{"generated_at":"2026-10-31T17:35:50Z","notes":"Boards group by HARDWARE configuration (card family at the same device count; RTX PRO 6000 Max-Q and plain RTX PRO 6000 share a family) \u2014 \"what is the best on this rig\" is the question. Every model with a recipe for that hardware competes together; entries are ranked by the requested ?sort= mode (aa = Artificial Analysis Intelligence of the base model DESC, null/un-scored models last, then C1 single-stream decode per GPU DESC \u2014 the default; decode = C1 decode per GPU DESC; c8 = the exact-C8 decode cell DESC (null-C8 trails, then the C1 decode fallback orders the null-C8 tail); prefill = whole-rig prefill DESC; ttft = TTFT at C1 ASC, lower is better; votes = community vote count DESC (recipe-grain \u2014 votes attach to recipes, one per anon identity); added = recipe creation date DESC, newest first (when it first got listed); updated = last real revision DESC (the same updated_at the recipe twins expose \u2014 a view/vote/import never re-sorts the board); max_context = the recipe's OWN DECLARED max_context_len DESC (t_703c7037 recipe metadata carried on every entry as max_context_len \u2014 NOT a measurement; rows without the declaration sink LAST via null-trail, never first); evidence = measured run file before author-reported) plus a secondary ?then= level applied immediately after the primary (default then=decode, so the default chain is AA intelligence then decode speed) with evidence (measured before reported) and slug as final tiebreaks; board sections and entry order are identical to /leaderboard?sort=<same value> (entry order IS the rank \u2014 there is no rank field). Board SECTION order is hardware-only (generation, family, device count) and never changes with the sort. Boards carry their base model name plus its Artificial Analysis Intelligence index (the quality axis) with aa_index_version/aa_source_url provenance. evidence: reported in the UI for all rows; run-file-backed rows are marked in tooltips (API evidence values: measured = run file on record, reported = author-reported). aa_intelligence is the Artificial Analysis index of the base model (null = not scored); aa_index_version / aa_source_url give the version and page every value was read from. aa_source_url is the source of the EFFECTIVE aa value (measured rows: the recorded page; published rows: the register snapshot_url \u2014 the exact href the HTML AA cell renders; the raw published snapshot also ships as aa_published_source_url). aa is the EFFECTIVE AA value (t_b3adaed5): COALESCE(aa_intelligence, aa_published_index) \u2014 the exact number the HTML AA cell renders and the value sort=aa ranks on. Sorting entries by aa reproduces the page order exactly (null trails, as rendered). PROVENANCE TIERS (aa_provenance field): measured = a locally recorded index in aa_intelligence; published = no local record, the AA-published index is shown instead in aa_published_index with aa_published_methodology_version / aa_published_snapshot_url / aa_published_fetched_at provenance (from the append-only aa_scores register; newest fetched_at wins) \u2014 provenance is descriptive metadata on every row (it is NOT a rank level: a published index ranks by its number like any other), and carries in the number tooltip (the former visible AA published chip is retired for good \u2014 owner, Oct 6); unscored = neither, rendered n/a and trailing (unchanged honest trail).","meta":{"count":3,"sort":"aa","then":"decode","sort_chain":"aa>decode>prefill>evidence>slug","applied":{"hardware":["rtx-3090-x4","radeon-ai-pro-r9700","rtx-3090"]},"origin":"https://localairecipes.com","openapi":"/openapi.json"},"boards":[{"label":"NVIDIA RTX 3090 24GB \u00d74 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18:30:00-06","aa_resolved_slug":"qwen3-8-flash-next","aa_published_source_url":"https://artificialanalysis.ai/models/qwen3-8-flash-next","card_variant":"RTX 3090","decode_c1_tok_s":174.30000000000001136868377216160297393798828125,"decode_c4_tok_s":null,"decode_c8_tok_s":533.700000000000045474735088646411895751953125,"decode_c16_tok_s":null,"prefill_tok_s":5484,"ttft_ms_c1":960,"other_concurrencies":[1,8],"max_context_len":262144,"evidence":"claimed","votes":0},{"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 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RTX 3090 24GB \u00d71 (Ampere)","card_family":"rtx-3090","representative_card":"RTX 3090","vendor":"nvidia","device_count":1,"generation":"ampere","vram_gb":24,"entries":[{"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)","url":"/r/glm-5-3-flash-exl3-3-05bpw-on-1x-rtx-3090-ram-nvme-expert-tierin","beta":false,"base_model":"GLM-5.3-Flash","aa_intelligence":42,"aa":42,"aa_index_version":"Index v4.3.2","aa_source_url":"https://artificialanalysis.ai/models/glm-5-3-flash","aa_recorded_at":"2026-09-30 22:34:38.6629-06","aa_provenance":"measured","aa_published_index":42,"aa_published_methodology_version":"v4.3.2","aa_published_snapshot_url":"https://artificialanalysis.ai/models/open-source","aa_published_fetched_at":"2026-10-04 14:15:00-06","aa_resolved_slug":"glm-5-3-flash","aa_published_source_url":"https://artificialanalysis.ai/models/open-source","card_variant":"RTX 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