Backlog F5: Qwen3-Embedding-4B measured, on llama.cpp and vLLM, and against bge-m3

Co-Authored-By: Claude Opus 5.5 (1M context) <noreply@anthropic.com>

BACKLOG.md

@@ -1085,6 +1085,21 @@ at a third of its CPUs.
   0.7) and a larger store machine.
 - Done: a batch of embeddings checked text by text against the context
   (branch `embeddings-batch`); it was refused as one over-long prompt.
+- Done: `query_prefix` on an embedding model, before questions only.
+- Qwen3-Embedding-4B on the RTX 6000 (2026-10-09): llama.cpp Q8 1,575
+  tok/s as first set up (32k cache, 16 slots), 2,700 with a 16k cache,
+  FlashAttention and ubatch 1–2k; f16 no better. vLLM fp16 (`--runner
+  pooling`) 6,200 direct, 4,700–4,900 through the gateway in the import,
+  and its vectors match llama.cpp's (cosine 0.9995). The 103,579 chunks
+  took 3 h. Wanted: an embedding model's GPU profile on vLLM and CPU
+  profile on llama.cpp, which needs a source per profile (the catalog has
+  one per model; the import swapped the entry by hand), and an admission
+  queue limit that suits bulk imports (30 s turned some away).
+- Retrieval on 40 questions written from the corpus, document in the top
+  5: bge-m3 31/40 (Turkish questions 13/21), Qwen3-Embedding-4B 37/40
+  (18/21), with its query prefix 36/40 and first place 31/40 instead of
+  28 (MRR 0.738 → 0.789 → 0.838). Next: translating the question with the
+  chat model, and a reranker.
 
 ## D. Facts worth knowing before starting