{"meta":{"count":2,"page":1,"pages":1,"per_page":12,"applied":{"engine":null,"model":null,"quant":null,"storage":null,"hardware":["170hx-x6","rtx-5090"],"generation":null,"task":null,"tier":null,"tags":[],"vram":null,"search":null,"sort":"updated","featured":false},"origin":"https://localairecipes.com","openapi":"/openapi.json","updated_at":"2026-11-01T20:52:48+00:00"},"recipes":[{"slug":"deepseek-v4-1-flash-1017-streaming-nvme-stack-on-1x-rtx-5090-fro","title":"DeepSeek-V4.1-Flash-1017 streaming NVMe stack on 1x RTX 5090 (fronteagle739)","summary":"Streams a 483 GiB DeepSeek-V4.1-Flash-1017 pack from NVMe straight into one 32 GB RTX 5090, so a frontier-size MoE runs on a desktop with 32 GB of VRAM and 32 GB of system RAM. Windows-only: the stack is built on DirectStorage (a Windows API) and ships as PowerShell launchers plus a Windows ds4-server.exe, so prefill cannot run on Linux - see install_note. Packed-FP4 measures 200K real-text prefill at 43.2 s (4,632 tok/s) with 7.6-7.9 tok/s max-quality decode and 22.7 tok/s max-speed; Q4_K/Q8_0 measures 3,356 tok/s prefill and 7.5 / 17.8 tok/s decode. An optional 512 GB M3 Ultra can decode while the RTX prefills. Weights are never requantized on load; a quality/speed dial trades exactness for cached-expert reuse.","tier":"community","beta":false,"generation":"blackwell","decode_tok_s":12.699999999999999289457264239899814128875732421875,"prefill_tok_s":3994,"updated_at":"2026-10-31 12:32:41.796895-06","commit_sha":"1926a9bbc1be021dbef26cb9199faf4531de2b9f","content_hash":"f40ac13b6d6e0cf49620410496b08fb7ce1ae6f6","votes":0,"credit":[{"name":"fronteagle739 - recipe, streaming stack and measurements","url":"https://github.com/fronteagle739/streaming-llama-stack"},{"name":"llama.cpp / ggml (MIT)","url":"https://github.com/ggml-org/llama.cpp"},{"name":"antirez/ds4 (MIT)","url":"https://github.com/antirez/ds4"},{"name":"IngeniousIdiocy/ds4-v41-m3ultra (MIT)","url":"https://github.com/IngeniousIdiocy/ds4-v41-m3ultra"},{"name":"DeepSeek - DeepSeek-V4.1-Flash-1017 base model","url":"https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/deepseek-v4-1-flash-1017-streaming-nvme-stack-on-1x-rtx-5090-fro.json","alternate":"/r/deepseek-v4-1-flash-1017-streaming-nvme-stack-on-1x-rtx-5090-fro"}},{"slug":"deepseek-v4-1-flash-sm80-deepinfer-kernel-port-6x-a100-dspark-k5","title":"DeepSeek-V4.1-Flash SM80 DeepInfer kernel port 6x CMP 170HX DSpark K5","summary":"DeepSeek-V4.1-Flash served on six 64 GB SM80 GPUs (GA100 die, owner-classified 170HX) with HalfVulpes' Deepinfer-SM80 patch set: a 30-file vLLM V1 overlay porting DeepSeek's DeepInfer kernel architecture (SM100 tcgen05/TMEM) to Ampere mma.sync tensor cores - unified 512-dim SM80 sparse-MLA decode/prefill kernels with bitshift UE8M0 scale decoding, a bit-exact single-CTA Triton TopK+SWA combiner (22.17x), direct-store KV-cache gather (7.64x @48K), SM80-aligned TileLang mHC routing (2.89x), and YOCO L20-L39 ragged indexer-metadata reuse (19 redundant kernel chains eliminated per chunk). TP2 x PP3 (14/14/12) + DSpark K=5 + 1M context, 4.65M-token GPU-resident KV. Author-measured: 64K cold prefill 4,507 tok/s (+24.2% vs baseline vLLM SM80), 64K TTFT 10.7 s (-18.9%), c1 DSpark decode 160.8 tok/s code / 188.8 tok/s 16K+ long-gen, c4 170.5 aggregate. Microbenchmarks bit-exact (0 diff) or within BF16 noise. Topology guide: PP6 = 2.1x slower single-stream; TP2+TP4 asymmetric hurts Marlin tiles.","tier":"community","beta":false,"generation":"ampere","decode_tok_s":160.80000000000001136868377216160297393798828125,"prefill_tok_s":4506.8000000000001818989403545856475830078125,"updated_at":"2026-10-28 15:48:17.404025-06","commit_sha":"ced28259b90a43ec103ecd450b68455a56d2808d","content_hash":"e00265eca8b84734c2c7de2b67d558b0d702d25e","votes":0,"credit":[{"name":"HalfVulpes (Deepinfer-SM80 port + measurements)","url":"https://github.com/HalfVulpes/Deepinfer-SM80"},{"name":"DeepSeek-AI (DeepInfer engine architecture + DeepSeek-V4.1-Flash checkpoint)","url":"https://github.com/deepseek-ai/DeepInfer"},{"name":"vLLM project (V1 engine + TPxPP runtime)","url":"https://github.com/vllm-project/vllm"},{"name":"TileLang + OpenAI Triton (SM80 kernel synthesis)","url":"https://github.com/tile-ai/tilelang"},{"name":"FlashInfer + IST-DASLab Marlin (sparse-MLA + MXFP4/W4A16 Ampere GEMM primitives)","url":"https://github.com/flashinfer-ai/flashinfer"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/deepseek-v4-1-flash-sm80-deepinfer-kernel-port-6x-a100-dspark-k5.json","alternate":"/r/deepseek-v4-1-flash-sm80-deepinfer-kernel-port-6x-a100-dspark-k5"}}]}