{"meta":{"count":5,"page":1,"pages":1,"per_page":12,"applied":{"engine":null,"model":null,"quant":null,"storage":null,"hardware":["rtx-3090-x4","dgx-spark-x2"],"generation":null,"task":null,"tier":null,"tags":[],"vram":null,"search":null,"sort":"updated","featured":false},"origin":"https://localairecipes.com","openapi":"/openapi.json","updated_at":"2026-10-31T18:51:01+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":"qwen3-8-flash-next-w4a16-merlin-on-4x-rtx-3090-tp2xpp2-ep-mtp3-f","title":"Qwen3.8-Flash-Next W4A16-Merlin on 4x RTX 3090 \u2014 TP2xPP2+EP, MTP3, FP8 KV, 925K pool (halt95)","summary":"Four-card RTX 3090 recipe for Qwen3.8-Flash-Next (125B MoE) in halt95's W4A16-Merlin build (Intel AutoRound INT4 + FP8 PLE + MTP draft, @ 17284bf): TP2 x PP2 with expert parallel, manual layer partition 25,23, FP8 KV with a 924,993-token pool (three full 262K sessions resident; block-size 4096, prefix-match-unit 64), MTP 3-token probabilistic draft with RecoverSSM, host-mapped PLE pull transport, host-resident embeddings, side-cache layout + Model Runner V2, 42 images/request. Agent-focused: 92.5% of follow-up prompt tokens from cache across 8 sessions, median follow-up first token 1.61 s (0.96 s solo). Maintainer-measured (submitter T0, v2.5.1 bench card 2026-10-06): 5,219-5,533 tok/s cold prefill 10K-261K, 158-174 tok/s single-stream decode (thinking on), 534 tok/s aggregate at 8 concurrent, 262K needle exact, GSM8K 198/200. Engine is a SOURCE BUILD: vLLM v0.30.0 + 97 commits (release bundle verified by commit AND tree hash); argv verbatim from scripts/serve-v2.5.sh @ 28cbb72.","tier":"community","beta":false,"generation":"ampere","decode_tok_s":168.099999999999994315658113919198513031005859375,"prefill_tok_s":5360,"updated_at":"2026-10-31 11:51:35.845609-06","commit_sha":"423a58839f188df961890110971ddb250e6fe6b5","content_hash":"b1b20a73286839a8b304e2a3bcb8ee2b35fd43e2","votes":0,"credit":[{"name":"halt95","url":"https://github.com/halt95/merlin-engine"},{"name":"Qwen (Qwen3.8-Flash-Next)","url":"https://huggingface.co/Qwen/Qwen3.8-Flash-Next"},{"name":"Intel AutoRound (INT4 experts)","url":"https://huggingface.co/Intel/Qwen3.8-Flash-Next-W4A16-AutoRound"},{"name":"RadixArk (FP8 PLE table)","url":"https://huggingface.co/RadixArk/Qwen3.8-Flash-Next-NVFP4"},{"name":"DominikBucko (MTP INT4 packing recipe)","url":"https://github.com/DominikBucko/qwen38-flash-next-2x3090"},{"name":"peakcrosser7 (Flash-Next vLLM branch)","url":"https://github.com/peakcrosser7/vllm/commits/release/qwen38next_offload"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/qwen3-8-flash-next-w4a16-merlin-on-4x-rtx-3090-tp2xpp2-ep-mtp3-f.json","alternate":"/r/qwen3-8-flash-next-w4a16-merlin-on-4x-rtx-3090-tp2xpp2-ep-mtp3-f"}},{"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"}},{"slug":"qwen3-8-27b-awq-int4-dspark-on-rtx-3090s-sglang-tp1-tp2-tp4","title":"Qwen3.8-27B AWQ-INT4 + DSpark on RTX 3090s \u2014 SGLang TP1/TP2/TP4","summary":"Validated SGLang serving for the dense Qwen3.8-27B hybrid GDN vision-language model (48 Gated-DeltaNet + 16 gated-attention layers) on RTX 3090 24 GB: one digest-pinned container whose TP env selects tuned 1x/2x/4x presets. AWQ-INT4 W4A16 (Marlin) beats FP8 on Ampere; DSpark speculative decoding (RadixArk 1.36B draft) on 2x/4x with auto-fallback on TP1. Image input everywhere, video on 2x/4x. Author-measured: 136.8 tok/s decode at 8k on 4x (169.8 aggregate at C8), 112.9 on 2x, 43.2 on 1x; up to 128k context on 4x.","tier":"community","beta":false,"generation":"ampere","decode_tok_s":127.099999999999994315658113919198513031005859375,"prefill_tok_s":1372.700000000000045474735088646411895751953125,"updated_at":"2026-10-20 21:52:36.534482-06","commit_sha":"26a1363966d9065a310e9ec7caf124206a25f3f2","content_hash":"3fa45739100d87065553d5fb598424a66d4742fb","votes":0,"credit":[{"name":"sybil-solutions / 0xSero (SGLang presets, image build, benchmarks)","url":"https://github.com/sybil-solutions/qwen-serve"},{"name":"cyankiwi (AWQ-INT4 W4A16 checkpoint)","url":"https://huggingface.co/cyankiwi/Qwen3.8-27B-AWQ-INT4"},{"name":"RadixArk (DSpark speculative drafter)","url":"https://huggingface.co/RadixArk/Qwen3.8-27B-DSpark"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/qwen3-8-27b-awq-int4-dspark-on-rtx-3090s-sglang-tp1-tp2-tp4.json","alternate":"/r/qwen3-8-27b-awq-int4-dspark-on-rtx-3090s-sglang-tp1-tp2-tp4"}}]}