LLM Reddit 4d ago 1 min read
The draw for LocalLLaMA was not just another coding model, but Cohere asking the local-inference crowd to test pre-release weights first.
#localllama
RSS FeedLLM Reddit Apr 25, 2026 2 min read
LocalLLaMA reacted like dense models had suddenly become fun again. The official Qwen numbers were strong, but the real community energy came from people immediately asking about quants, GGUF builds, and whether 27B had become the practical sweet spot. By crawl time on April 25, 2026, the thread had 1,688 points and 603 comments.
LLM Reddit Apr 23, 2026 2 min read
A r/LocalLLaMA benchmark compared 21 local coding models on HumanEval+, speed, and memory, putting Qwen 3.6 35B-A3B on top while surfacing practical RAM and tok/s trade-offs.
LLM Reddit Apr 23, 2026 2 min read
A r/LocalLLaMA post is not a formal benchmark, but it captured the community mood: local models can be attractive when hosted models drift, filter unexpectedly, or change behavior across updates.
LLM Reddit Apr 8, 2026 2 min read
A popular r/LocalLLaMA self-post lays out a concrete 2x H200 serving stack for GPT-OSS-120B, including routing, monitoring, and queueing tradeoffs. The appeal is not just the headline throughput, but the unusually detailed operational data behind it.
LLM Reddit Apr 5, 2026 2 min read
A LocalLLaMA thread highlighted Gemma 4 31B's unexpectedly strong FoodTruck Bench showing, and the discussion quickly turned to long-horizon planning quality and benchmark reliability.
LLM Reddit Mar 18, 2026 2 min read
A detailed r/LocalLLaMA experiment claims that copying layer blocks around 50-56% depth consistently hurts or collapses model quality across multiple architectures. The post stands out because it compares dense, hybrid, MoE, and transplant setups from a fully local MLX workflow.
LLM Reddit Mar 1, 2026 2 min read
r/LocalLLaMA Benchmarks: <code>Krasis</code> reports 3,324 tok/s prefill for 80B MoE on one RTX 5080
A r/LocalLLaMA post (score 180, 53 comments) shared benchmark data for <code>Krasis</code>, a hybrid CPU/GPU runtime aimed at large MoE models. The key claim is that GPU-heavy prefill plus CPU decode can reduce long-context waiting time even when full models do not fit in consumer VRAM.
LLM Reddit Feb 28, 2026 2 min read
A Reddit thread spotlighted LLmFit, a CLI/TUI tool for recommending runnable models per hardware profile, while commenters raised data-quality and recommendation-validity questions.
LLM Reddit Feb 28, 2026 2 min read
A high-engagement r/LocalLLaMA thread reviewed Unsloth’s updated Qwen3.5-35B-A3B dynamic quantization release, including KLD/PPL data, tensor-level tradeoffs, and reproducibility artifacts.
LLM Reddit Feb 20, 2026 2 min read
A widely discussed LocalLLaMA post introduces open Kitten TTS v0.8 models (80M/40M/14M), emphasizing CPU-friendly deployment and sub-25MB footprint for the smallest variant.
LLM Reddit Feb 19, 2026 2 min read
LocalLLaMA Discussion: 13M MatMul-Free CPU Model Highlights the Real Bottleneck in Tiny LLM Training
A high-signal LocalLLaMA post reports training a 13.6M parameter matmul-free language model on a 2-thread CPU in about 1.2 hours, with the author arguing the output head, not the ternary core, dominated compute cost.