{"meta":{"count":35,"page":1,"pages":3,"per_page":12,"applied":{"engine":null,"model":null,"quant":null,"storage":null,"hardware":["170hx-x2","170hx-x4","dgx-spark"],"generation":null,"task":null,"tier":null,"tags":[],"vram":null,"search":null,"sort":"updated","featured":false},"origin":"https://localairecipes.com","openapi":"/openapi.json","updated_at":"2026-10-31T13:07:23+00:00"},"recipes":[{"slug":"deepseek-v4-1-flash-1017-hybrid-exl3-tp4-4x-cmp-170hx-morrowmake","title":"DeepSeek-V4.1-Flash-1017 Hybrid EXL3 TP4 4x CMP 170HX Morrowmake","summary":"DeepSeek-V4.1-Flash-1017 (748B MoE) on four CMP 170HX cards (sm_80): Morrowmake's release 1.0.0 recipe serves a hybrid checkpoint - routed experts from LibertAI's EXL3 2.9-bpw export, every other weight from DeepSeek's official FP8 1017 checkpoint (sha256-validated link build, no copies), and the model's ~189 GiB Engram tables in host RAM (VLLM_ENGRAM_PLACEMENT=auto, SSD fallback below ~212 GiB available). up to 319 tok/s one user structured (287 code / 162 prose), up to 765 across eight, up to 3,818 tok/s cold 2K prefill and a 1,247,406-token KV pool at 262,144 context, full-precision BF16 KV. Engine: Morrowmake's vLLM fork @ a0c57b52 on upstream 4e0681ed56 (sm_80 EXL3 + Marlin kernels, custom P2P all-reduce after a startup content check, DSpark speculative decoding 7/5 adaptive), pinned container image, TP4 with whole routed experts per card, optimised for PCIe x16 links. HumanEval 157/164, GSM8K 1,280/1,319, within test noise of the unmodified-checkpoint reference.","tier":"curated","beta":false,"generation":"ampere","decode_tok_s":162,"prefill_tok_s":3804,"updated_at":"2026-10-30 08:13:13.515043-06","commit_sha":"1d43d37b0b9cffa384845e62bcd3af425c04088c","content_hash":"79c2a1c9569e13d38af257c3a7c84e011e8a9cbe","votes":0,"credit":[{"name":"Morrowmake (recipe, engine fork, measurements)","url":"https://github.com/Morrowmake/dsv41-flash-cmp170hx-recipe"},{"name":"LibertAI - EXL3 2.9-bpw routed-expert quantization","url":"https://huggingface.co/LibertAIDAI/DeepSeek-V4.1-Flash-1017-EXL3-2.9bpw"},{"name":"DeepSeek - DeepSeek-V4.1-Flash-1017 base model (MIT)","url":"https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017"},{"name":"cmpunlocker (amoghmunikote) - P2P driver basis + minimal TRAP31 patch","url":"https://github.com/amoghmunikote/cmpunlocker"},{"name":"MiaAI-Lab - decode benchmark protocol (used as data)","url":"https://huggingface.co/Mia-AiLab"},{"name":"Marlin (IST-DASLab + Neural Magic contributors) - kernel basis","url":"https://github.com/IST-DASLab/marlin"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/deepseek-v4-1-flash-1017-hybrid-exl3-tp4-4x-cmp-170hx-morrowmake.json","alternate":"/r/deepseek-v4-1-flash-1017-hybrid-exl3-tp4-4x-cmp-170hx-morrowmake"}},{"slug":"glm-5-3-flash-max-effort-on-2x-cmp170hx-exl3-tp2-four-slots","title":"GLM-5.3-Flash max effort on 2x CMP170HX EXL3 TP2 four slots","summary":"Four native active sequences on two 64 GiB CMP170HX cards; GLM-5.3-Flash EXL3 3.05bpw, TP2, with the Weightless GLP44 steering vector at alpha 2, FP16 recurrent state, Q8 GPU KV, DFlash2 K7 and hybrid15. This recipe records max reasoning effort, selected per request with model GLM-5.3-Flash-max. llm-inference-bench v0.8.0, unmodified