{"meta":{"count":4,"page":1,"pages":1,"per_page":12,"applied":{"engine":null,"model":null,"quant":null,"storage":null,"hardware":["dgx-spark-x2","rtx-5090"],"generation":null,"task":null,"tier":null,"tags":[],"vram":null,"search":null,"sort":"updated","featured":false},"origin":"https://localairecipes.com","openapi":"/openapi.json","updated_at":"2026-11-01T08:59:18+00:00"},"recipes":[{"slug":"glm-5-3-flash-nvfp4-on-2x-dgx-spark-atlas-rc4-2-tp2-dflash2-1m","title":"GLM-5.3-Flash NVFP4 on 2x DGX Spark - Atlas RC4.2 TP2, DFlash2, 1M","summary":"GLM-5.3-Flash NVFP4 (nvidia checkpoint) served by Enntity's Atlas engine with tensor+expert parallelism across two DGX Sparks over one ConnectX-7 cable, DFlash2 speculative decoding (incoai drafter), FP8-latent KV pool with prefix caching, up to 4 concurrent requests at per-request 1M context; opinionated ./start.sh stack (image from pinned engine commit, verified NVFP4 MTP overlay, optional display-carveout / disk prefix cache / KV shard) with RigMark benchmark receipts, bounded determinism (greedy output independent of concurrency), install + converter tests, and published cross-recipe comparison (RiNGSiDE vLLM TP2).","tier":"community","beta":false,"generation":"blackwell","decode_tok_s":56,"prefill_tok_s":0,"updated_at":"2026-10-31 12:36:48.458353-06","commit_sha":"c6672f65706eb7353091e662c24981e4b478cfa7","content_hash":"840bc43a5d941be08f419fe5a1b0e7411c3d32b5","votes":0,"credit":[{"name":"Enntity (Jason McCartney)","url":"https://github.com/Enntity"},{"name":"Reiner Schmidt (GLM-5.3-Flash Atlas port)","url":"https://github.com/Mango-kid/atlas"},{"name":"Atlas-Inf contributors (upstream base)","url":"https://github.com/Atlas-Inf/atlas"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/glm-5-3-flash-nvfp4-on-2x-dgx-spark-atlas-rc4-2-tp2-dflash2-1m.json","alternate":"/r/glm-5-3-flash-nvfp4-on-2x-dgx-spark-atlas-rc4-2-tp2-dflash2-1m"}},{"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"}},{"slug":"qwen3-8-flash-next-uncensored-nvfp4-2x-dgx-spark-sglang-tp2-roce","title":"Qwen3.8-Flash-Next Uncensored NVFP4 2x DGX Spark SGLang TP2 ROCE","summary":"Recipe by ttlogan. Production 2-node GB10 (ASUS GX10 / DGX Spark) cluster serve of jpezzulli/OrcaRouter-Qwen3.8-Flash-Next-Uncensored-ModelOpt-NVFP4 (OrcaRouter uncensored weights repacked to ModelOpt NVFP4 with the PLE n-gram table in FP8, ~126 GiB) on the MiaAI native vLLM stack --tp 2 2-node over ConnectX/ROCE (slug name says SGLang: historical identifier - the repo now runs vLLM at ec4d38624b), MTP speculative decoding, and a REQUIRED qwen4_exp tokenizer registry overlay (BPE tokenizer.json, NOT stock tokenizer behaviour). Podman rootless deployment with boot autostart and a deepseek/glm/qwen swap orchestrator. Measured numbers unchanged (no re-measurement in d5bd003301..ec4d38624b): 40.3 tok/s single, 117.3 tok/s agg at c=4 (the locked optimum), prefill ~2900-3000 tok/s flat; 30-min c=4 soak clean (2432 req / 0 fail / health 200). License notes: PixelML patches MIT; QSA fp8-KV overlay vendored AGPL-3.0-or-later from the single-Spark recipe.","tier":"community","beta":false,"generation":"blackwell","decode_tok_s":40.2999999999999971578290569595992565155029296875,"prefill_tok_s":2936.6999999999998181010596454143524169921875,"updated_at":"2026-10-28 16:04:52.791091-06","commit_sha":"1217f615379960f758d79b04ceb4ca2a71e8e6cc","content_hash":"db96361cfc2e1999cd1d8ce8f34678e090df5f9b","votes":0,"credit":[{"name":"ttlogan","url":"https://github.com/ttlogan/qwen38-flash-next-recipe"},{"name":"PixelML/MiaAI (SM121 QSA fallback + token-0 guard patches, MIT)"},{"name":"jpezzulli (OrcaRouter uncensored ModelOpt NVFP4 + FP8 PLE repack)","url":"https://huggingface.co/jpezzulli/OrcaRouter-Qwen3.8-Flash-Next-Uncensored-ModelOpt-NVFP4"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/qwen3-8-flash-next-uncensored-nvfp4-2x-dgx-spark-sglang-tp2-roce.json","alternate":"/r/qwen3-8-flash-next-uncensored-nvfp4-2x-dgx-spark-sglang-tp2-roce"}}]}