“という事で、現環境でどうにかこうにかローカルでLLMを稼働できないか試行錯誤し、Windowsで実践してみました。”

“This series dissects the inner workings of LLMs, from full scratch implementations with Python and NumPy, to cutting-edge techniques used in Qwen-32B class models.”

“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.”

“Quantized models can be seamlessly deployed on Amazon SageMaker AI using a few lines of code.”

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“So by merging LoRA to full model, it's possible to quantize the merged model and have a Q8_0 GGUF FLUX.2 [dev] Turbo that uses less memory and keeps its high precision.”

“The models are fully compatible with the LightX2V lightweight video/image generation inference framework.”

“HyperNova 60B base architecture is gpt-oss-120b.”

“The model struggled to write unit tests for a simple function called interval2short() that just formats a time interval as a short, approximate string... It really struggled to identify that the output is "2h 0m" instead of "2h." ... It then went on a multi-thousand-token thinking bender before deciding that it was very important to document that interval2short() always returns two components.”

“The paper formulates the covariant hydrodynamics equations as an intersection problem on an infinite dimensional symplectic manifold associated with spacetime.”

“Certain compression strategies not only preserve but can also improve robustness, particularly on networks with more complex architectures.”

“The paper explores integer (Int8) quantization and a resource-aware gait scheduling viewpoint to maximize RL reward under power constraints.”

“Utilizing 2:4 sparsity combined with quantization on $4096 imes 4096$ matrices, our approach achieves a reduction of up to $4\times$ in weight storage and a $1.71\times$ speedup in matrix multiplication, yielding a $1.29\times$ end-to-end latency reduction compared to dense GPU baselines.”

“PP-ACDC achieves asymptotic (exact) average consensus on any strongly connected digraph under appropriately chosen quantization parameters.”

“The paper identifies the obstruction to the existence of the Prequantum Groupoid as the non-additivity of the integration of the prequantum form on the space of loops.”

“MDBF enhances perplexity and zero-shot accuracy over previous binary formats at matched bits per weight while preserving the same deployment-friendly inference primitive.”

“The GUP corrections reduce to total derivatives, preserving the absence of the Boulware-Deser ghost.”

“OptRot outperforms both Hadamard rotations and more expensive, data-dependent methods like SpinQuant and OSTQuant for weight quantization.”

“The approach substantially improves both the representational power and the RD performance of 2DGS while maintaining over 1000 FPS decoding. Compared with the baseline GSImage, we reduce BD-rate by 43.44% on Kodak and 29.91% on DIV2K.”

“The paper's core contribution is "DivQAT, a novel algorithm to train quantized CNNs based on Quantization Aware Training (QAT) aiming to enhance their robustness against extraction attacks."”

“The paper derives generators and relations of the Coulomb branch operator algebra for specific SU(2) theories and analyzes theories with a specific Coulomb branch structure.”

“The consistent orderings are in one-to-one correspondence with the Jacobians associated with all field redefinitions of a set of canonical degrees of freedom. For each admissible operator ordering--or equivalently, each path-integral measure--we identify a definite, positive Hilbert-space inner product. All such prescriptions define the same quantum theory, in the sense that they lead to identical physical observables.”

“INT8 quantization consistently preserves over 90% of the FP16 baseline accuracy and achieves a 1.5x prefill speedup on the Atlas A2.”

“"Suffices for llama?"”

“The extreme constraints nerd-sniped me and forced interesting trade-offs: trigram hashing (typo-tolerant, loses word order), 16-bit integer math, and some careful massaging of the training data meant I could keep the examples 'interesting'.”

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

“The paper discusses two correspondences: one based on Hamiltonian reduction and its quantum counterpart, and another involving non-trivial dualities like Fourier and Legendre transforms.”

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

“Gemini 3 is not that great if you use it in the Gemini App or AIS in the browser, it's quite quantized most of the time, doesn't reason for long, and hallucinates a lot more.”

“The article is based on the content of the provided Colab notebook (mnist_t4_ultrafast_inference_v7.ipynb).”

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

“Achieved state-of-the-art results with 98.38% of tensors quantized to the FP8 format.”

“The paper constructs a covariant formulation for self-dual Yang-Mills and self-dual gravity, and subsequently extends this construction to the full Chiral Higher Spin Gravity.”

“Specializing a small model for a single task often yields better results than using a massive, general-purpose one.”

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“The paper's strength lies in its practical relevance and potential for improving the performance of DOA estimation algorithms in resource-constrained environments.”

“Looking for anyone who has some benchmarks they would like to share. I am trying to optimize my EVO-X2 (Strix Halo) 128GB box using GLM-4.5-Air for use with Cline.”

“PGR$^2$M improves Fréchet inception distance and reconstruction metrics for both generation and editing compared with CoMo and recent diffusion- and tokenization-based baselines, while user studies confirm that it enables intuitive, structure-preserving motion edits.”

“Mify-Coder achieves comparable accuracy and safety while significantly outperforming much larger baseline models on standard coding and function-calling benchmarks.”

“I would prefer minimax-m2.1 for general usage from the benchmark result, about ~2.5x prompt processing speed, ~2x token generation speed”

“The research is based on an ArXiv publication.”

“LLM lightweighting technology has evolved from the traditional 16bit to 8bit, 4bit, but now there is even more challenge to the 1bit area and technology to suppress memory consumption other than weight is attracting attention.”

“In order to operate large language models at a practical cost, quantization technology that reduces the number of bits of data is indispensable.”

“SemDAC outperforms DAC across perceptual metrics and achieves lower WER when running Whisper on reconstructed speech, all while operating at substantially lower bitrates (e.g., 0.95 kbps vs. 2.5 kbps for DAC).”

“The paper proposes a novel data-aware PTQ approach for 1-bit LLMs that explicitly accounts for activation error accumulation while keeping optimization efficient.”

“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.”

“LLM quantization from theory to implementation.”

“The research focuses on sensitivity-aware mixed-precision quantization.”

“The paper focuses on query-aware mixed-precision KV cache quantization.”

“The study suggests that 8-bit quantization can improve continual learning capabilities in LLMs.”