under a per-agent counting wrapper, measures 1, 2 and 4 agents on short and nominal 32K/128K inputs, one 30-second run per cell; actual tokenized input lengths are recorded. Native completion-token usage includes reasoning. Active RAM KV paging is disabled. The 655,360-token pool is shared, with 392,960 including output as the per-request cap. Throughput is inference speed, not an accuracy or intelligence score. Differences between the low, high and max recipes are not separated from run-to-run variation, and with ignore-EOS effort changes what the model writes, not how many tokens. Accepts optional chat_template_kwargs.clear_thinking (default-off).","tier":"community","beta":false,"generation":"ampere","decode_tok_s":41.5,"prefill_tok_s":0,"updated_at":"2026-10-29 23:29:37.835428-06","commit_sha":"663174ab27ccbc0f93296953b897b19e08b64726","content_hash":"1bf0157fd231d61ac68175f7a1493df8e9583746","votes":0,"credit":[{"name":"alexvnesta (tensor-parallel fork: TP2, hybrid drafting, concurrent sessions, RAM tier, vision, NCCL backend)","url":"https://github.com/alexvnesta/glm53-flash-cmp170hx-tp"},{"name":"Flun (original 2-card layer-split server + recipe this fork starts from)","url":"https://github.com/Flun/glm53-flash-cmp170hx-exl3"},{"name":"turboderp (ExLlamaV3 engine + GLM-5.3-Flash-exl3 checkpoint + GLM-5.3 TP + vision tower support in engine)","url":"https://github.com/turboderp-org/exllamav3"},{"name":"incoai (GLM-5.3-Flash-DFlash2 BF16 drafter, CC BY-NC-ND 4.0)","url":"https://huggingface.co/incoai/GLM-5.3-Flash-DFlash2"},{"name":"0xSero (k_hcfuse fused hyper-connection kernels, via Flun's repo)","url":"https://github.com/sybil-solutions/glm-flash-lite"},{"name":"Morrowmake (content-verified P2P check idea + benchmark prompt style)","url":"https://github.com/Morrowmake/glm53-flash-cmp170hx-recipe"},{"name":"bayley + arvkmr (fault probe idea + router-precision findings, vllm-170hx-glm5)","url":"https://github.com/bayley/vllm-170hx-glm5"},{"name":"Z.ai (GLM-5.3-Flash base model)","url":"https://huggingface.co/zai-org/GLM-5.3-Flash"},{"name":"msuiche (Weightless GLP-44 vector and specification)","url":"https://huggingface.co/msuiche/GLM-5.3-Flash-abliterated-cyber-GLP-44"}],"uploaded_by":"alex.v.nesta","measured":null,"links":{"self":"/api/v1/recipes/glm-5-3-flash-max-effort-on-2x-cmp170hx-exl3-tp2-four-slots.json","alternate":"/r/glm-5-3-flash-max-effort-on-2x-cmp170hx-exl3-tp2-four-slots"}},{"slug":"glm-5-3-flash-low-effort-on-2x-cmp170hx-exl3-tp2-four-slots","title":"GLM-5.3-Flash low effort on 2x CMP170HX EXL3 TP2 four slots","summary":"Four native active sequences on two 64 GiB CMP170HX cards; GLM-5.3-Flash EXL3 3.05bpw, TP2, with the Weightless GLP44 steering vector at alpha 2, FP16 recurrent state, Q8 GPU KV, DFlash2 K7 and hybrid15. This recipe records low reasoning effort, selected per request with model GLM-5.3-Flash-low. llm-inference-bench v0.8.0, unmodified under a per-agent counting wrapper, measures 1, 2 and 4 agents on short and nominal 32K/128K inputs, one 30-second run per cell; actual tokenized input lengths are recorded. Native completion-token usage includes reasoning. Active RAM KV paging is disabled. The 655,360-token pool is shared, with 392,960 including output as the per-request