{"meta":{"count":8,"page":1,"pages":1,"per_page":12,"applied":{"engine":null,"model":null,"quant":null,"storage":null,"hardware":["170hx-x3","170hx","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":"2026-10-31T17:37:00+00:00"},"recipes":[{"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-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":0,"prefill_tok_s":0,"updated_at":"2026-10-29 21:32:26.380514-06","commit_sha":"653270250a28129f993c556f74d24012fe5ee558","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":"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":"ds4-flash-3x170hx-pp3-dspark-k5","title":"DeepSeek V4 Flash-0731 abliterated on 3x CMP 170HX \u2014 vLLM patched PP3 DSpark K5","summary":"The proven 3-card pipeline-parallel recipe on CMP 170HX (sm_80, 64GB unlocked): PP=3 with manual layer partition 15,16,12 at util 0.98, FP8 KV, block-size 256, DSpark block drafting at exactly 5 speculative tokens, async scheduling, CPU pinning (dedicated physical cores, placeholder in launch args), hard memory cap 64g with swap disallowed. Structured decode 181 tok/s at 99% acceptance. Upstream reference repo: allover326/deepseek-v4-cmp170hx @ 3dd2d88 (Aug 13 2026) \u2014 documents 15,15,13 3-card split, retracted 4-card requirement, INT4 524288 ctx w/ DSV4_LOGITS_ROW_CHUNK=64, +7% decode via c3046d1 rebase patches (full source build required). First-party 15,16,12 numbers on this page are ours.","tier":"curated","beta":false,"generation":"ampere","decode_tok_s":129.900000000000005684341886080801486968994140625,"prefill_tok_s":2350,"updated_at":"2026-10-29 21:32:23.860633-06","commit_sha":"24a9ac90156459e4efb49c951590edbea775df38","content_hash":"02d56ece2249f2b3e2b1ee2ffce158e841e20d26","votes":0,"credit":[{"name":"allover326","url":"https://github.com/allover326/deepseek-v4-cmp170hx"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/ds4-flash-3x170hx-pp3-dspark-k5.json","alternate":"/r/ds4-flash-3x170hx-pp3-dspark-k5"}},{"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-flash-next-w4a16-3x-cmp-170hx-pp3-nvme-ple-fp8-kv-1m-cle","title":"Qwen3.8-Flash-Next W4A16 3x CMP 170HX PP3 NVMe PLE fp8 KV 1M (clee)","summary":"Working PP3 recipe for Qwen3.8-Flash-Next (Qwen4Exp 180B-A6B MoE, Intel INT4 W4A16 AutoRound) on 3x CMP 170HX (SM80, unlocked 64 GiB each): the 95.4 GiB BF16 PLE n-gram table streams live from NVMe (shard-direct preadv into a fixed GPU staging buffer, never fully loaded), fp8 e4m3 KV software-decoded on SM80, mamba SSM cache bf16, and 1,048,576-token context via YaRN x4 over the 262144-native checkpoint. KV pool 5.97M tokens (~5.7 concurrent 1M sessions). W4A16 experts run GPTQ-Marlin bit-exact; no GPU P2P on this card pairing, so PP stages through host RAM with NCCL_P2P_DISABLE=1 + --disable-custom-all-reduce. Measured cold-cache temp=0: prefill 2389 tok/s @4k rising to 4690 @1M (QSA sparse crossover past ~64k), decode 73.2 -> 59.8 tok/s @1M, TTFT 223 s @1M; exact needle recall at all depths through 1M. Fork clee/vllm-sm80 on a PINNED nightly; newer nightlies not validated.","tier":"community","beta":false,"generation":"ampere","decode_tok_s":73.2000000000000028421709430404007434844970703125,"prefill_tok_s":3563,"updated_at":"2026-10-20 15:34:25.555323-06","commit_sha":"fb3da33b3299da70eb24ce68a2c772de0dc1a356","content_hash":"0dea5f8b482a14a47e6cef76e85ad322d56f9821","votes":1,"credit":[{"name":"clee (recipe, fork patches, bench + needle harness)","url":"https://github.com/clee/qwen3.8-flash-next-3x170hx"},{"name":"clee (vllm-sm80 fork: units-fix, fp8-kv-sm80, ple-pp3, ple-nvme-staged)","url":"https://github.com/clee/vllm-sm80"},{"name":"Kai / 2Wild (Qwen38-Flash-Next-4x3090 \u2014 staged-PLE architecture inspiration)","url":"https://github.com/tonyd2wild/Qwen38-Flash-Next-4x3090"},{"name":"Intel (INT4 W4A16 AutoRound quant)","url":"https://huggingface.co/Intel/Qwen3.8-Flash-Next-W4A16-AutoRound"},{"name":"froggeric (bugfixed Qwen3.8 chat templates)","url":"https://huggingface.co/froggeric/Qwen-Fixed-Chat-Templates"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/qwen3-8-flash-next-w4a16-3x-cmp-170hx-pp3-nvme-ple-fp8-kv-1m-cle.json","alternate":"/r/qwen3-8-flash-next-w4a16-3x-cmp-170hx-pp3-nvme-ple-fp8-kv-1m-cle"}},{"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-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"}}]}