Qwen3.5 9B

User reports 69.8 t/s generation. Source evidence (verbatim): Source: https://www.fitmyllm.com/reports/rtx-3070-8gb # FitMyLLM original RTX3070 single-stream Qwen3.5 9B Q4_K_M measurement FitMyLLM first-party public report preview: measured on one rented NVIDIA GeForce RTX 3070 with 8GB VRAM, run finished 2026-08-29. Selected row Qwen3.5 9B Q4_K_M native decode 69.8 tokens/s, explicitly single stream (concurrent requests 1). llama.cpp build 91f8c9c5fb038c086e13e9cd823c29b33b07ba54, driver 595.71.05. The preview identifies seven successful models and two failed models, but exposes only five decode rates. Actual prompt/context depth, input/output token counts, repetitions, OS/CPU/core counts/system RAM, prefill rate, KVprecision and speculative-decoding settings are not disclosed for this selected row. Do not assign4K/8K/16K/32K depth sweep labels or1–8stream load tests from the paid report contents to this preview row. No exact tested HF repository, file checksum or model revision is linked; do not invent one or substitute a base-model reference. The runtime commit is a llama.cpp build revision, not a model revision. Original public source https://www.fitmyllm.com/reports/rtx-3070-8gb . Public methodology https://www.fitmyllm.com/methodology distinguishes first-party rented-hardware measurements from fitted estimates and describes headline rates as medians of independent runs; the specific repetition count and spread are not exposed in the preview. The methodology says raw JSON is freely accessible, while this report says its JSON requires purchasing the report; no raw API or paid material was accessed. Other public preview rows, separate deployments: Qwen3 4B Instruct2507 Q4_K_M 126; Llama3.1 8B Instruct Q4_K_M 80.9; Qwen3 8B Q4_K_M 78.4; Qwen3 8B Q3_K_M 73.3 tokens/s. These are not repetitions or ranges to average with the selected Qwen3.5 row.