmarks, unrelated cloud AI chatter, AI-generated slop, and thinly disguised ads for Claude-wrapper startups.
An Implementation of NanoQuant: A Flexible Binary Quantization Method
r/LocalLLaMA top day6 days agoNew ToolA r/LocalLLaMA post presents an unofficial PyTorch implementation of NanoQuant, a 2026 post-training quantization method for dense transformers. The method factorizes weights into scaling vectors and binary matrices, then quantizes and fine-tunes blocks sequentially to reduce hardware requirements. Early Qwen3-0.6B and Qwen3-4B experiments are promising for base models, but instruct quality remains weak and highly dependent on calibration data.
Luce Spark: a 35B MoE on a 16 GB GPU, without the offload tax★ 72
r/LocalLLaMA top day6 days agoNew ToolLuce Spark is an open-source MoE offload system for running 33B-35B A3B models on 16GB-class GPUs. It keeps frequently routed experts on GPU, stores the long tail in system RAM, and swaps cold experts through a bounded async cache. The author reports 13.3 GiB for Qwen3.6 35B-A3B and about 100 tok/s with Spark optimizations, but notes real 16GB GPU testing is still missing.
[3090] Gemma4 QAT + MTP quick TPS numbers
r/LocalLLaMA top day6 days agoBenchmarkA r/LocalLLaMA user shared quick throughput numbers for Gemma4 QAT with MTP speculative decoding on an RTX 3090 24GB setup. They report roughly 1.2-1.8x TPS improvement, with Gemma 4 31B moving from about 40 tok/s to 70-80 tok/s. The author frames this as a rough benchmark, using 11 task categories and noting stochastic variation from temp 1.0.
mtmd adds video input support in llama.cpp★ 72
r/LocalLLaMA top day6 days agoReleaseggml-org/llama.cpp merged PR #24269, adding video input support to mtmd through mtmd-cli and /chat/completions, which also enables the web UI path. The implementation invokes a locally installed ffmpeg subprocess instead of bundling codec support, and currently extracts visual frames only, with no audio support yet. It was tested with Qwen3-VL-2B in CLI and Gemma 4 E4B in web UI, making local multimodal video experiments more accessible.
Leanstral: Open-Source Foundation for Trustworthy Vibe-Coding★ 76
Mistral AI News6 days agoReleaseMistral AI introduced Leanstral, an open-source code agent designed for Lean 4 and formal proof engineering. The model is available through Apache 2.0 weights, Mistral Vibe, and a Labs API endpoint. Mistral positions it as a cost-efficient alternative for verified coding workflows, with FLTEval benchmarks comparing it against Claude family models and large open-source competitors.
Introducing Mistral Small 4★ 76
Mistral AI News6 days agoReleaseMistral AI introduced Mistral Small 4 as the next major release in the Mistral Small family. It combines reasoning, multimodal, and agentic coding capabilities into one open model with configurable reasoning effort. The model uses a MoE architecture, supports a 256k context window and text-image inputs, and is available through Mistral API, AI Studio, Hugging Face, NVIDIA NIM, and common inference stacks.
Introducing Mistral Small 4★ 78
Mistral AI News6 days agoReleaseMistral Small 4 is the next major release in the Mistral Small family, unifying Magistral-style reasoning, Pixtral-style multimodality, and Devstral-style coding agents. It uses a MoE architecture with 119B total parameters, 6B active parameters per token, a 256k context window, and configurable reasoning effort. The model is available via Mistral API, AI Studio, Hugging Face, open-source serving stacks, and NVIDIA deployment options.
Gemma 4 31B FP8 Matches Claude Sonnet 4.6 Medium in Custom Benchmark★ 75
r/LocalLLaMA top day6 days agoBenchmarkA Reddit user shared benchmark results showing Google's Gemma 4 31B (FP8) performing on par with Claude Sonnet 4.6 Medium. The custom evaluation harness tested complex tasks including Neo4j Cypher queries, entity extraction, agentic tool calling, Python coding, and multi-vector retrieval synthesis. This highlights how quantized mid-sized open-source models are closing the gap with leading proprietary frontier models.
Qwen 3.6 27B DeepSWE Benchmark Results Highlight Gap Between Local and Closed-Source Models
r/LocalLLaMA top day6 days agoBenchmarkA community benchmark of Qwen 3.6 27B on DeepSWE yielded a score of 1.79% (18/20th place), slightly outperforming Haiku 4.5. Run on a single RTX 6000 Blackwell GPU via vLLM with reasoning enabled, the test averaged 32 minutes and 44k output tokens per task. The author notes that while Qwen 3.6 27B represents a 'poor man's local SOTA,' the massive gap compared to frontier closed models suggests local LLMs are struggling to keep pace in complex coding.
Qwen 3.6 27B KV Cache Quantization Benchmarks: KVarN, Turbo, and TCQ Evaluated
r/LocalLLaMA top day7 days agoBenchmarkReddit user Anbeeld shared comprehensive KV cache quantization benchmarks for Qwen 3.6 27B across 75 configuration pairs. Using BeeLlama.cpp (a custom llama.cpp fork), the test evaluates q8, q6, q5, and q4 quantization levels. It specifically highlights advanced implementations like KVarN, TurboQuant, and TCQ to optimize long-context inference efficiency.
LLM Research Papers: The 2026 List (January to May)
Ahead of AI (Raschka)8 days agoPaperSebastian Raschka compiles a curated reference list of LLM papers he bookmarked from January through May 2026. The list is not comprehensive, but organized around topics useful for future articles, lectures, code examples, and research work. Public sections emphasize reasoning, RL, efficient inference, long context, agent systems, tool use, coding agents, diffusion language models, and serving infrastructure.