cap. Throughput is inference speed, not an accuracy or intelligence score. Differences between the low, high and max recipes are not separated from run-to-run variation, and with ignore-EOS effort changes what the model writes, not how many tokens. Accepts optional chat_template_kwargs.clear_thinking (default-off).","tier":"community","beta":false,"generation":"ampere","decode_tok_s":41.7000000000000028421709430404007434844970703125,"prefill_tok_s":0,"updated_at":"2026-10-29 23:29:37.671135-06","commit_sha":"7a55561ae92fcdeb4a011950b04c4848b7666861","content_hash":"1c7b1a323fae222fd633e500b576c9f1fe1e7cec","votes":0,"credit":[{"name":"alexvnesta (tensor-parallel fork: TP2, hybrid drafting, concurrent sessions, RAM tier, vision, NCCL backend)","url":"https://github.com/alexvnesta/glm53-flash-cmp170hx-tp"},{"name":"Flun (original 2-card layer-split server + recipe this fork starts from)","url":"https://github.com/Flun/glm53-flash-cmp170hx-exl3"},{"name":"turboderp (ExLlamaV3 engine + GLM-5.3-Flash-exl3 checkpoint + GLM-5.3 TP + vision tower support in engine)","url":"https://github.com/turboderp-org/exllamav3"},{"name":"incoai (GLM-5.3-Flash-DFlash2 BF16 drafter, CC BY-NC-ND 4.0)","url":"https://huggingface.co/incoai/GLM-5.3-Flash-DFlash2"},{"name":"0xSero (k_hcfuse fused hyper-connection kernels, via Flun's repo)","url":"https://github.com/sybil-solutions/glm-flash-lite"},{"name":"Morrowmake (content-verified P2P check idea + benchmark prompt style)","url":"https://github.com/Morrowmake/glm53-flash-cmp170hx-recipe"},{"name":"bayley + arvkmr (fault probe idea + router-precision findings, vllm-170hx-glm5)","url":"https://github.com/bayley/vllm-170hx-glm5"},{"name":"Z.ai (GLM-5.3-Flash base model)","url":"https://huggingface.co/zai-org/GLM-5.3-Flash"},{"name":"msuiche (Weightless GLP-44 vector and specification)","url":"https://huggingface.co/msuiche/GLM-5.3-Flash-abliterated-cyber-GLP-44"}],"uploaded_by":"alex.v.nesta","measured":null,"links":{"self":"/api/v1/recipes/glm-5-3-flash-low-effort-on-2x-cmp170hx-exl3-tp2-four-slots.json","alternate":"/r/glm-5-3-flash-low-effort-on-2x-cmp170hx-exl3-tp2-four-slots"}},{"slug":"glm-5-3-flash-4x170hx-tp4-pp4-morrowmake-141","title":"GLM-5.3-Flash on 4x CMP 170HX \u2014 Morrowmake recipe 1.8.0, TP4 DFlash2","summary":"PP4 (pipeline-parallel 4, TP1) as-run on a 4x CMP 170HX rig 2026-09-28 at --gpu-memory-utilization 0.93 after 0.95 OOM-killed warmup (a 72 MiB KDA prefill workspace did not fit in the ~70 MiB left on the last card). Author 1.8.0 measurements below are TP4 unless labeled PP4; KV pool 2,088,960 tokens under PP4 (author-published 1.8.0 figure). Recipe release 1.8.0 2026-10-16 (engine 819146023f, image engine-819146023f) \u2014 updated from 1.7.2; engine rebased on upstream vLLM 4e0681ed56, same context/batch/fair-prefill defaults; 21 engine-switch env names renamed (old names silently ignored at code defaults); container-only install; author re-measured throughput/KV tables at 1.8.0.","tier":"curated","beta":false,"generation":"ampere","decode_tok_s":264.3999999999999772626324556767940521240234375,"prefill_tok_s":6264,"updated_at":"2026-10-29 