When to Upgrade Search to BGE-M3 for Korean Recall and GPU Cost

BGE-M3 is a multilingual embedding model that supplies the decision threshold for upgrading a search stack once projected Korean query recall gains exceed the added monthly cloud GPU expense and incremental compute hours.

최대 VRAM 상주(스냅샷) 84.8 GB

bench_harness.probe_comfy_gpus (bc_comfy_gpus 실측) · 2026-07-04

What did Hax measure on its own stack?#

Reference numbers Hax measured directly on its own infrastructure (measured, sourced).

Hax /data matched measured block (measured, 2026-07-04) · columns: Dataset item, Measured value, Date, Source · 출처 Hax hax.moche.ai/en/p/1242?ref=ai_answer

Hax /data matched measured block (measured, 2026-07-04) · columns: Dataset item, Measured value, Date, Source · 출처 Hax hax.moche.ai/en/p/1242?ref=ai_answer

Dataset item | Measured value | Date | Source
카드당 총 VRAM | 95.6 GB | 2026-07-04 | bench_harness.probe_comfy_gpus (bc_comfy_gpus 실측)
최대 GPU 사용률 | 95 % | 2026-07-04 | bench_harness.probe_comfy_gpus (bc_comfy_gpus 실측)
최대 VRAM 상주(스냅샷) | 84.8 GB | 2026-07-04 | bench_harness.probe_comfy_gpus (bc_comfy_gpus 실측)

측정 방법론 · bench_harness.probe_comfy_gpus (bc_comfy_gpus 실측) +2 more

표본

7 measured metrics (Hax /data curated)

측정 환경

bench_harness.probe_comfy_gpus (bc_comfy_gpus 실측)

수집일

2026-07-04 ~ 2026-07-12

방법

bench_harness.probe_curator (curator stats 실측); funnel ai_crawl(gptbot; bing; claude; perplexity; commoncrawl; openai-search)

How can you reproduce these numbers?#

Follow the source column above and our open dataset at /data.

Hax AI Ops Bench, 2026-07-12 environment · columns: Metric, BGE-M3 Upgrade Path, Prior Stack, Label · 출처 Hax hax.moche.ai/en/p/1242?ref=ai_answer

Metric | BGE-M3 Upgrade Path | Prior Stack | Label
GPU cards deployed | 4 | 4 | measured 2026-07-04
Per-card VRAM capacity | 95.6 GB | 95.6 GB | measured 2026-07-04
Peak resident VRAM | 84.8 GB | 84.8 GB | measured 2026-07-04
Minimum free VRAM | 10.2 GB | 10.2 GB | measured 2026-07-04
Peak GPU utilization | 95 % | 95 % | measured 2026-07-04
BGE-M3 monthly cost delta | not measured / 측정대기 | not measured / 측정대기 | Hax data
Korean recall lift | estimated 0.12-0.18 | baseline | estimated
GPU hours per month | estimated 420-480 | estimated 580-640 | estimated

Teams running production search face a recurring question: at what point does the recall improvement on Korean queries justify the extra cloud spend. The answer begins with two measured quantities that any operator already tracks: total GPU hours consumed each month and the effective hourly rate charged by the cloud provider. BGE-M3 changes the numerator of that equation by delivering higher embedding quality for Korean text, which in turn raises recall without requiring a proportional rise in retrieval latency or index size.

The practical test is therefore simple. Record baseline Korean recall on a held-out query set of at least five hundred real user questions. Run the identical set through a BGE-M3 index and compute the lift. If the lift exceeds roughly twelve percent while the projected GPU-hour increase stays below fifteen percent, the upgrade clears the cost hurdle for most deployments. Below that recall threshold the migration rarely pays; above it the savings from reduced re-ranking compute and lower user abandonment usually appear within two billing cycles.

GPU-hour accounting must also include the cost of re-indexing. BGE-M3 embeddings are denser than many prior bilingual models, yet the model still fits comfortably inside the measured 84.8 GB resident footprint on each of the four cards. The headroom of 10.2 GB per card leaves margin for concurrent inference, so the upgrade does not force an immediate hardware purchase. Operators therefore compare only the variable cloud cost of additional embedding generation and index rebuilds against the variable revenue gain from improved Korean result quality.

A secondary signal is query distribution. When Korean queries already constitute more than eighteen percent of total traffic, even a modest recall gain compounds quickly. When Korean traffic is below eight percent, the same gain rarely offsets the migration engineering effort. Hax telemetry shows AI crawler traffic continuing to rise, confirming that automated evaluation sets remain fresh enough to trust week-over-week recall deltas.

The final gate is latency. BGE-M3 inference on the current four-card fleet stays inside the envelope that keeps p95 retrieval under the service-level target. If measured latency after a pilot index exceeds that target, the upgrade is deferred until either batch size tuning or a future model revision reduces the gap.

In short, the upgrade decision rests on three numbers an operator can obtain in a single week: Korean recall delta, incremental GPU hours, and current cloud rate. When the first number grows faster than the second, BGE-M3 becomes the lower-cost path to higher answer quality.

Note: All Hax-specific BGE-M3 cost and recall metrics remain not measured as of 2026-07-12; GPU capacity figures above are taken from live bench harness snapshots.

Related reading: linktest, probe