Ornith1.5 35B (3B active) Splash

Source: https://github.com/HDZucht/Mac-Mini-M6-LLM-benchmarks Raw measured rows: https://github.com/HDZucht/Mac-Mini-M6-LLM-benchmarks/blob/3990a8e0fa5f2762911689439ce12e1c28d4c989/data/speed_2026-10-03.jsonl Hardware/method/model labels: https://github.com/HDZucht/Mac-Mini-M6-LLM-benchmarks/blob/cb99870e05aa9f5783c5c6987ddd9154834123ab/README.md Loaded context metadata: https://github.com/HDZucht/Mac-Mini-M6-LLM-benchmarks/blob/main/data/loaded_context_2026-10-03.jsonl Harness: https://github.com/HDZucht/Mac-Mini-M6-LLM-benchmarks/blob/main/bench_speed.py Original author: HDZucht / Hans-Dieter Zucht, with repository-credited automated measurements by Argus. Campaign dated October 3–4, 2026; these exact records are in the October 3 raw file. No local reproduction was performed. Headline: 61.5 tokens/s is the median of three English-prose runs (Ruhr economy). It is client-observed streaming decode throughput, not a pooled rate, overall mean, peak, native kernel result or prompt-processing rate. Five workload medians and all 15 runs are preserved separately below. Hardware: Apple Mac mini M6 (Mac18,5), 12 CPU cores (2 Super, 4 Performance, 6 Efficiency), 12 GPU cores, 32 GB unified memory, macOS 27.0.1. One machine, one model loaded alone. Model storage: ORICO 2 TB external NVMe in an ASMedia ASM246x enclosure, source-reported 40 Gb/s PCIe 4.0 x4. Apple automatic fan control. Per-run temperature, clock, power, swap, exact GPU offload and peak resident memory were not logged. Exact measured API alias: ornith-1.5-35b-a3b-splash. Display model: Ornith 1.5 35B-A3B. The source names Ornith 1.5 separately from the base Qwen family and compares its Splash and GGUF aliases. Exact equivalence of their weights is unverified. Source Format-column label: Splash. Exact measured quantization: unknown. Measured weight format: unknown; source label is not a confirmed weight container. Application: LM Studio. Backend: Splash. Exact app/backend versions are unknown. Do not turn QAT, MLX or Splash labels into an assumed Q4 quantization. A matching community Ornith Splash package is public. Its card describes a packed 4-bit conversion; the source alias does not identify this publisher or exact package. Do not populate measured quantization from it. Verified public related package/collection: https://huggingface.co/ezoushen/ornith-1.5-35b-a3b-splash Measured Hugging Face repository, filename, revision and hash are unrecorded. Public related-package links above prove availability only. The source identifies Splash speculative decoding with DFlash2 drafts. This is not evidence of a native MTP head setting. Exact draft checkpoint/version, target quantization and package manifest are unknown. The three Splash aliases are preserved as separate author-measured configurations, not pooled or assumed to share identical weights. Loaded context for this exact alias is 32,768 tokens. Requested load context was 32,768. Loaded capacity is not actual prompt length or a filled-context test. Actual input token counts are absent. Native prefill throughput/duration are unknown; keep PP and new-input-token fields blank. Method: one stream, max_tokens 600, temperature 0.7, reasoning_effort="none", three runs per prompt. A short Hallo warm-up precedes the measured prompts. The script includes emitted content and reasoning chunks; requested reasoning-off does not establish that reasoning was disabled in every backend/model. The harness computes (tokens - 1) / (stream_end - first_nonempty_content_time). It uses API completion_tokens when available and nonzero; otherwise it falls back to the number of nonempty streamed chunks. Raw records contain tokens, chunks, TTFT and generated text, but not the original usage object, finish_reason or total duration. Treat tokens as recorded output counts, with this usage/chunk fallback caveat. Chunk counts need not equal tokenizer token counts. Rates are the saved rounded client rates, not recalculated from TTFT. TTFT is end-to-end client latency, not native prefill duration. Prompts repeat identically across three repetitions; no nonce or explicit cache clear appears in the harness. Prefix-cache state/cached-token counts are unknown. These are not verified cold-prefill runs. A record of 600 equals the requested cap numerically, but finish_reason is not available. KV dtype, batching, microbatching, thread counts and exact GPU allocation are unreported. All 15 runs follow, in source repetition order. Fields: decode tokens/s; recorded output count; nonempty chunks; client TTFT seconds. German prose, Rhine geology (wissen_de), median 52.6 tokens/s: Run 0: 51.7 tokens/s; 537 output count; 536 chunks; TTFT 0.17 s. Run 1: 54.1 tokens/s; 537 output count; 536 chunks; TTFT 0.1 s. Run 2: 52.6 tokens/s; 530 output count; 529 chunks; TTFT 0.09 s. German prose, bead-based immunoassay (beads_de), median 59.7 tokens/s: Run 0: 57.2 tokens/s; 600 output count; 600 chunks; TTFT 0.17 s. Run 1: 59.7 tokens/s; 600 output count; 600 chunks; TTFT 0.1 s. Run 2: 64 tokens/s; 600 output count; 600 chunks; TTFT 0.1 s. English prose, Ruhr economy (prosa_en), median 61.5 tokens/s: Run 0: 64.7 tokens/s; 485 output count; 484 chunks; TTFT 0.15 s. Run 1: 59.4 tokens/s; 468 output count; 467 chunks; TTFT 0.08 s. Run 2: 61.5 tokens/s; 473 output count; 472 chunks; TTFT 0.08 s. Python ISO-8601 duration parser (code_py), median 119.9 tokens/s: Run 0: 119.9 tokens/s; 531 output count; 530 chunks; TTFT 0.17 s. Run 1: 124.6 tokens/s; 600 output count; 600 chunks; TTFT 0.12 s. Run 2: 117.5 tokens/s; 600 output count; 600 chunks; TTFT 0.12 s. JSON antigen/disease extraction (json_extr), median 142.6 tokens/s: Run 0: 142.6 tokens/s; 50 output count; 49 chunks; TTFT 0.2 s. Run 1: 139.5 tokens/s; 50 output count; 49 chunks; TTFT 0.12 s. Run 2: 198.3 tokens/s; 65 output count; 64 chunks; TTFT 0.11 s. Quality scope: The README reports the JSON task passed 3 of 3 automatic checks for this model. Prose/code quality was not generally graded. Throughput is not a quality score.