{"meta":{"count":7,"page":1,"pages":1,"per_page":12,"applied":{"engine":null,"model":null,"quant":null,"storage":null,"hardware":["rtx-3090-x4","170hx","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":"2026-10-31T16:47:25+00:00"},"recipes":[{"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":"2026-10-29 21:32:28.596454-06","commit_sha":"d2343f0bc9d171b68356492709d3915f2e0eb610","content_hash":"8aefc67a1e986e515743a30617a02bbc169edc71","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":"qwen3-8-flash-next-3bpw-fp8-ple-1x-cmp-170hx-dflash2-recoverssm","title":"Qwen3.8-Flash-Next 3bpw FP8-PLE 1x CMP 170HX DFlash2 RecoverSSM","summary":"Qwen3.8-Flash-Next (125B MoE) 3 bpw AutoRound experts on ONE CMP 170HX card with the full 262,144-token context: 197.8/296.4/135.5 tok/s one user (structured/code/prose), 371 tok/s editing code, 799.5/791.3/586.4 across eight, up to 48 requests at once - 1,384 tok/s total with the default DFlash2 drafter (1,403 with the checkpoint's MTP head), 4,243 tok/s cold prefill, 292,784-token KV pool. Serves klee100's 3 bpw AutoRound checkpoint (3-bit g128 / 2-bit g64 experts, 47 GiB on card) with halt95's FP8 n-gram table in HOST RAM (48 GiB, hard-linked at build) on Morrowmake's vllm-cmp170hx fork @ 18305948acba (stock release image, digest-pinned) so the whole decode step is one CUDA graph on a 2016 dual-Xeon host. inco.ai's DFlash2 drafter (7/5/3 by request count) + the recipe's RecoverSSM + BF16 GDN-state patches (9 files mounted over the image) fit 48 concurrent requests on one card. Measured 2026-10-23 under the MiaAI-Lab protocol.","tier":"community","beta":false,"generation":"ampere","decode_tok_s":197.80000000000001136868377216160297393798828125,"prefill_tok_s":0,"updated_at":"2026-10-29 21:32:26.955646-06","commit_sha":"af7d7dde12376551476c8b9e36cd72c68dc32408","content_hash":"c82e80ffa9955d905b61bda381cf7d89db1350ba","votes":0,"credit":[{"name":"ElectroHobo","url":"https://github.com/ElectroHobo/qwen38-flash-next-3bpw-1x-cmp170hx-recipe"},{"name":"Morrowmake (vllm-cmp170hx engine + image + Qwen EXL3 recipe scripts this descends from, MIT)","url":"https://github.com/Morrowmake/vllm-cmp170hx"},{"name":"klee100 (3 bpw AutoRound checkpoint with its MTP head, made for a 64 GiB card; not involved in this recipe)","url":"https://huggingface.co/klee100/Qwen3.8-Flash-Next-AutoRound-3bpw-MTP"},{"name":"halt95 (W4A16-Merlin checkpoint whose FP8 n-gram table this recipe serves; not involved in this recipe)","url":"https://huggingface.co/halt95/Qwen3.8-Flash-Next-W4A16-Merlin"},{"name":"inco.ai (DFlash2 drafter, Apache-2.0)","url":"https://huggingface.co/incoai/Qwen3.8-Flash-Next-DFlash2"},{"name":"Intel (AutoRound quantisation) + RadixArk (FP8 n-gram table quantisation)","url":"https://huggingface.co/Intel/Qwen3.8-Flash-Next-W4A16-AutoRound"},{"name":"Qwen (Qwen3.8-Flash-Next base model, Qwen Community License 1.0)","url":"https://huggingface.co/Qwen/Qwen3.8-Flash-Next"},{"name":"MiaAI-Lab (benchmark protocol + category prompts, used as data)","url":"https://huggingface.co/Mia-AiLab"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/qwen3-8-flash-next-3bpw-fp8-ple-1x-cmp-170hx-dflash2-recoverssm.json","alternate":"/r/qwen3-8-flash-next-3bpw-fp8-ple-1x-cmp-170hx-dflash2-recoverssm"}},{"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":"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":"qwen3-8-27b-int8-mtp3-prefix-cache-on-1x-cmp-170hx-hynix","title":"Qwen3.8-27B INT8 MTP3 prefix-cache on 1x CMP 170HX Hynix","summary":"Qwen3.8-27B at 65-67 tok/s decode (74 structured) with MTP k=3 speculative decoding AND prefix caching simultaneously on a single NVIDIA CMP 170HX 64GB (Hynix HBM2e, GA100/sm80), 262,144-token context on fp8_e4m3 KV, 24/7 production on pinned upstream vLLM nightly 0.26.1rc1.dev1024+g1fe3a1571 \u2014 the first build where prefix caching and MTP spec decode coexist (GDN mamba-align). Cached