“Llama-3.2-1B-4bit → 464 tok/s”

“The article highlights discussions on X (formerly Twitter) about which small LLM is best for Japanese and how to disable 'thinking mode'.”

“I'm able to run huge models on my weak ass pc from 10 years ago relatively fast...that's fucking ridiculous and it blows my mind everytime that I'm able to run these models.”

“The Raspberry Pi AI HAT+ 2 includes a 40TOPS AI processing chip and 8GB of memory, enabling local execution of AI models like Llama3.2.”

“This article dives into the implementation of modern Transformer architectures, going beyond the original Transformer (2017) to explore techniques used in state-of-the-art models.”

“Once connected, the Raspberry Pi 5 will use the AI HAT+ 2 to handle AI-related workloads while leaving the main board's Arm CPU available to complete other tasks.”

“The key is (1) 1B-class GGUF, (2) quantization (Q4 focused), (3) not increasing the KV cache too much, and configuring llama.cpp (=llama-server) tightly.”

“"This article provides a valuable benchmark of SLMs for the Japanese language, a key consideration for developers building Japanese language applications or deploying LLMs locally."”

“Overall, the findings demonstrate that carefully designed prompt-based strategies provide an effective and resource-efficient pathway to improving open-domain dialogue quality in SLMs.”

“the ik_llama.cpp project (a performance-optimized fork of llama.cpp) achieved a breakthrough in local LLM inference for multi-GPU configurations, delivering a massive performance leap — not just a marginal gain, but a 3x to 4x speed improvement.”

“前回の記事ではAMD Ryzen AI Max+ 395でgpt-oss-20bをllama.cppとvLLMで推論させたときの性能と精度を評価した。”

“This is an abliterated version of the allegedly leaked Llama 3.3 8B 128k model that tries to minimize intelligence loss while optimizing for compliance.”

“due to being a hybrid transformer+mamba model, it stays fast as context fills”

“Cloudflare Workers API server was blocked from directly accessing Groq API. Resolved by using Cloudflare AI Gateway.”

“The article mentions the popularity of the Llama series (1-3) and the negative reception of Llama 4, implying a significant drop in quality or performance.”

“I'm using Qwen3 vl 8b with llama.cpp to OCR text from japanese artwork, it's the most accurate model for this that i've tried, but it still sometimes gets a character wrong or omits it entirely. I'm sure the correct prediction is somewhere in the top tokens, so if i had access to them i could easily correct my outputs.”

“The core issue was that when two conflicting documents had the exact same reliability score, the model would often hallucinate a 'winner' or make up math just to provide a verdict.”

“The initial conclusion was that Llama 3.2 Vision (11B) was impractical on a 16GB Mac mini due to swapping. The article then pivots to testing lighter text-based models (2B-3B) before proceeding with image analysis.”

“The author, a former network engineer, is new to Mac and IT, and is building the environment for app development.”

“"Cloudflare Workers AI is an AI inference service that runs on Cloudflare's edge. You can use open-source models such as Llama 3 and Mistral at a low cost with pay-as-you-go pricing."”

“The results show that attention-based adversarial examples lead to measurable drops in evaluation performance while remaining semantically similar to the original inputs.”

“"Suffices for llama?"”

“A diffusion language model that runs 3-6× faster than vLLM-optimized Qwen3-8B on math reasoning tasks.”

“The main findings is that when running certain models partially offloaded to GPU, some models perform much better on Vulkan than CUDA”

“By varying epsilon on this one dim: Negative ε: outputs become restrained, procedural, and instruction-faithful Positive ε: outputs become more verbose, narrative, and speculative”

“What has your experience been with using q8 KV cache and a vision model? Would you say it’s good enough or does it ruin outputs?”

“PLaMo 3 NICT 31B Base is a 31B model pre-trained on English and Japanese datasets, developed by Preferred Networks, Inc. collaborative with National Institute of Information and Communications Technology, NICT.”

“Is there anything ~100B and a bit under that performs well?”

“Which one of these works the best in production: 1. bge m3 2. embeddinggemma-300m 3. qwen3-embedding-0.6b”

“XiaomiMiMo/MiMo-V2-Flash has 310B param and top benches. Seems to compete well with KimiK2Thinking, GLM4.7, MinimaxM2.1, Deepseek3.2”

“Z.AI is providing 431.1 tokens/sec on OpenRouter !!”

“Implementation of AETHER-X: Adaptive POVM Kernels for 4.9x Inference Speedup.”

“Tool calling wise **gpt-oss** is leagues ahead of all the others, at least in my experience using them”

“I would expect it be obvious, the _XL should be better than the _M… right? However the more lossy quant is somehow bigger?”

“Instruction-following capabilities improve substantially (+46% to +75% in IFEval for Llama-3.2-1B and 3B models).”

“Modern language models preserve the geometric substrate that enables Bayesian inference in wind tunnels, and organize their approximate Bayesian updates along this substrate.”

“What are 7b, 20b, 30B parameter models actually FOR?”

“Zed's AI features, to be honest...”

“LIME achieves 1.7x and 3.7x speedups over state-of-the-art baselines under sporadic and bursty request patterns respectively, without compromising model accuracy.”

“How many of you used --fit flag on your llama.cpp commands? Please share your stats on this(Would be nice to see before & after results).”

“DeepSeek-V3 and Llama 3 have emerged, and their amazing performance is attracting attention. However, in order to operate these models at a practical speed, a technique called quantization, which reduces the amount of data, is essential.”

“"It is #1 overall amongst all open weight models and ranks just behind Gemini 3 Pro Preview, a 15-place jump from GLM 4.6"”

“Pruning $8$--$16$ attention sublayers yields up to $1.30\times$ higher inference throughput while keeping average zero-shot accuracy within $2\%$ of the unpruned baseline.”

“Meta's Llama 3.1 can recall 42 percent of the first Harry Potter book”

“Lacking a provided context, it is impossible to extract a key fact.”

“Achieved 4.2x Sonnet 3.5 accuracy for code generation.”

“”

“The article's context, being a Hacker News post, likely focuses on technical details and community discussions regarding Llama-3.3-70B-Instruct.”

“”

“The author spent a lot of time and money on this project and considers themselves the target audience for Hacker News.”