# 24GB vs 48GB GPU VRAM Sizing for Local Coding Agents & Vector RAG

> **Direct Answer (October 2026 Verified Scenario)**: 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.

- **Canonical URL**: https://pulse-forge.io/scenario/self-hosted-vllm-qdrant-rag-gpu-vram-sizing
- **Download Free Starter CLAUDE.md ($0)**: https://pulse-forge.io/api/launch-kit/free-claude-md?repo=ggml-org/llama.cpp
- **1-Click $4.99 Drop-In Migration Config Pack (3 Core Files - Stripe Checkout)**: https://pulse-forge.io/buy/micro_pass?repo=ggml-org/llama.cpp&source=md_scenario
- **1-Click $9.99 Complete 9-File Production Config .ZIP (Stripe Checkout)**: https://pulse-forge.io/buy/repo_pass_999?repo=ggml-org/llama.cpp&source=md_scenario_999
- **1-Click $49 Verified Leaderboard Boost (Stripe Checkout)**: https://pulse-forge.io/buy/instant_boost?repo=ggml-org/llama.cpp&source=md_scenario_49
- **Trigger Symptom**: OOM crashes when running 32B coding models alongside local embedding & vector indexes on 24GB GPUs
- **Root Cause**: Unbounded KV-cache context lengths and unquantized weights exceeding single-card 24GB VRAM envelopes.
- **Baseline Context Tokens/Turn**: `24,500` -> **Hardened**: `9,600` (`-58%`)
- **5-Engineer Monthly Savings**: `$390.00/mo`
- **Recommended Reference Repo**: `ggml-org/llama.cpp` (https://pulse-forge.io/launch-kit?repo=ggml-org/llama.cpp)