TTFT on a 7K shared prefix drops ~4.0 s -> 1.44 s (33.5% hit rate) while decode gains 7-9% over the 0.27.1 fork fallback \u2014 the 'MTP vs prefix cache, pick one' tradeoff is gone. Load-bearing trio --async-scheduling + --max-num-batched-tokens 8192 + --max-num-scheduled-tokens 8192 (default chunked-prefill batch = 0.0% hit rate while looking healthy); --prefix-match-unit 400 halves cached TTFT further (must divide the 1600-token GDN block); batch budget 32000 OOMs and hard-wedges the card. Samsung-40GB side recipe (int8 + DFlash2, 97.8 tok/s) with an sm80 int8-repack CPU-fallback patch.","tier":"community","beta":false,"generation":"ampere","decode_tok_s":68.7000000000000028421709430404007434844970703125,"prefill_tok_s":0,"updated_at":"2026-10-20 15:34:22.585629-06","commit_sha":"ad3f55dab02cedfb2b187f7dcfc3f1647e0aaa0c","content_hash":"68f95048b88c132d1ddbffc8ceceaf5c38be5140","votes":0,"credit":[{"name":"ahnguyen17 (recipe, patches, probes, A/B benchmarks)","url":"https://github.com/ahnguyen17/cmp-170hx-vllm"},{"name":"lued (Qwen3.8-27B-INT8-W8A16-MTP checkpoint)","url":"https://huggingface.co/lued/Qwen3.8-27B-INT8-W8A16-MTP"},{"name":"vllm-project (vLLM upstream; #50021 GDN spec bounds, #45238 align-mode cache retention)","url":"https://github.com/vllm-project/vllm"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/qwen3-8-27b-int8-mtp3-prefix-cache-on-1x-cmp-170hx-hynix.json","alternate":"/r/qwen3-8-27b-int8-mtp3-prefix-cache-on-1x-cmp-170hx-hynix"}},{"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"}},{"slug":"qwen3-8-flash-next-3bpw-mtp-on-1x-cmp-170hx-ssd-ple-aio-iiiusi0n","title":"Qwen3.8-Flash-Next 3bpw MTP on 1x CMP 170HX SSD PLE AIO (iIIusi0n)","summary":"First single-CMP-170HX recipe for Qwen3.8-Flash-Next: the AutoRound 3bpw+MTP checkpoint on ONE 64 GiB SM80 GPU with the 95.37 GiB BF16 PLE table left ON the SSD \u2014 native Linux AIO async reads, 16K prompt read-ahead and a 512 MiB row cache stream it per-row while the model loads metadata-only, so the 176B MoE fits one card. Patch adds async PLE reads/read-ahead, metadata-only loading, CUDA-graph host breaks, mixed-bit expert handling and MTP layer remapping; KLD 0.1208 vs original weights. Three-round medians (128 output tokens, temp 0, thinking off, EOS ignored): 8K prefill 2,636 tok/s, single-request decode 111 tok/s, 667 tok/s aggregate at 16 concurrent. Honest state: 31 SSD/graph tests PASS, 134 requests no API errors, BUT an arithmetic-consistency issue remains open; context advertised 262K, timing tested only through 32K. Tested sparse (15 GiB RAM, 31 GiB swap) on ENTERPRISE SATA at 180 W. Documented swap: the orcarouter uncensored AutoRound 3bpw checkpoint runs the same stack.","tier":"community","beta":false,"generation":"ampere","decode_tok_s":111,"prefill_tok_s":2132,"updated_at":"2026-10-08 21:11:56.66944-06","commit_sha":"827bfe01549b43357fa06a8610ea5e64b55583b0","content_hash":"3a31e6a965360ba192fd941458c89f62f07e27e6","votes":0,"credit":[{"name":"iIIusi0n (recipe, vLLM ple-ssd patch set, AIO helper, benchmark harness)","url":"https://github.com/iIIusi0n/qwen3.8-flash-next-cmp170hx"},{"name":"Qwen (official Qwen3.8-Flash-Next base, Qwen Community License 1.0)","url":"https://huggingface.co/Qwen/Qwen3.8-Flash-Next"},{"name":"klee100 (AutoRound 3bpw+MTP quantized checkpoints, official + uncensored)","url":"https://huggingface.co/klee100/Qwen3.8-Flash-Next-AutoRound-3bpw-MTP"},{"name":"OrcaRouter (Qwen3.8-Flash-Next-Uncensored BF16 abliterated fine-tune)","url":"https://huggingface.co/orcarouter/Qwen3.8-Flash-Next-Uncensored"},{"name":"vLLM project (upstream vLLM @ a5a30471 the patch applies to)","url":"https://github.com/vllm-project/vllm"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/qwen3-8-flash-next-3bpw-mtp-on-1x-cmp-170hx-ssd-ple-aio-iiiusi0n.json","alternate":"/r/qwen3-8-flash-next-3bpw-mtp-on-1x-cmp-170hx-ssd-ple-aio-iiiusi0n"}}]}