Open Weights Hub Navigator & Local Inference Sizer

Hardware & Model Specs Zero-API local math

Model Architecture

Target Hardware

Quantization Scheme

Active Context Window 8,192 tokens

KV Cache Precision

Batch Size (Concurrent Streams) 1

VRAM Allocation Breakdown 6.53 GB / 24.00 GB

Model Weights (4.68 GB)

KV Cache (1.25 GB)

Runtime Overhead (0.60 GB)

Free Headroom (17.47 GB)

Base Architecture & Parameter Count	7.07 B (Qwen 2.5)
Attention Layers & GQA KV Heads	28 Layers · 4 KV Heads (GQA)
Weight VRAM Memory Footprint	4.68 GB
KV Cache Memory (Context: 8192, Batch: 1)	1.25 GB
CUDA Context & PyTorch Runtime Overhead	0.60 GB
Total Required Memory	6.53 GB
Estimated Inference Throughput	64.2 tokens/sec
Recommended Local Runner	llama.cpp / Ollama

Agentic Paper Summarization Task Simulator

Demonstrates agentic multi-pass abstract evaluation using compact models (such as Inkling-Small or Qwen-2.5) on Hugging Face ICML 2026 paper reproductions.

Ready. Click 'Run Synthesizer' to simulate prompt ingest, KV cache allocation, and abstract synthesis.

Generated Deployment Script & Config

# Initializing deployment generator...

Optimizing LLM KV Cache Memory

Your browser does not support video.

Read the explanation

During autoregressive generation, large language models store past key and value vectors so each step can reuse cached attention keys and values instead of regenerating them. This dynamic buffer is the KV cache. In this tool, KV cache size scales with context length and precision bytes. For Qwen 2.5 7B at eight thousand tokens, the tool displays a calibrated demo estimate of 1.25 gigabytes for FP16. Switching the KV Cache Precision control from FP16 to FP8 halves each element from two bytes to one. In this demo model, the estimated cache drops from 1.25 to 0.63 gigabytes, expanding available headroom.