AI VRAM Calculator

Mathematically precise memory planning dashboard supporting DeepSeek MLA, grouped attention formats, and real-time GPU cost profiling.

Model Parameters

B Params

Operational Scaling

5128,192 tokens128k
Requests

Hardware Topology

Memory Capacity Profile

10.6GB of 24GB
✅ CLUSTER HEURISTICS SAFE

The model fits into VRAM with a clean headroom buffer of 13.4 GB remaining. Safe to execute and allocate context spaces!

Memory Component Allocation
Model Weights (4.0 GB)
KV Cache (4.3 GB)
Activations (0.3 GB)
Framework/CUDA Context (2.0 GB)
Throughput63 tok/sEstimated generation speed
Latency (TTFT)10 msTime to first token response
Node Power Draw450 W5.1 kg CO₂ / day
Deployment Pricing$0.25 / hrEst. cloud pricing average

Auto Deployment Code Generator

python -m vllm.entrypoints.openai.api_server \
  --model meta-llama-3-1---3 \
  --tensor-parallel-size 1 \
  --gpu-memory-utilization 0.90 \
  --max-model-len 8192 \
  --kv-cache-dtype auto \
  --port 8000