23:29:37.313564-06","commit_sha":"6e47f035b02d04cee06466adb7fb69b21d9d5240","content_hash":"171456f9f739f5c140a9a08da526b95e056ea9fe","votes":1,"credit":[{"name":"Morrowmake","url":"https://github.com/Morrowmake/glm53-flash-cmp170hx-recipe"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/glm-5-3-flash-4x170hx-tp4-pp4-morrowmake-141.json","alternate":"/r/glm-5-3-flash-4x170hx-tp4-pp4-morrowmake-141"}},{"slug":"glm-5-3-flash-high-effort-on-2x-cmp170hx-exl3-tp2-four-slots","title":"GLM-5.3-Flash high effort on 2x CMP170HX EXL3 TP2 four slots","summary":"Four native active sequences on two 64 GiB CMP170HX cards; GLM-5.3-Flash EXL3 3.05bpw, TP2, with the Weightless GLP44 steering vector at alpha 2, FP16 recurrent state, Q8 GPU KV, DFlash2 K7 and hybrid15. This recipe records high reasoning effort, selected per request with model GLM-5.3-Flash-high. llm-inference-bench v0.8.0, unmodified under a per-agent counting wrapper, measures 1, 2 and 4 agents on short and nominal 32K/128K inputs, one 30-second run per cell; actual tokenized input lengths are recorded. Native completion-token usage includes reasoning. Active RAM KV paging is disabled. The 655,360-token pool is shared, with 392,960 including output as the per-request cap. Throughput is inference speed, not an accuracy or intelligence score. Differences between the low, high and max recipes are not separated from run-to-run variation, and with ignore-EOS effort changes what the model writes, not how many tokens. Accepts optional chat_template_kwargs.clear_thinking (default-off).","tier":"community","beta":false,"generation":"ampere","decode_tok_s":43.2000000000000028421709430404007434844970703125,"prefill_tok_s":0,"updated_at":"2026-10-29 23:29:26.881493-06","commit_sha":"26ce83d34683309b83e4478edb3ed1213018d87f","content_hash":"58f06fe95f6c37780466d207181753f0f7c5b12f","votes":0,"credit":[{"name":"alexvnesta (tensor-parallel fork: TP2, hybrid drafting, concurrent sessions, RAM tier, vision, NCCL backend)","url":"https://github.com/alexvnesta/glm53-flash-cmp170hx-tp"},{"name":"Flun (original 2-card layer-split server + recipe this fork starts from)","url":"https://github.com/Flun/glm53-flash-cmp170hx-exl3"},{"name":"turboderp (ExLlamaV3 engine + GLM-5.3-Flash-exl3 checkpoint + GLM-5.3 TP + vision tower support in engine)","url":"https://github.com/turboderp-org/exllamav3"},{"name":"incoai (GLM-5.3-Flash-DFlash2 BF16 drafter, CC BY-NC-ND 4.0)","url":"https://huggingface.co/incoai/GLM-5.3-Flash-DFlash2"},{"name":"0xSero (k_hcfuse fused hyper-connection kernels, via Flun's repo)","url":"https://github.com/sybil-solutions/glm-flash-lite"},{"name":"Morrowmake (content-verified P2P check idea + benchmark prompt style)","url":"https://github.com/Morrowmake/glm53-flash-cmp170hx-recipe"},{"name":"bayley + arvkmr (fault probe idea + router-precision findings, vllm-170hx-glm5)","url":"https://github.com/bayley/vllm-170hx-glm5"},{"name":"Z.ai (GLM-5.3-Flash base model)","url":"https://huggingface.co/zai-org/GLM-5.3-Flash"},{"name":"msuiche (Weightless GLP-44 vector and specification)","url":"https://huggingface.co/msuiche/GLM-5.3-Flash-abliterated-cyber-GLP-44"}],"uploaded_by":"alex.v.nesta","measured":null,"links":{"self":"/api/v1/recipes/glm-5-3-flash-high-effort-on-2x-cmp170hx-exl3-tp2-four-slots.json","alternate":"/r/glm-5-3-flash-high-effort-on-2x-cmp170hx-exl3-tp2-four-slots"}},{"slug":"deepseek-v4-1-flash-1017-fidelity-first-4x-cmp-170hx-alanoo81","title":"DeepSeek-V4.1-Flash-1017 