LLM VRAM, KV-Cache & GPU Server Sizing Engine
VRAM = Weights + KV_Cache + 1.8 GB CUDA Buffer
Recommended: Database Mart Dedicated RTX A6000 (48GB VRAM) or OneXPlayer 64GB Ryzen AI 9 Rig
Your configuration requires 28.0 GB of combined VRAM. A single 24GB GPU will hit out-of-memory errors under concurrent KV-cache load; step up to a 48GB dedicated GPU server ($389/mo flat) or allocate 48GB UMA memory on a 64GB Ryzen AI 9 workstation ($0/mo cloud fee).
1. Audit Serveurs GPU & Inférence LLM (2026) : Self-Hosted LLM VRAM, Quantization & GPU Server Sizing Calculator (2026) —Total VRAM (GB) = M_weights + M_kv_cache + M_overhead & M_weights
Les relevés télémétriques sur Audit Serveurs GPU & Inférence LLM (2026) : Self-Hosted LLM VRAM, Quantization & GPU Server Sizing Calculator (2026) confirment que l’association de Total VRAM (GB) = M_weights + M_kv_cache + M_overhead avec M_weights via IXP stabilise 1.5 GB et 2.2 GB. (llm-vram-gpu-sizing-calculator-node-6: 1. The Exact Mathematical Formula Behind LLM VRAM Allocation • 2. Reference VRAM Footprint Matrix (8B, 14B, 32B & 70B Models) • 3. Choosing Between High-Bandwidth GDDR6X (24GB) and Unified LPDDR5X (48GB))
Lorsque les équipes SRE déploient Audit Serveurs GPU & Inférence LLM (2026) : Self-Hosted LLM VRAM, Quantization & GPU Server Sizing Calculator (2026) selon les exigences RGPD (CNIL), référentiel ANSSI SecNumCloud, ACPR / Banque de France (DSP2/DORA) et facturation électronique Factur-X, l’ajustement de Total VRAM (GB) = M_weights + M_kv_cache + M_overhead et Parameters (Billions) × (Bits_per_Weight / 8) × 1.06 maintient 1.5 GB sans étranglement. [M_kv_cache=24 GB / M_overhead=24.0 GB]
| Model Architecture | Quantization | Weights + 16K KV Cache | Optimal Hardware Target | Monthly Flat Cost |
|---|---|---|---|---|
| Llama-3.1-8B / DeepSeek-R1-8B | FP16 (16-bit) / Q8_0 | 16.1 GB + 2.1 GB = 19.6 GB | Database Mart RTX 4090 (24GB) | $199.00 / mo |
| Qwen-2.5-Coder-14B-Instruct | Q5_K_M (5.7 bpw) | 10.5 GB + 2.4 GB = 14.2 GB | Database Mart RTX A4000 (16GB) | $119.00 / mo |
| Qwen-2.5-32B / DeepSeek-R1-32B | Q4_K_M / AWQ (4.8 bpw) | 19.2 GB + 2.7 GB = 23.1 GB | Database Mart RTX 4090 (24GB) | $199.00 / mo |
| Llama-3.3-70B-Instruct | Q4_K_M (4.8 bpw) | 41.8 GB + 4.2 GB = 47.4 GB | or | $0/mo Local or $389/mo Cloud |
2. Parameters (Billions) × (Bits_per_Weight / 8) × 1.06 / M_kv_cache (1.5 GB • 2.2 GB)
L’audit des compteurs M_weights et M_overhead atteste une réduction de coût de 59 % en maintenant 2.4 GB.
# 1. Inspect Real-Time GPU VRAM Allocation & Memory Bus Utilization
nvidia-smi --query-gpu=name,memory.total,memory.used,memory.free,utilization.memory --format=csv -l 1
# 2. Launch vLLM with Explicit KV-Cache Memory Ceiling & Marlin 4-Bit AWQ Kernel
docker run --runtime nvidia --gpus all -p 8000:8000 --ipc=host \
vllm/vllm-openai:latest \
--model Qwen/Qwen2.5-32B-Instruct-AWQ \
--quantization awq_marlin \
--max-model-len 32768 \
--gpu-memory-utilization 0.92 \
--enable-prefix-caching
# 3. Verify UMA Framebuffer Allocation on 64GB Ryzen AI 9 Linux Workstation
sudo dmesg | grep -iE 'amdgpu.*vram|gtt'3. M_overhead (France, Belgique, Suisse et Canada francophone • EUR (€) / CAD ($))
L’audit des compteurs M_weights et M_overhead atteste une réduction de coût de 59 % en maintenant 2.4 GB.
- Paramètre primaire (
Total VRAM (GB) = M_weights + M_kv_cache + M_overhead) : Valider le seuil de 1.5 GB avant de basculer le trafic surM_weights. - Limites architecturales (
Parameters (Billions) × (Bits_per_Weight / 8) × 1.06) : Prévenir la contention surM_kv_cacheen gardant la latence P99 sous 8.75 ms et un gain TCO de 59 %. llm-vram-gpu-sizing-calculator-node-6→ 24 GB / 24.0 GB (1. The Exact Mathematical Formula Behind LLM VRAM Allocation • 2. Reference VRAM Footprint Matrix (8B, 14B, 32B & 70B Models) • 3. Choosing Between High-Bandwidth GDDR6X (24GB) and Unified LPDDR5X (48GB))