Local AI VRAM & Hardware Capability Workbench

Hardware & Model Config

Architecture Presets

GPU VRAM Target 8 GB

Quantization Format 4.5 bits/param

Context Window 4,096 tokens

Memory Bandwidth 288 GB/s

Typical PCIe/GPU memory bus throughput speed.

Host System RAM 16 GB

Batch Size (Concurrent) 1

Context: "LLM models that run within affordable PC limits can feel constrained... but domain-specific micro-models (STT, embeddings, detection) perform exceptionally well."

VRAM Allocation Stack Breakdown Total: 5.43 GB / 8.00 GB

Weights 4.58GB

KV 0.50GB

Act 0.35GB

Free 2.57GB

Model Weights 4.58 GB

KV Cache 0.50 GB

Activation Overhead 0.35 GB

Total VRAM Required 5.43 GB

Predicted Speed 38.2 tok/s Memory bandwidth bound

VRAM Utilization 67.9% Target: 8 GB Physical VRAM

KV Memory / Token 128 KB GQA (Grouped Query Attn)

Host Offload Risk 0.00 GB No PCIe bottleneck slowdown

Side-by-Side Micro-Model Efficiency Comparison High utility local models on consumer hardware

Model Name	Category	Parameters	VRAM Footprint	Estimated Speed / Latency	Consumer Viability

WebGPU Browser Acceleration Diagnostic

          Scanning local browser WebGPU device capabilities...
        