How much VRAM do I need to run AI locally?

It depends on the model size and quantization. A 7B model at Q4 needs ~5GB, fitting on 8GB GPUs. A 70B model at Q4 needs ~40GB. For most users, 12-16GB VRAM handles 7B-14B models well, which is the sweet spot for local AI in 2026.

What is quantization?

Quantization reduces model precision from 16-bit to 4-bit or 8-bit, shrinking VRAM requirements by 2-4×. Q4_K_M is the recommended balance — it retains ~78% quality while using ¼ the memory of full precision. Most users won't notice quality differences at Q4 or higher.

Can I run AI on a Mac?

Yes. Apple Silicon Macs (M1/M2/M3/M4) use unified memory shared between CPU and GPU. An M4 Pro with 24GB can run 14B models easily. M4 Max (48GB) handles 32B+ models. Use Ollama or LM Studio for the easiest setup.

What is CPU offloading?

When a model doesn't fit entirely in VRAM, some layers run on your CPU using system RAM. This works but is 5-10× slower than full GPU inference. A model showing 'offload' status will run, but expect 1-5 tokens/sec instead of 30-60.

What's the best GPU for running AI locally?

Best value: RTX 4060 Ti 16GB or RTX 3060 12GB. Best performance: RTX 4090 (24GB) or RTX 5090 (32GB). Best for large models: Apple M4 Max/Ultra (48-192GB unified memory) since VRAM capacity matters more than raw speed for running bigger models.

How fast will models run on my GPU?

Speed depends on memory bandwidth. An RTX 4090 achieves ~40-80 tokens/sec on 7B models. An RTX 4060 gets ~15-30 tokens/sec. For reference, human reading speed is ~4-5 tokens/sec, so even slower GPUs provide a usable experience for chat.

What is tokens per second?

Tokens per second (t/s) measures how fast the AI generates text. 1 token ≈ ¾ of a word. 20+ t/s feels instant, 10-20 t/s is comfortable, 5-10 t/s is usable, and below 5 t/s feels slow. For comparison, ChatGPT typically streams at 30-60 t/s.

Should I run AI locally or use an API?

Run locally if: you want privacy, have unlimited usage needs, or have a good GPU. Use API if: you need the largest models (GPT-4, Claude Opus), want zero setup, or your GPU is too small. Many users do both — local for daily tasks, API for complex work.

Can My PC Run This AI Model?

Check which AI models your GPU can run locally. Enter your hardware specs and see which LLMs fit in VRAM, with speed estimates and quantization recommendations.

✅ Updated April 2026· 22 models · 32 GPUs

🖥️ Select Your GPU

🟢

RTX 4060

NVIDIA · Ada Lovelace

GB VRAM

272

GB/s

⚙️ System Configuration

System RAM (GB)

Used for CPU offloading when model exceeds VRAM

Context Length (tokens)

