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Apple M5 Max

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Headlines · 3

  1. Community tester: DFlash2 for Qwen3.8-Flash-Next gives +17% decode speed in llama.cpp v0.8.0

    A Reddit tester in r/LocalLLaMA reports that llama.cpp v0.8.0 is the first release supporting DFlash2 drafters for Qwen3.8-Flash-Next, after quantizing their own GGUFs of the drafter. Testing on an Apple M5 Max with Q4_K_XL showed a geometric mean of 87.9 tps for DFlash2 versus 75.1 tps for MTP, a 17% gain, with code prompts at 95.1 tps (+18%) and list prompts at 127 tps (+39%) but prose slightly lower at 51.9 tps (-3%). At around 25K context DFlash2 roughly equalled MTP with some drop-off, and the tester noted its Q4_0 draft head is only about 350MB versus 2.2GB for the ggml MTP head, though the required compute buffer offsets the VRAM savings.

    r/LocalLLaMA · 🔥 2
  2. Community dev ships DeepSeek V4.1 Flash 1017 quant for Dwarfstar with vision support

    Reddit user /u/returnity released a quantized build of DeepSeek v4.1 Flash 1017 for Dwarfstar (ds4), noting that the language model already loads but the vision tower required their pull request to work. The quant follows the recipe antirez used for the original DS4.1 Q2 build: 2-bit routed experts with 8-bit or higher for everything else, plus the original FP8 engram table, with the imatrix built from the 1017 checkpoint rather than the original. The developer says the wired backbone is just over 150GB, so it streams from SSD on 128GB systems while the roughly 200GB engram table is paged in on demand, reaching decode speeds up to 20 tokens per second on an M5 Max.

    r/LocalLLaMA · 🔥 0
  3. Liquid AI releases LFM2.5-24B-A2B, a 24B open-weight reasoning model for on-device agents

    Liquid AI released LFM2.5-24B-A2B on Oct. 13, an open-weight reasoning model with 24B total parameters and roughly 2B active per token (Liquid AI elsewhere cites 2.45B), aimed at running agents locally on laptops and PCs. It is the largest and final member of the LFM2.5 family, with the context window extended from 32K to 131K tokens, a vocabulary doubled from 65K to 128K for better non-Latin tokenization, pretraining scaled from about 17T to 33T tokens, and a switch to reasoning-only output with an explicit chain of thought. Liquid AI says it matches the larger Qwen3.6-35B-A3B on five of eight agentic and tool-use benchmarks (for example 48.09 versus 38.19 on LiveClaw), though those benchmarks and the speed figures (184 tokens/s on an Apple M5 Max) are vendor-run. Base and post-trained weights are on Hugging Face with day-one support in llama.cpp, MLX, vLLM, SGLang and ONNX.

    Liquid AI Blog · 🔥 0

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