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infrastructure#llm📝 BlogAnalyzed: Jan 18, 2026 12:45

Unleashing AI Creativity: Local LLMs Fueling ComfyUI Image Generation!

Published:Jan 18, 2026 12:31
1 min read
Qiita AI

Analysis

This is a fantastic demonstration of combining powerful local language models with image generation tools! Utilizing a DGX Spark with 128GB of integrated memory opens up exciting possibilities for AI-driven creative workflows. This integration allows for seamless prompting and image creation, streamlining the creative process.
Reference

“With the 128GB of integrated memory on the DGX Spark I purchased, it's possible to run a local LLM while generating images with ComfyUI. Amazing!”

PermalinkQiita AI
Hardware#AI Hardware📝 BlogAnalyzed: Jan 3, 2026 06:16

NVIDIA DGX Spark: The Ultimate AI Gadget of 2025?

Published:Jan 3, 2026 05:00
1 min read
ASCII

Analysis

The article highlights the NVIDIA DGX Spark, a compact AI supercomputer, as the best AI gadget for 2025. It emphasizes its small size (15cm square) and powerful specifications, including a Grace Blackwell processor and 128GB of memory, potentially surpassing the RTX 5090. The source is ASCII, a tech publication.

Key Takeaways

Reference

“N/A”

PermalinkASCII
Research#llm📝 BlogAnalyzed: Dec 28, 2025 19:00

Which are the best coding + tooling agent models for vLLM for 128GB memory?

Published:Dec 28, 2025 18:02
1 min read
r/LocalLLaMA

Analysis

This post from r/LocalLLaMA discusses the challenge of finding coding-focused LLMs that fit within a 128GB memory constraint. The user is looking for models around 100B parameters, as there seems to be a gap between smaller (~30B) and larger (~120B+) models. They inquire about the feasibility of using compression techniques like GGUF or AWQ on 120B models to make them fit. The post also raises a fundamental question about whether a model's storage size exceeding available RAM makes it unusable. This highlights the practical limitations of running large language models on consumer-grade hardware and the need for efficient compression and quantization methods. The question is relevant to anyone trying to run LLMs locally for coding tasks.
Reference

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

Permalinkr/LocalLLaMA
Research#llm📝 BlogAnalyzed: Dec 27, 2025 08:31

Strix Halo Llama-bench Results (GLM-4.5-Air)

Published:Dec 27, 2025 05:16
1 min read
r/LocalLLaMA

Analysis

This post on r/LocalLLaMA shares benchmark results for the GLM-4.5-Air model running on a Strix Halo (EVO-X2) system with 128GB of RAM. The user is seeking to optimize their setup and is requesting comparisons from others. The benchmarks include various configurations of the GLM4moe 106B model with Q4_K quantization, using ROCm 7.10. The data presented includes model size, parameters, backend, number of GPU layers (ngl), threads, n_ubatch, type_k, type_v, fa, mmap, test type, and tokens per second (t/s). The user is specifically interested in optimizing for use with Cline.

Key Takeaways

Reference

“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.”

Permalinkr/LocalLLaMA