{"meta":{"count":5,"page":1,"pages":1,"per_page":12,"applied":{"engine":null,"model":null,"quant":null,"storage":null,"hardware":["rtx-3090-x2","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":"2026-10-31T22:54:38+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-1017-dsx-2-6-bit-gptq-2x-rtx-3090-engram","title":"DeepSeek-V4.1-Flash-1017 dsx 2.6-bit GPTQ 2x RTX 3090 (Engram)","summary":"DeepSeek-V4.1-Flash-1017 (552B MoE + 196B Engram) quantized to 187.6 GB single GGUF (2.6127 bpw routed-expert average: 5717 PTQ1_0 / 2545 Q2_0 / 7098 Q3_K per-expert GPTQ with allocator-spent bit budget; dense Q8_0 attention/shared expert/head, F16 embedding/compressor/indexer/Engram-wkv, F32 norms/router/mHC), served by dsx, a Rust+CUDA engine built for this checkpoint, on two RTX 3090s with 192 GB RAM. ~26 tok/s decode / ~170 tok/s prefill at --ctx 262144 with all 15,360 routed experts resident. Exact routing: no expert pruning, no router bias - the rank file orders placement only. Engram n-gram tables (~203 GB) are NOT quantized: read at runtime from the official safetensor shards by a prefetch thread. Hottest ~3,300 experts in VRAM across both GPUs (GPU0's set adapts to the conversation, DSX_ADAPT=8 swaps/token), remaining experts computed on 14 CPU threads from a hugepage RAM arena. Quality vs FP8/FP4 teacher (dsx eval, Lucebox 1017 set): KL 0.1972, top-1 82.59%, PPL 9.3626.","tier":"community","beta":false,"generation":"ampere","decode_tok_s":26,"prefill_tok_s":170,"updated_at":"2026-10-28 15:52:50.29183-06","commit_sha":"942f2d4b4ca3b3ca6697019daf09d9615a47b0af","content_hash":"57641e58a944d825b6d88744c42b9451d8ee916a","votes":0,"credit":[{"name":"lxe (dsx engine + 2bit reference quant)","url":"https://github.com/lxe/dsx"},{"name":"icecubetr (2.6-bit GPTQ quant + measurements)","url":"https://huggingface.co/icecubetr/DeepSeek-V4.1-Flash-1017-dsx-2.6bit"},{"name":"deepseek-ai (DeepSeek-V4.1-Flash-1017 checkpoint)","url":"https://huggingface.co/deepseek-ai/DeepSeek-V4.1-Flash-1017"},{"name":"Lucebox (1017 eval set)","url":"https://huggingface.co/Lucebox/DeepSeek-V4.1-Flash-1017-ROCMFP-GGUF"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/deepseek-v4-1-flash-1017-dsx-2-6-bit-gptq-2x-rtx-3090-engram.json","alternate":"/r/deepseek-v4-1-flash-1017-dsx-2-6-bit-gptq-2x-rtx-3090-engram"}},{"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":"glm-5-3-flash-exl3-3-05bpw-on-2x-rtx-3090-vllm-exllamav3-cpu-exp","title":"GLM-5.3-Flash EXL3 3.05bpw on 2x RTX 3090 \u2014 vLLM + ExLlamaV3 CPU expert offload, 256K (DominikBucko)","summary":"GLM-5.3-Flash (turboderp EXL3 3.05bpw) tensor-parallel on 2x RTX 3090 24 GB + 128 GB system RAM: vendor vLLM image with the spele1100/vllm-glm53 GLM-5.3 overlay, ExLlamaV3 @ 499890c with EXL3 MoE decode kernels, and CPU expert-offload pools (UVA exact host registers, per-layer LRU expert cache with the hottest 20% pinned, glm53_host.py pool planner, checkpoint lock + k-pool index installer). Bounded 4,096-token thinking budget + preserved reasoning for agent loops, 262,144-token context, prefix caching, OpenAI-compatible API. Docker image + systemd example, reproducible from repro.lock.json pins; measured up to 1,155 tok/s prefill and 47-50 tok/s decode with needles-at-32K/131K/260K + coherence gates passing.","tier":"community","beta":false,"generation":"ampere","decode_tok_s":49.39999999999999857891452847979962825775146484375,"prefill_tok_s":1130.5,"updated_at":"2026-10-20 21:52:36.396387-06","commit_sha":"c93c82b83e6832d41dd10a7037c607a3f4b53d1b","content_hash":"c807eb1b13116b97fedcab43cef2aad9dca165e7","votes":0,"credit":[{"name":"DominikBucko","url":"https://github.com/DominikBucko/glm53-flash-2x3090"},{"name":"turboderp-org (EXL3 quant + ExLlamaV3)","url":"https://github.com/turboderp-org/exllamav3"},{"name":"spele1100 (GLM-5.3 vLLM overlay)","url":"https://github.com/spele1100/vllm-glm53"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/glm-5-3-flash-exl3-3-05bpw-on-2x-rtx-3090-vllm-exllamav3-cpu-exp.json","alternate":"/r/glm-5-3-flash-exl3-3-05bpw-on-2x-rtx-3090-vllm-exllamav3-cpu-exp"}},{"slug":"qwen3-8-flash-next-w4a16-fp8ple-on-2x-rtx-3090-tp2-expert-offloa","title":"Qwen3.8-Flash-Next W4A16-FP8PLE on 2x RTX 3090 \u2014 TP2, expert-offload, MTP3 (DominikBucko)","summary":"Two-card RTX 3090 recipe for Qwen3.8-Flash-Next (125B MoE) in the albucino W4A16-FP8PLE build (Intel AutoRound INT4 backbone + FP8 PLE table + INT4 MTP draft, checkpoint @ ef55414): TP2 + expert parallel on the humming MoE path, UVA CPU offload (30 GB of cold experts in RAM), dynamic LRU expert cache (hot-84), BF16 KV at full 262,144-token context, MTP 3-token speculative draft, prefix caching. Author-measured (submitter T0, 128 GB RAM host): 131K prompt first token in 31.3 s and up to 4,191 tok/s prefill on the 8K-chunk prefill profile; agent 128K profile decodes 104.8-111.5 tok/s; fast 256K profile reads 260,096 tokens in 90.8 s (2,865 tok/s). Also ships 64 GB RAM and opt-in uncensored (abliteration) profiles. Engine is the pinned vLLM runtime image 0.1.dev20073+g8e685d198, republished as a release image pinned by digest; argv transcribed verbatim from scripts/serve-container.sh @ 939905b.","tier":"community","beta":false,"generation":"ampere","decode_tok_s":106.5,"prefill_tok_s":2947,"updated_at":"2026-10-20 15:34:08.646082-06","commit_sha":"bb7322bf4f7bdf564d617b1ab9f9d2985bf71877","content_hash":"5e0fd674c07293431c87af75dcd971af50a99d37","votes":0,"credit":[{"name":"DominikBucko","url":"https://github.com/DominikBucko/qwen38-flash-next-2x3090"},{"name":"albucino (checkpoint)","url":"https://huggingface.co/albucino/Qwen3.8-Flash-Next-W4A16-FP8PLE"},{"name":"Intel AutoRound (W4A16 target)","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"}],"uploaded_by":"localai-recipes","measured":null,"links":{"self":"/api/v1/recipes/qwen3-8-flash-next-w4a16-fp8ple-on-2x-rtx-3090-tp2-expert-offloa.json","alternate":"/r/qwen3-8-flash-next-w4a16-fp8ple-on-2x-rtx-3090-tp2-expert-offloa"}}]}