{"meta":{"count":2,"page":1,"pages":1,"per_page":12,"applied":{"engine":null,"model":null,"quant":null,"storage":null,"hardware":["rtx-5090","dgx-spark-x3"],"generation":null,"task":null,"tier":null,"tags":[],"vram":null,"search":null,"sort":"updated","featured":false},"origin":"https://localairecipes.com","openapi":"/openapi.json","updated_at":"2026-11-01T01:38:03+00:00"},"recipes":[{"slug":"deepseek-v4-1-flash-1017-streaming-nvme-stack-on-1x-rtx-5090-fro","title":"DeepSeek-V4.1-Flash-1017 streaming NVMe stack on 1x RTX 5090 (fronteagle739)","summary":"Streams a 483 GiB DeepSeek-V4.1-Flash-1017 pack from NVMe straight into one 32 GB RTX 5090, so a frontier-size MoE runs on a desktop with 32 GB of VRAM and 32 GB of system RAM. Windows-only: the stack is built on DirectStorage (a Windows API) and ships as PowerShell launchers plus a Windows ds4-server.exe, so prefill cannot run on Linux - see install_note. Packed-FP4 measures 200K real-text prefill at 43.2 s (4,632 tok/s) with 7.6-7.9 tok/s max-quality decode and 22.7 tok/s max-speed; Q4_K/Q8_0 measures 3,356 tok/s prefill and 7.5 / 17.8 tok/s decode. An optional 512 GB M3 Ultra can decode while the RTX prefills. Weights are never requantized on load; a quality/speed dial trades exactness for cached-expert reuse.","tier":"community","beta":false,"generation":"blackwell","decode_tok_s":12.699999999999999289457264239899814128875732421875,"prefill_tok_s":3994,"updated_at":"2026-10-31 12:32:41.796895-06","commit_sha":"1926a9bbc1be021dbef26cb9199faf4531de2b9f","content_hash":"f40ac13b6d6e0cf49620410496b08fb7ce1ae6f6","votes":0,"credit":[{"name":"fronteagle739 - recipe, streaming stack and measurements","url":"https://github.com/fronteagle739/streaming-llama-stack"},{"name":"llama.cpp / ggml (MIT)","url":"https://github.com/ggml-org/llama.cpp"},{"name":"antirez/ds4 (MIT)","url":"https://github.com/antirez/ds4"},{"name":"IngeniousIdiocy/ds4-v41-m3ultra (MIT)","url":"https://github.com/IngeniousIdiocy/ds4-v41-m3ultra"},{"name":"DeepSeek - DeepSeek-V4.1-Flash-1017 base model","url":"https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/deepseek-v4-1-flash-1017-streaming-nvme-stack-on-1x-rtx-5090-fro.json","alternate":"/r/deepseek-v4-1-flash-1017-streaming-nvme-stack-on-1x-rtx-5090-fro"}},{"slug":"glm-5-3-flash-exlr8-exl3-experts-on-dgx-spark-tp1-4","title":"GLM-5.3-Flash exlr8 EXL3 experts on DGX Spark TP1-4","summary":"GLM-5.3-Flash exlr8: MoE layers are EXL3-trellis units decoded by the custom exlr8 MoE kernel; dense linears stay FP8/NVFP4. One release carries three expert widths (k2/k3/k4 = 2/3/4 bpw), chosen at boot via VLLM_EXLR8_WIDTHS. Reference topology TP=4 on four GB10 boxes; TP=1 serves the whole model (~87 GiB) on one 128 GB DGX Spark / ASUS GX10 via tp1.yaml plus a distinct MENTAT_GROUP. TP=2 and TP=3 also measured. 2026-10-19, clocks TP-locked at 2200 MHz: decode code 128.0 tok/s, prose 72.5 at TP=4; 53.0 / 29.5 at TP=1; prefill 4,393 tok/s at 8K, 4,190 at 32K. Trade vs the shipping NVFP4 build: decode +3-8%, NLL equal or better, KV pool ~+15%, prefill 7.7% lower at Second 1x-Spark GLM-5.3-Flash lane: not the VQ Q4_K GGUF lane (/r/glm-5-3-flash-vq-q4-k-1x-dgx-spark-1m-ctx-jlfn), and not a fork of the 2x ATLAS build (/r/glm-5-3-flash-nvfp4-on-2x-dgx-spark-atlas-rc4-2-tp2-dflash2-1m) whose NVFP4 Flash base this stack overlays.","tier":"community","beta":false,"generation":"blackwell","decode_tok_s":65.2000000000000028421709430404007434844970703125,"prefill_tok_s":3438.5,"updated_at":"2026-10-31 11:51:31.886893-06","commit_sha":"e576a851c441c37ac2fb42dfa0188eef41f910e0","content_hash":"237f8f6570a37cb5ddb895edbae6d611ef23660e","votes":0,"credit":[{"name":"kindlingai","url":"https://github.com/kindlingai/glm-5.3-flash-exlr8-gx10"},{"name":"nvidia","url":"https://huggingface.co/nvidia/GLM-5.3-Flash-NVFP4"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/glm-5-3-flash-exlr8-exl3-experts-on-dgx-spark-tp1-4.json","alternate":"/r/glm-5-3-flash-exlr8-exl3-experts-on-dgx-spark-tp1-4"}}]}