{"meta":{"count":4,"page":1,"pages":1,"per_page":12,"applied":{"engine":null,"model":null,"quant":null,"storage":null,"hardware":["rtx-3090","dgx-station-gb300-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":"2026-10-31T06:47:11+00:00"},"recipes":[{"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-1017-fp8-tp1xpp2-dspark-2x-dgx-station-gb300","title":"DeepSeek V4.1 Flash 1017 FP8 TP1xPP2 DSpark 2x DGX Station GB300 RoCE vLLM 0.31 overlay","summary":"Pipeline-parallel TP1xPP2 + DSpark serving of DeepSeek V4.1 Flash (1017 official FP8 weights) across two DGX Station GB300 nodes over 400G CX8 RoCE. PP2 splits the 40 layers 20/20 and removes all 84 per-step all-reduces, replacing them with two point-to-point hops: single-stream temp-0 391 tok/s vs TP2 stock 290.5 (+34%), step 12.3 ms -> ~9.4 ms; aggregate soak 323/406/595/974 tok/s at C1/C2/C4/C8 (3.01x C1); GSM8K 97/100, 0 errors; 1M-token context, FP8 KV, Engram host table pinned in Grace RAM on rank 0. Ships the five-file vLLM v0.31.0 source-fix overlay (bind-mounted over stock vllm/vllm-openai:v0.31.0) that makes DSpark work under PP, plus preflight, launch scripts and soak/receipt verifiers. ALL perf numbers author-measured on their own pair (2026-10-25) - not reproduced locally; site has no GB300 hardware. Recipe by ebfio (port by Iris, a GLM-5.3 model, with Eduardo & Kestrel).","tier":"community","beta":false,"generation":"blackwell","decode_tok_s":357,"prefill_tok_s":0,"updated_at":"2026-10-27 16:38:42.712204-06","commit_sha":"2acdf45851ff045dc48344004a9ec5fde3af3de4","content_hash":"739e619707a224aa4cd8782dc5ed8d43f68dce86","votes":0,"credit":[{"name":"ebfio - DeepSeek V4.1 Flash PP2+DSpark GB300 pair recipe (port by Iris on 4x RTX Pro 6000, with Eduardo & Kestrel)","url":"https://github.com/ebfio/deepseek-v41-flash-pp2-pair"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/deepseek-v4-1-flash-1017-fp8-tp1xpp2-dspark-2x-dgx-station-gb300.json","alternate":"/r/deepseek-v4-1-flash-1017-fp8-tp1xpp2-dspark-2x-dgx-station-gb300"}},{"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":"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"}}]}