Fidelity-First 4x CMP 170HX alanoo81","summary":"DeepSeek-V4.1-Flash-1017 (748B MoE) on 4x CMP 170HX (sm_80, 64 GB each, PCIe Gen2 x16), TP4, as a fidelity-first variant of the GNmac-lab recipe 1.2.0 @ 2ad5741ab1 with four changes: routed experts of layers 18-22 grafted 2-bit to 3-bit (LibertAIDAI EXL3 3.5bpw shards 00020-00024-of-00044 into the 2.9bpw pack, all 40 layers 3-bit); no DSpark drafter (SPEC_TOKENS=0, frees ~2.8 GiB/card for KV); Engram tables genuinely loaded (ENGRAM_STORAGE=ssd from a 200 GiB RAM tmpfs); gpu_memory_utilization 0.98, KV pool 2,717,420 tokens at 262,144 context. Author-measured: 300/300 exact identifier copies, byte-identical repeats; perplexity -1.80% overall / -4.45% French vs the 2.9bpw pack (95% CIs exclude zero); ~105 tok/s decode c1, 449-560 tok/s aggregate c8; cold prefill 2,514 tok/s median. Documents a silent failure: leaving DSV41_ENGRAM_STORAGE unset (host mode) leaves the Engram tables unfilled - layers 1 and 14 act as identity functions, 15/20 copies, temp-0 output drift, no error.","tier":"community","beta":false,"generation":"ampere","decode_tok_s":105,"prefill_tok_s":2514,"updated_at":"2026-10-29 21:57:43.459028-06","commit_sha":"5af1460f4a2dce4f590f6fdfcd108119285e3e7c","content_hash":"67eb1810e7eea071e85e8edab747fd98247dd872","votes":0,"credit":[{"name":"alanoo81 (recipe, graft, measurements)","url":"https://github.com/alanoo81/deepseek-v41-flash-4x-cmp170hx"},{"name":"GNmac-lab (base recipe 1.2.0 @ 2ad5741ab1, MIT)","url":"https://github.com/GNmac-lab/deepseek-v41-flash-1017-cmp170hx"},{"name":"LibertAIDAI (EXL3 2.9bpw @ 0be63c33 / 3.5bpw @ 04a492fc)","url":"https://huggingface.co/LibertAIDAI/DeepSeek-V4.1-Flash-1017-EXL3-2.9bpw"},{"name":"Curtis White (hybrid layout + quantization_config.json)","url":"https://github.com/CurtisAccelerate/DeepSeekv41-Flash-1017-EXL3-CMP170HX"},{"name":"vcruz305/vllm-exl3 (AGPL-3.0-only, inside the built image)","url":"https://github.com/vcruz305/vllm-exl3"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/deepseek-v4-1-flash-1017-fidelity-first-4x-cmp-170hx-alanoo81.json","alternate":"/r/deepseek-v4-1-flash-1017-fidelity-first-4x-cmp-170hx-alanoo81"}},{"slug":"qwen3-8-flash-next-w4a16-int4ple-on-2x-modded-cmp-170hx-pp2-mtp3","title":"Qwen3.8-Flash-Next W4A16 INT4PLE on 2x modded CMP 170HX \u2014 PP2 MTP3 (DUFRelic)","summary":"Production 2x CMP 170HX recipe for Qwen3.8-Flash-Next: zankich INT4PLE repackage of halt95 Merlin W4A16 (94.4 GiB, 51B-param PLE table repacked INT4) on stock vLLM 0.30.0 + a 6-layer hash-guarded overlay stack (PP2/PLE #56444, KV filter #54793, MTP-over-PP #54709, slot statefix #55506, SpeedSuite 5-PR overlay, batched-P2P). PP2/TP1, MTP 3-token draft (measured 52.27% accept). Hardware is the recipe's own mod stack: 74-SM unlock (cmpunlocker), VBIOS 92.00.6D.00.0A, 170tune 1420 MHz SM cap + HBM 1728 MHz undervolt, BAR1 P2P driver patches, 140 W/card. Author-measured 2026-10-03 (median of 3, isolation audit): prefill C1 6213.7 tok/s @16k, decode C1 133.4 tok/s (SD 22.8 reported), decode C4 agg 245.2 tok/s; 55/56 C at 160 W peaks, no Xid/OOM. INT4 PLE table (~26.8 GiB) pinned in HOST RAM; KV BF16 (pinned