{"meta":{"count":6,"page":1,"pages":1,"per_page":12,"applied":{"engine":null,"model":null,"quant":null,"storage":null,"hardware":["dgx-spark-x4","rtx-3090"],"generation":null,"task":null,"tier":null,"tags":[],"vram":null,"search":null,"sort":"updated","featured":false},"origin":"https://localairecipes.com","openapi":"/openapi.json","updated_at":"2026-10-31T13:36:32+00:00"},"recipes":[{"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":"2026-10-29 21:32:25.385656-06","commit_sha":"2ddd4d836d8efa003f7203ba30597c20dc21e495","content_hash":"79db03010b10026e9788b7c749df25daf5fb38be","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":"glm-5-3-flash-exl3-3-05bpw-on-1x-rtx-3090-ram-nvme-expert-tierin","title":"GLM-5.3-Flash EXL3 3.05bpw on 1x RTX 3090 \u2014 RAM/NVMe expert tiering (near-exact)","summary":"320B MoE served on ONE 24 GB RTX 3090 with stock exllamav3 1.5.1 plus process-local monkeypatches: the 12,096 routed experts (42 MoE layers x 288, 8 picked per token per layer) tier across VRAM, pinned host RAM and a packed NVMe store. An elastic VRAM cache holds the ~1,300 hottest experts (12.4 GB); evicted experts move into a pinned no-duplicates RAM tier sized from the container cap; a 117 GB O_DIRECT record store covers the rest, with layer-ahead prefetch, a victim write-back ring and an AVX2 CPU lane computing RAM-resident decode picks in place. Modes: fast (all experts in RAM, ~218 GiB free, 28.2 tok/s, KL 0.005), nvme (55 GiB cap, 19.5 tok/s, KL 0.008; 16 GiB cap 14.7 lab / 14.38 sweep), nvme-exact (bit-exact, 8.3 tok/s), and an experimental Intel Arc Pro B70 peer-offload tier (GLM53_B70=1, 26.5 tok/s, KL 0.003 - not lab-run). 131,072-token KV, OpenAI API on port 30000, model id glm-5.3-flash, no output caps. Registry lab six gates pass at 55 GiB (PR #199); 16 GiB reported.","tier":"community","beta":false,"generation":"ampere","decode_tok_s":24.10000000000000142108547152020037174224853515625,"prefill_tok_s":1015,"updated_at":"2026-10-29 21:32:24.549403-06","commit_sha":"53520bbf5e06f65db6670e8693efb018f75026c8","content_hash":"65ab07a0787590e7c21331b006d4a13553080d02","votes":0,"credit":[{"name":"sybil-solutions (image, tiering runtime, omarchy plugin)","url":"https://github.com/sybil-solutions/glm-flash-lite"},{"name":"turboderp (ExLlamaV3 + EXL3 3.05bpw checkpoint)","url":"https://huggingface.co/turboderp/GLM-5.3-Flash-exl3"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/glm-5-3-flash-exl3-3-05bpw-on-1x-rtx-3090-ram-nvme-expert-tierin.json","alternate":"/r/glm-5-3-flash-exl3-3-05bpw-on-1x-rtx-3090-ram-nvme-expert-tierin"}},{"slug":"deepseek-v4-1-flash-on-4x-dgx-spark-tuned-tp4-sglang-dspark","title":"DeepSeek-V4.1-Flash on 4x DGX Spark \u2014 tuned TP4 SGLang/DSpark","summary":"","tier":"community","beta":false,"generation":"blackwell","decode_tok_s":89.7000000000000028421709430404007434844970703125,"prefill_tok_s":5650,"updated_at":"2026-10-29 21:32:23.622356-06","commit_sha":"ddf188add74be5de70f13f6f71735ec18a87c83c","content_hash":"647b167cbdd40dfd95cbaabc81e8624e929d847d","votes":0,"credit":[{"name":"knapcio (TP4 profile, v2.1-v2.3 production stack, release gates)","url":"https://github.com/knapcio/DeepSeek-V4.1-Flash-4x-DGX-Spark-TP4"},{"name":"MiaAI-Lab (upstream: launcher, Engram store, b12x routing, memory model)","url":"https://github.com/MiaAI-Lab/DeepSeek-v4.1-Flash-DGX-Sparks"},{"name":"0xSero (original 4x RTX PRO 6000 recipe skeleton boot.py derives from)","url":"https://github.com/sybil-solutions/deepseek-v4.1-flash-4x-rtx-pro-6000"},{"name":"kpham-sgl (sglang#39187 bounded dense-indexer prefill transient, backported as adapter/indexer_chunked*.py)","url":"https://github.com/sgl-project/sglang/pull/39187"},{"name":"BBuf + dsv4.1 branch contributors (#39370, #39646, #39648, #39653 decode kernel work in the canary images)","url":"https://github.com/sgl-project/sglang"},{"name":"rhys101 (SG17 RoCEnante overlay + SG18 prefill TP split from DeepSeek-V4.1-Flash-vLLM-DGX-Spark-8)","url":"https://github.com/rhys101/DeepSeek-V4.1-Flash-vLLM-DGX-Spark-8"},{"name":"local-inference-lab \u2014 Luke Alonso & Jason (b12x: RoCEnante one-shot RDMA all-reduce + fused MoE kernels)","url":"https://github.com/local-inference-lab/b12x"},{"name":"FujitsuPolycom + sparkring contributors (switchless-ring opposite-node paths, path-aware