[VERIFIED SCENARIO GUIDE] Vector: VRAM-ENVELOPE-GUARD
24GB vs 48GB GPU VRAM Sizing for Local Coding Agents & Vector RAG
DIRECT ANSWER: Sizing quantized Q4_K_M local inference alongside embedded LanceDB or Qdrant keeps total concurrent memory inside a single 24GB workstation GPU with zero cloud egress.
Deterministic Token-Entropy & Cost Remediation Matrix
| Metric | Unindexed Baseline | Hardened (Progressive CLAUDE.md + Router) |
|---|---|---|
| Trigger Symptom | OOM crashes when running 32B coding models alongside local embedding & vector indexes on 24GB GPUs | Eliminated (-58% to -64% Discovery Waste) |
| Context Tokens / Turn | 24,500 tokens/turn | 9,600 tokens/turn |
| 5-Engineer Net Monthly Savings | Uncapped Overage Exposure | +$390.00/mo Saved ($4,680.00/yr) |
1-Tap Verdict & TCO Simulator
Select your primary engineering priority:
Team Size:
✓ ACTIVE VERDICT: llama.cpp Q4_K_M + LanceDB / Qdrant + 8k KV-Cache Cap (Saves ~$312/mo for 5 Devs with valkey-cluster.prod.conf + Root CLAUDE.md)
Sizing quantized Q4_K_M local inference alongside embedded LanceDB or Qdrant keeps total concurrent memory inside a single 24GB workstation GPU with zero cloud egress.
Specimen #1 Unmasked Preview (`valkey-cluster.prod.conf` + `CLAUDE.md`) & Locked 9-File Production Deliverables ▾✓ P99 Load-Tested + Production Docker/Helm Audited • 30-Day Instant Refund Guarantee
# Specimen 01 Preview (llama.cpp Q4_K_M + LanceDB / Qdrant + 8k KV-Cache Cap • ggml-org/llama.cpp • 5 Devs): + [valkey-cluster.prod.conf] healthcheck: 5s | restart: unless-stopped | mem_limit: 2g (Production HA Spec) + [Verdict TCO] ggml-org/llama.cpp | route_65_35_cache: true | cap: $188/mo (Saves ~$312/mo)
valkey-cluster.prod.conf (HA Stack)
helm-values.prod.yaml + terraform.tf
prometheus-alerts.yml (SLO Gate)
promptfooconfig.yaml (50-Probe OWASP)
1-Tap Engineering Poll (Optional)
What is your main goal while comparing llama.cpp Q4_K_M + LanceDB / Qdrant + 8k KV-Cache Cap and its alternatives today?
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