build's QSA path rejects FP8). Driver mods optional for function but required for the published throughput.","tier":"community","beta":false,"generation":"ampere","decode_tok_s":133.400000000000005684341886080801486968994140625,"prefill_tok_s":0,"updated_at":"2026-10-29 21:32:28.370851-06","commit_sha":"c26a7fd62ded418a43f5a10abf82eaedb7afc6bc","content_hash":"7c6051a80e2a07b8bca384eb300935641c88349b","votes":0,"credit":[{"name":"DUFRelic","url":"https://github.com/DUFRelic/qwen38-flash-next-recipe"},{"name":"zankich (INT4-PLE repackage + plugin)","url":"https://github.com/zankich/vllm"},{"name":"halt95 (W4A16-Merlin base checkpoint)","url":"https://github.com/halt95/merlin-engine"},{"name":"michael-lgtm (public overlay rebuild)","url":"https://github.com/michael-lgtm/qwen38-flash-next-cmp-overlay"},{"name":"amoghmunikote (cmpunlocker SM unlock + BAR1 P2P)","url":"https://github.com/amoghmunikote/cmpunlocker"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/qwen3-8-flash-next-w4a16-int4ple-on-2x-modded-cmp-170hx-pp2-mtp3.json","alternate":"/r/qwen3-8-flash-next-w4a16-int4ple-on-2x-modded-cmp-170hx-pp2-mtp3"}},{"slug":"qwen3-8-flash-next-w4a16-autoround-dgx-spark-vllm-mtp4-iter7g","title":"Qwen3.8-Flash-Next W4A16-AutoRound DGX Spark vLLM MTP4 Iter7g","summary":"Recipe by kyaky. Qwen3.8-Flash-Next W4A16-AutoRound hybrid (INT4 experts + FP8 side layers + INT8 head; 51B-token n-gram PLE table on NVMe mmap) on one DGX Spark (GB10, 121 GB unified memory) on a verified-deterministic vLLM floor (image qwen38-flash-dgx:iter7g-20261015 on the digest-pinned vllm/vllm-openai:qwen38-flash-next base): MoE align arrival-order shim, drafter-Marlin atomicAdd fix, pinned triton config, compile-cache mount. Measured line v19a-w5 = MTP=4 window-5 block verification: 76.8 tok/s single-stream, 261.2 tok/s aggregate at 8 streams (+9.2% N1 / +4.8% N8 over MTP=3, two boots per arm, same image). Promoted serving line v20-fp8: fp8 KV cache on a 24 GB pool at native 262,144 context, 1,291,120-token capacity. Chinese +31.3% via a 25,862-id (39.5%) Han draft-vocab rebalance (prose-zhen 34.8 to 45.7 tok/s). Stochastic-correctness gate 37/37 vs bit-exact fp64 oracle; per-round raw data + scripts ship in-repo; copy-agentic-1 failure disclosed.","tier":"community","beta":false,"generation":"blackwell","decode_tok_s":61.2999999999999971578290569595992565155029296875,"prefill_tok_s":0,"updated_at":"2026-10-29 21:32:28.107205-06","commit_sha":"dc8982acabf5e6b6417f4871ad5149d61f01755d","content_hash":"e6a2cfdf4903c7ca1b80df3d0f496d3ca3f13840","votes":0,"credit":[{"name":"kyaky","url":"https://github.com/kyaky/qwen3.8-flash-next-gb10-ultrafast"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/qwen3-8-flash-next-w4a16-autoround-dgx-spark-vllm-mtp4-iter7g.json","alternate":"/r/qwen3-8-flash-next-w4a16-autoround-dgx-spark-vllm-mtp4-iter7g"}},{"slug":"qwen3-8-flash-next-nvfp4-dgx-spark-gb10-sglang-nextn-mtp","title":"Qwen3.8-Flash-Next NVFP4 DGX Spark GB10 SGLang NEXTN MTP","summary":"Production single-node DGX Spark (Grace Blackwell GB10, SM121, 128GB unified memory) recipe for Qwen3.8-Flash-Next on SGLang: ModelOpt NVFP4 quantization with zero-copy NVMe mmap of the 47.7 GiB ple_table.bin via --ple-offload-embedding (eliminates 51 GiB of