Fetching from the wire…
Models2026-09-17 · source-backed
An r/LocalLLaMA builder patched vLLM to offload most of the KV cache to host RAM and reports 1M context on 3x RTX 3090: about 80 tok/s at short context, dropping to roughly 60 once QSA hits its 2,048-token budget and then staying flat as context grows, ~150 tok/s at four concurrent requests, 3,701 tok/s prefill at 248K. The argument is architectural rather than a trick. Decode speed is bound by the per-step memory read, and in the qwen4_exp family the layers reading the whole cache every step are sparse enough that the rest can sit across PCIe. Patches and model on the author's HF page; the claim generalizes to any qwen4exp-based model.
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Supports both an integrated MTP head and a separate -md draft file, with the poster reporting 45 to 90 tok/s on a 5090 with 128GB system RAM and working configs down to a 12GB 4070. A separate 2x3090 plus DDR4 build reports 37-41 t/s decode with UD-Q4_K_XL plus expert cache pl...
A Hugging Face repo packages the model with the n-gram lookup table offloaded to SSD and streamed. The credible reply in the thread: a builder on an RTX Pro 6000 running the RAM variant reports over 12k prefill and over 170 tok/s single-stream decode, plus 440 tok/s at concurr...
DeepSeek-V4-Flash-0731 landed July 31 under MIT with a DSpark speculative-decoding module attached. Terminal Bench 2.1: 82.7. Toolathlon-Verified: 70.3. DSBench-FullStack: 68.7. DeepSWE: 54.4. NL2Repo: 54.2. The model card claims it beats DeepSeek-V4-Pro (Preview) "despite its...
Part 4 of a running 2x3090 series: prefill was 80+ seconds to first token on an 8k prompt and 24 minutes on a 119k one, and releasing the 150-slot expert cache off the GPU during prompt processing bought the speedup. The comments are why it's here. A reader calculated the 2.8s...
Every flaky quantized agent I've debugged, I blamed the quantization. A paper published September 4 says I've probably been blaming the wrong layer (arXiv 2609.04748). The setup is clean. Model, decoding parameters, seed, request order and batch size all fixed. Requests issued...
A month of solo work produced quants for LongCat-Flash-Lite-Sparse, Qwen3.8-27B, Qwen3.5-122B-A10B with MTPs, Qwen3-Coder-Next and Laguna-S2.1 with vision. Getting there meant writing Heretic support for the architecture from scratch and then adding llama.cpp support, and main...
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