Fetching from the wire…
Infra2026-09-23 · source-backed
arXiv 2609.26333 trains a separate NVFP4 prefill checkpoint while decode keeps compact weights for memory traffic. On released GGUF decoders that's 32.5 points on MMLU-Pro and 35.3 on MMMU-Pro without touching the decode checkpoint, plus a 1.78x TTFT speedup at 8K prompts in llama.cpp by streaming prefill weights off SSD. Prefill and decode no longer have to share one quantization compromise, which is the most useful local-inference result of the week.
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The abliteration tool gained 215 stars to reach 30,103, but the stronger signal is downstream: the HF trending endpoint returns DavidAU/Qwen3.8-27B-TURBO-Fable-Cold-Fusion-735-882-Heretic-Uncensored-NM-DAU and Momoking/Qwen3-VL-32B-Heretic-MiniMax-H3-NVFP4, both naming the too...
A llama.cpp fork by fewtarius aimed at AMD APUs, iGPUs and handhelds adds a persistent SSD-backed KV cache with hot/warm/cold tiering. On an Ayaneo Flip KB running Qwen3.6-35B over a 15,700-token prompt, cold TTFT was 143.1 seconds versus 0.99 seconds warm, a 144.5x speedup. A...
PR #26062, "server: support MCP stdio," by ngxson, merged into ggml-org/llama.cpp on July 25 (r/LocalLLaMA). It landed alongside #26061 (vendored subprocess.h, merged July 24) and pwilkin's #26075 integration-and-tests PR. Until now, llama-server's web UI could only talk to MC...
Edge0-AI/Edge0, created September 8, went from 269 to 583 stars in two days. It packages SSD expert offload, Recover-LoRA and prerouter routing prediction into an MLX-backed framework: edge0-35b is a 4-bit 40-layer 256-expert model built on Qwen3.5-MoE 35B-A3B needing about 2....
AutoModelForCausalLM.from_pretrained("unsloth/Qwen3.5-4B-GGUF", gguf_file="Qwen3.5-4B-Q4_K_M.gguf") and everything after is the normal transformers API, with BF16, Q6_K, Q5_K_M and Q4_K_M supported. On a MacBook Pro M2 Max throughput came close to llama.cpp across three checkp...
A Hugging Face post dated September 10 documents 96+ hours across 1,000+ quantization configurations on Qwen 3.5 0.8B and 4B, producing per-tensor layout maps replacing the generic GGUF heuristics. Findings: token embeddings are 8-16x more sensitive to degradation than other w...
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