RoCEnante)","url":"https://github.com/FujitsuPolycom/sparkring"},{"name":"Saolence (#1/#2/#4/#5/#6: ring as opt-in switch, NCCL/build fixes, first-deployer review)","url":"https://github.com/knapcio/DeepSeek-V4.1-Flash-4x-DGX-Spark-TP4/pull/4"},{"name":"rsync/rchmagos (#8: RoCEnante on the switchless ring, DSV41_ROCE_RING + scripts/ring_mesh/)","url":"https://github.com/knapcio/DeepSeek-V4.1-Flash-4x-DGX-Spark-TP4/pull/8"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/deepseek-v4-1-flash-on-4x-dgx-spark-tuned-tp4-sglang-dspark.json","alternate":"/r/deepseek-v4-1-flash-on-4x-dgx-spark-tuned-tp4-sglang-dspark"}},{"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"}},{"slug":"glm-5-3-flash-nvfp4-4x-dgx-spark-switchless-ring-tp4-dflash2","title":"GLM-5.3-Flash NVFP4 4x DGX Spark switchless ring TP4 DFlash2","summary":"Recipe by Alex Ellis / OpenFaaS Ltd. GLM-5.3-Flash (320B-A18B, NVFP4) served tensor-parallel 4 across four DGX Spark (GB10, sm_121) nodes cabled as a closed switchless RoCE ring: two rails per node wired to two neighbours, the non-adjacent hop relayed through a neighbour \u2014 no switch on the data path or the procurement path. Collectives use canonical switchless-nccl v0.0.1 (NCCL 2.30.7 + two Apache-2.0 switchless patches + OpenFaaS hardening patch), LD_PRELOADed with pinned library sha256 and NCCL_ALGO=Ring; not SparkRing/SIRCL, distinct from the site's switched 4x Spark SGLang lane. vLLM on the public tonyd2wild sm121-v11-dflash2 image: TP4, marlin MoE, FP8 E4M3 KV at 12 GiB per rank (pool 1,576,246 tokens = 6.01x window), DFlash2 drafter k=7, glm47 tool + glm45 reasoning parsers, max-num-seqs 6, workers (ranks 3,2,1) headless first, then head rank 0 opens the single OpenAI-compatible endpoint on :8000. Same fabric also served GLM-5.2 (EXL3, QuantTrio) and DeepSeek-V4-Flash at TP4.","tier":"community","beta":false,"generation":"blackwell","decode_tok_s":45,"prefill_tok_s":1971.59999999999990905052982270717620849609375,"updated_at":"2026-10-20 21:52:36.421715-06","commit_sha":"3ec203d32cde9b249d3e88f040a8d0c79650c3db","content_hash":"9cf1d9bb8001b5e721793f15e201aee0b5916f6d","votes":0,"credit":[{"name":"Alex Ellis / OpenFaaS Ltd","url":"https://github.com/alexellis/glm-5.3-flash-4x-dgx-spark-switchless"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/glm-5-3-flash-nvfp4-4x-dgx-spark-switchless-ring-tp4-dflash2.json","alternate":"/r/glm-5-3-flash-nvfp4-4x-dgx-spark-switchless-ring-tp4-dflash2"}},{"slug":"qwen3-8-flash-next-w4a16-fp8ple-on-1x-rtx-3090-cpu-cold-experts","title":"Qwen3.8-Flash-Next W4A16-FP8PLE on 1x RTX 3090 \u2014 CPU cold experts, MTP3, 128K (DominikBucko)","summary":"Single-card RTX 3090 recipe for Qwen3.8-Flash-Next (125B MoE) in the albucino W4A16-FP8PLE build (@ ef55414, same checkpoint as the 2x3090 runtime): the GPU holds attention + dense weights + the 32 busiest experts per layer, a pinned AVX2 CPU thread pool computes the other 480 experts straight from RAM during decode (GPU takes ~22% over PCIe), and prefill streams all experts through the GPU in 8,192-token chunks. INT8 KV cache fits the full 135,168-token context in 3.05 GB; MTP 3-token draft; PLE table read in place from NVMe. The most common local-AI machine \u2014 one 24 GB card + 64 GB RAM. Author-measured (submitter T0, release image): 131,099-token prompt reads at 2,106 tok/s (62 s to first token) then decodes 47.4 tok/s; 43-51 tok/s decode range on 32 cores; 32K prompt at 2,141-2,219 tok/s prefill. Same vLLM runtime as the 2x3090 recipe, repackaged for half the hardware, nothing pruned. Argv transcribed verbatim from scripts/serve-container.sh @ 10b24c2.","tier":"community","beta":false,"generation":"ampere","decode_tok_s":48.89999999999999857891452847979962825775146484375,"prefill_tok_s":2123.5,"updated_at":"2026-10-20 15:34:09.15642-06","commit_sha":"088542fd7b452ae34d7f6b0253b939a3ef6b350d","content_hash":"a354418ea2f8a6dbcef09d963e5901758b7dd549","votes":0,"credit":[{"name":"DominikBucko","url":"https://github.com/DominikBucko/qwen38-flash-next-3090"},{"name":"albucino (checkpoint)","url":"https://huggingface.co/albucino/Qwen3.8-Flash-Next-W4A16-FP8PLE"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/qwen3-8-flash-next-w4a16-fp8ple-on-1x-rtx-3090-cpu-cold-experts.json","alternate":"/r/qwen3-8-flash-next-w4a16-fp8ple-on-1x-rtx-3090-cpu-cold-experts"}}]}