Can I Run Qwen?

Find out which Qwen AI models your hardware can run locally. This free tool auto-detects your GPU, estimates performance, and shows compatibility for every Qwen model — from tiny 0.6B to massive 397B.

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* Auto-detected specs. You can adjust any value manually above.
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Model ▲▼ Family ▲▼ Grade ▲▼ Params ▲▼ VRAM ▲▼ Speed ▲▼ Best Quant ▲▼ Download
How It Works

This tool uses your GPU's VRAM capacity and memory bandwidth to estimate how well each Qwen model will run on your hardware. Here's the process:

  1. GPU Detection: We use WebGL to auto-detect your graphics card. You can also select it manually from the dropdown.
  2. VRAM Calculation: Each model's GGUF file size plus ~1.5 GB overhead (for KV cache and runtime) gives the total VRAM needed.
  3. Speed Estimation: LLM inference is memory-bandwidth-bound. We estimate tokens per second using your GPU's bandwidth, an efficiency factor, and the model's memory footprint.
  4. Grade Assignment: Based on speed and VRAM headroom, each model gets a grade from A (excellent) to F (can't run). The recommended quantization is the highest quality that achieves the best possible grade.

For Apple Silicon Macs, the tool accounts for unified memory architecture — the OS and GPU share the same RAM pool, so the thresholds are adjusted accordingly.

Understanding Quantization

Quantization reduces a model's precision to make it smaller and faster. GGUF is the standard format for running LLMs locally with tools like Ollama, LM Studio, and llama.cpp. Here's what the common formats mean:

FormatBits/WeightQualityBest For
BF1616.0Original qualityResearch, when VRAM is not a concern
Q8_08.5Near-losslessBest quality with meaningful size reduction
Q6_K6.6ExcellentHigh quality with good compression
Q5_K_M5.7Very goodGreat balance for most users
Q4_K_M4.8GoodRecommended default — best size/quality trade-off
Q3_K_M3.9AcceptableWhen VRAM is very limited
Q2_K3.4LowExtreme compression, last resort

For most users, Q4_K_M is the sweet spot. If you have plenty of VRAM, step up to Q8_0 for near-original quality. The S/M/L variants (e.g. Q3_K_S vs Q3_K_M vs Q3_K_L) trade off size for quality within the same bit range.

Quick VRAM Guide

Not sure which GPU you need? Here's a quick reference for running the most popular Qwen models at Q4_K_M quantization:

VRAM AvailableBest Qwen Models (Q4_K_M)
6 GBQwen3-0.6B, Qwen3.5-0.8B, Qwen3.5-2B, Qwen3-4B
8 GBQwen3.5-4B, Qwen3-4B, Qwen3-8B (tight)
12 GBQwen3.5-9B, Qwen3-8B, Qwen2.5-7B
16 GBQwen3-14B, Qwen2.5-14B
24 GBQwen3.5-27B, Qwen3-32B (tight), QwQ-32B (tight), Qwen3.5-35B-A3B (tight)
32+ GBQwen3-32B, QwQ-32B, all MoE models up to 35B
48+ GBQwen2.5-72B, Qwen3-Coder-Next-80B
80+ GBQwen3.5-122B-A10B
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