Longer context = more VRAM for KV cache

Models Run

Need Offload

Won't Run

🏆 Best Model for Your GPU

Gemma 3 4B

✅ Runs GreatQ8_0 · 4.5GB / 8GB· ~34 tok/s

Fast, lightweight · 4B parameters · Gemma

Filter:Show only compatible22 models

Full Compatibility Results — RTX 4060

Model	Status	VRAM	Quant	Quality	Speed
Llama 3.2 3B3B Lightweight, edge devices	✅ Runs Great	6.5GB	FP16	🟢 Excellent	~23 t/s 🚀 Fast
Llama 3.2 1B1B Ultra-lightweight, mobile	✅ Runs Great	2.5GB	FP16	🟢 Excellent	~68 t/s ⚡ Blazing fast
Qwen 3 1.7B1.7B Edge / mobile	✅ Runs Great	3.9GB	FP16	🟢 Excellent	~40 t/s ⚡ Blazing fast
Qwen 3 4B4B Lightweight, fast	✅ Runs Great	4.5GB	Q8_0	🟢 Excellent	~34 t/s 🚀 Fast
Gemma 3 4B4B Fast, lightweight	✅ Runs Great	4.5GB	Q8_0	🟢 Excellent	~34 t/s 🚀 Fast
Qwen 3 14B14B Balanced performance	✅ Runs Great	6.7GB	Q3_K_M	🟡 Acceptable	~22 t/s 🚀 Fast
DeepSeek-R1 14B14B Balanced reasoning	✅ Runs Great	6.7GB	Q3_K_M	🟡 Acceptable	~22 t/s 🚀 Fast
Phi-4 14B14B Coding, STEM	✅ Runs Great	6.7GB	Q3_K_M	🟡 Acceptable	~22 t/s 🚀 Fast
Llama 4 Scout17B General chat, code	✅ Runs Great	5.8GB	Q2_K	🔴 Low	~26 t/s 🚀 Fast
DeepSeek-R1 7B7B Fast reasoning	⚡ Tight Fit	7.6GB	Q8_0	🟢 Excellent	~19 t/s ✅ Comfortable
Mistral 7B7B Fast, efficient	⚡ Tight Fit	7.6GB	Q8_0	🟢 Excellent	~19 t/s ✅ Comfortable
Qwen 3 8B8B Fast general use	⚡ Tight Fit	7.1GB	Q6_K	🟢 Excellent	~21 t/s 🚀 Fast
Gemma 3 12B12B Balanced quality	⚡ Tight Fit	7.3GB	Q4_K_M	🟢 Good	~20 t/s 🚀 Fast
Qwen 3 32B32B High quality, coding	🐌 CPU Offload	10.8GB	Q2_K	🔴 Low	~1 t/s 🐌 Slow
Gemma 3 27B27B Code, reasoning	🐌 CPU Offload	9.2GB	Q2_K	🔴 Low	~1 t/s 🐌 Slow
DeepSeek-R1 32B32B Reasoning, coding	🐌 CPU Offload	10.8GB	Q2_K	🔴 Low	~1 t/s 🐌 Slow
Mixtral 8x7B46.7B MoE efficiency	🐌 CPU Offload	15.8GB	Q2_K	🔴 Low	~1 t/s 🐌 Slow
Command R 35B35B RAG, enterprise	🐌 CPU Offload	11.9GB	Q2_K	🔴 Low	~1 t/s 🐌 Slow
Llama 3.3 70B70B Best open-source quality	❌ Won't Run	23.7GB	Q2_K	🔴 Low	—
Qwen 3 72B72B Top-tier multilingual	❌ Won't Run	24.4GB	Q2_K	🔴 Low	—
DeepSeek-R1 70B70B Reasoning, math	❌ Won't Run	23.7GB	Q2_K	🔴 Low	—
Command R+ 104B104B Enterprise, tool use	❌ Won't Run	35.3GB	Q2_K	🔴 Low	—

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Frequently Asked Questions

🖥️ Select Your GPU

🟢

RTX 4060

NVIDIA · Ada Lovelace

GB VRAM

272

GB/s

Model

Status

VRAM

Llama 3.2 3B3B

Lightweight, edge devices

✅ Runs Great

6.5GB

Llama 3.2 1B1B

Ultra-lightweight, mobile

✅ Runs Great

2.5GB

Qwen 3 1.7B1.7B

Edge / mobile

✅ Runs Great

3.9GB

Qwen 3 4B4B

Lightweight, fast

✅ Runs Great

4.5GB

Gemma 3 4B4B

Fast, lightweight

✅ Runs Great

4.5GB

Qwen 3 14B14B

Balanced performance

✅ Runs Great

6.7GB

DeepSeek-R1 14B14B

Balanced reasoning

✅ Runs Great

6.7GB

Phi-4 14B14B

Coding, STEM

✅ Runs Great

6.7GB

Llama 4 Scout17B

General chat, code

✅ Runs Great

5.8GB

DeepSeek-R1 7B7B

Fast reasoning

⚡ Tight Fit

7.6GB

Mistral 7B7B

Fast, efficient

⚡ Tight Fit

7.6GB

Qwen 3 8B8B

Fast general use

⚡ Tight Fit

7.1GB

Gemma 3 12B12B

Balanced quality

⚡ Tight Fit

7.3GB

Qwen 3 32B32B

High quality, coding

🐌 CPU Offload

10.8GB

Gemma 3 27B27B

Code, reasoning

🐌 CPU Offload

9.2GB

DeepSeek-R1 32B32B

Reasoning, coding

🐌 CPU Offload

10.8GB

Mixtral 8x7B46.7B

MoE efficiency

🐌 CPU Offload

15.8GB

Command R 35B35B

RAG, enterprise

🐌 CPU Offload

11.9GB

Llama 3.3 70B70B

Best open-source quality

❌ Won't Run

23.7GB

Qwen 3 72B72B

Top-tier multilingual

❌ Won't Run

24.4GB

DeepSeek-R1 70B70B

Reasoning, math

❌ Won't Run

23.7GB

Command R+ 104B104B

Enterprise, tool use

❌ Won't Run

35.3GB