Qwen3.5 4B
on NVIDIA RTX 3080 10GB · llama.cpp · 704,000 ctx
Use cases
long-context
Summary
User tests Qwen3.5-4B Q4_K_M on an RTX 3080 10GB with llama.cpp built from upstream PR 29827, flash attention on and ubatch 64, on a RULER-style multikey needle task. Prefill at a 709,596-token prompt (704K, YaRN x2.75) was about 662 tok/s, 18.4 minutes per run, peaking at 9,784 MiB VRAM.
Stating the question before the document as well as after fixed all five configs that failed with question-last, at 256K and at 512K/704K with YaRN. One run per config, so the user calls it a strong signal on a small sample rather than a rate.
In 10 paired trials at 256K with question last, q8_0 KV cache passed 7/10 at ~7,730 MiB peak and q4_0 passed 6/10 at ~5,680 MiB; 9 of 10 pairs gave identical answers and McNemar exact p = 1.0. The user notes 114-170 MiB showed as shared memory, f16 cache does not fit at 256K on this card, and the model rambles after answering.