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If you are running quantized LLMs locally, especially 4-bit models, memory bandwidth usually matters more than raw CUDA core count. Once the model fits in VRAM, inference speed is largely determined by how fast the GPU can stream weights from VRAM into the tensor cores. For 7B models this is less obvious. For 34B, 70B, the bandwidth becomes one of the main bottlenecks. This a...


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Qwen3.5 27B fits comfortably on a 24 GB GPU up to 131k context in 4-bit, but becomes memory heavy at 262k. Qwen3.5 35B MoE in 4-bit is the more practical long-context model for 24 GB cards, and it is significantly faster in token generation despite having more total parameters. VRAM is still the main constraint, but memory bandwidth determines how enjoyable the model feels at...


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If you run quantized LLMs locally, VRAM is your main constraint. 16GB is the practical entry point for 13B class models in 4-bit, and anything above 24GB opens the door to 70B with multi GPU setups. Between November 2025 and February 2026, pricing for 16GB and higher GPUs has moved sharply upward. This article focuses only on cards relevant for serious local inference workloa...


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The OpenClaw ecosystem just split into two new directions. A Go rewrite called PicoClaw and a Rust implementation called ZeroClaw both claim to run on $10 class hardware, including Raspberry Pi type boards. The Mac mini is no longer part of the story. For local LLM enthusiasts who followed the recent OpenClaw security controversy, this is not just a performance refactor. It i...


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A recent pull request to llama.cpp is delivering a measurable performance jump for recently released Qwen3 Coder Next, with tests showing a significant increase in both prompt processing and next token generation speeds. The largest gains are in token generation, which directly impacts real time coding and chat workflows. The changes come from a compute graph rework that redu...


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