unified-memory footprint), custom Triton FP4 prefill kernels + trtllm_mha decode kernels on SM121, NEXTN MTP (3 verification steps / 4 draft tokens) lifting output to 110-152 tok/s, GDN+QSA hybrid (131,072-token served window, 262k native capacity), 96-slot mamba recurrence buffers, sharp froggeric v22.1 Jinja chat template (-18% response length), persistent Triton/Inductor SM121 JIT caches on NVMe, 8-worker parallel NVMe pre-warm (206 shards in 17s) + 16-core Grace OMP. Author-measured vs same-box 27B dense baseline: 110.4-152.8 tok/s decode w/ MTP (baseline 12.1 base / 21.5 MTP), single-stream CoT 25.1-28.8 tok/s (2.62x speculative gain), TTFT ~347ms radix hit / ~465ms cold, 113 GiB / 121 GiB RAM used (~8 GiB headroom), 103.5 GiB","tier":"community","beta":false,"generation":"blackwell","decode_tok_s":110.400000000000005684341886080801486968994140625,"prefill_tok_s":0,"updated_at":"2026-10-29 21:32:27.869274-06","commit_sha":"2c5220aa85b5379d7373abd0aa14bb13b1ec993f","content_hash":"bf0c02f95b99494d6720a515e2f8f438f126bc87","votes":0,"credit":[{"name":"Michael Bemler (Bemler Labs)","url":"https://github.com/bemlerlabs/qwen3.8-flash-next-dgx-spark-sglang"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/qwen3-8-flash-next-nvfp4-dgx-spark-gb10-sglang-nextn-mtp.json","alternate":"/r/qwen3-8-flash-next-nvfp4-dgx-spark-gb10-sglang-nextn-mtp"}},{"slug":"qwen3-8-flash-next-nvfp4-2x-cmp-170hx-tep2-gds-ssd-ple-mtp6","title":"Qwen3.8-Flash-Next NVFP4 2x CMP 170HX TEP2 GDS SSD PLE MTP6","summary":"Recipe by nguyenthimy2022kg-alt. Community patched vLLM run of the NVIDIA Qwen3.8-Flash-Next NVFP4 checkpoint on 2x unlocked CMP 170HX (SM80, ~63.4 GiB usable each) with TEP2 tensor+expert parallel and fixed MTP6 speculative decoding with per-request draft isolation. v0.3.0 supports 1-10 active requests (max_num_seqs accepts 1-10, defaults to 1 when omitted; repo example sets 10) with compact/topk vocabulary transport and expanded PCIe communication; history-copy (ngram hybrid) drafting removed. The ~51.2 GB FP8 PLE table stays on NVMe, read GPU-direct via GDS/cuFile with 32 persistent readers and 3 staging slots - no CPU-memory relay; optional host-read PLE backend off by default. FlashInfer PCIe IPC AllReduce, 36-token prefix matching, bayley/cmpunlocker BAR1/P2P driver + host GDS. Measured upstream (2026-10-11, thinking off): 10 requests x 5,000 tokens, 749.67 tok/s aggregate, 76.29/request; historical 2026-09-08 window 151.05-168.29 tok/s decode at 8K-128K inputs xhigh thinking.","tier":"community","beta":false,"generation":"ampere","decode_tok_s":158.19999999999998863131622783839702606201171875,"prefill_tok_s":2059.90000000000009094947017729282379150390625,"updated_at":"2026-10-29 21:32:27.639988-06","commit_sha":"7d911c7dd2ce6b0a45d3c14791ee74036a43e7ae","content_hash":"247ee93d0bf55ac542edef7091844ac3df34a323","votes":0,"credit":[{"name":"nguyenthimy2022kg-alt","url":"https://github.com/nguyenthimy2022kg-alt/Qwen-Flash-SM80-170HX"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/qwen3-8-flash-next-nvfp4-2x-cmp-170hx-tep2-gds-ssd-ple-mtp6.json","alternate":"/r/qwen3-8-flash-next-nvfp4-2x-cmp-170hx-tep2-gds-ssd-ple-mtp6"}},{"slug":"qwen3-8-flash-next-fp4-local-1x-dgx-spark-vllm-b12x","title":"Qwen3.8-Flash-Next FP4-Local 1x DGX Spark vLLM b12x","summary":"Recipe by sojufx. Serves NVIDIA's nvidia/Qwen3.8-Flash-Next-FP4-Local (96 GiB NVFP4 quantization-aware-distilled checkpoint in a new compressed NVFP4 scale format) on one DGX Spark (GB10) via the digest-pinned eugr/spark-vllm-b12x community container (reported vLLM 0.1.dev21830+g8244d1c44.d20261015) - the model card requires community b12x containers until the format lands upstream. FP8 KV cache, exact MTP K=3 with standard rejection sampling, 262,144-token context, 4 scheduler slots, 4096-token batched prefill, disk-backed io_uring PLE, prefix cache with Mamba align, vision plus qwen3_xml tool calling, thinking off by default, localhost-only 127.0.0.1:8001 OpenAI-compatible backend with a deliberate no-replace/no-port-take guard. Measured 2026-10-15/16 on the author's Spark: 66.2 tok/s single-stream sparkDash counting decode, 166.4 tok/s at C4, 41-54 tok/s single-stream on the four-prompt wall-clock suite - different measurement bases, not interchangeable.","tier":"community","beta":false,"generation":"blackwell","decode_tok_s":66.2000000000000028421709430404007434844970703125,"prefill_tok_s":0,"updated_at":"2026-10-29 21:32:27.177524-06","commit_sha":"5fc0d101d8e9b1c6a1419f3d465e21649f4ecbad","content_hash":"be42677d0cc5ca7f71fe72210e756f1622fe3604","votes":0,"credit":[{"name":"sojufx","url":"https://github.com/sojufx/Qwen3.8-Flash-Next-FP4-Local-DGX-Spark-Recipe"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/qwen3-8-flash-next-fp4-local-1x-dgx-spark-vllm-b12x.json","alternate":"/r/qwen3-8-flash-next-fp4-local-1x-dgx-spark-vllm-b12x"}},{"slug":"nemotron-3-5-bolt-74b-a9b-1x-dgx-spark-vllm-b12x-lilicorr-262k","title":"Nemotron 3.5 Bolt 74B-A9B 1x DGX Spark vLLM b12x LiLiCorr 262K","summary":"NVIDIA Nemotron 3.5 Bolt 74B-A9B-24GB (hybrid Mamba-2/Transformer latent-MoE, 74B with 9B active, IQ2_XXS 2-bit experts, NVFP4 Mamba layers, Q8_0 rest; text+image+video in, 262,144-token window, OpenMDW-1.1) on one DGX Spark (GB10/SM121) with vLLM nightly-77154ca6 (base image vllm/vllm-openai@sha256:1ee85ca8) + b12x 1.5.1 kernels (--linear-backend b12x --moe-backend b12x), FP8 KV cache, gpu-memory-utilization 0.70, max-num-seqs 16, and the NVIDIA LiLiCorr MTP drafter at 7 speculative tokens. Pinned revisions: model c6cc2e8f, drafter 4c6a5613. Measured on the reference box: ~140 tok/s code rewrite / 63 summarise / 50 prose single-stream with the drafter (mean acceptance 4.0 tokens/pass), 31 tok/s without; 74.8/113.4/163.6/201.2 tok/s at 1/4/8/16 streams; KV cache ~6.52M tokens at the 0.70 share. On a 64 GB Spark the lane takes its own memory share 0.60: 667,874 KV tokens (2.55 windows), 110.8 tok/s greedy, 7.1 GiB left at the peak (2026-10-24).","tier":"community","beta":false,"generation":"blackwell","decode_tok_s":63,"prefill_tok_s":0,"updated_at":"2026-10-29 21:32:26.717952-06","commit_sha":"2cdb5f11ffa1fb1af8676496fdd631976c3d536d","content_hash":"28887fb2cf3bab1914c803e792898fff92b9afe0","votes":0,"credit":[{"name":"hasso5703","url":"https://github.com/hasso5703"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/nemotron-3-5-bolt-74b-a9b-1x-dgx-spark-vllm-b12x-lilicorr-262k.json","alternate":"/r/nemotron-3-5-bolt-74b-a9b-1x-dgx-spark-vllm-b12x-lilicorr-262k"}}]}