16GB VRAM is the sweet spot for local AI in 2026 — it’s what you get with an RTX 4090, RTX 4080, or a Mac with 32GB unified memory. Here are the best models that fit.

Best coding models under 16GB

Model	VRAM (Q4)	Best for	Setup
Codestral 22B	12GB	Autocomplete/FIM	ollama pull codestral:22b
Devstral Small 24B	14GB	Agentic coding	ollama pull devstral-small:24b
Qwen 2.5 Coder 14B	8GB	Coding breadth	ollama pull qwen2.5-coder:14b
DeepSeek Coder V2 Lite	9GB	Budget coding	ollama pull deepseek-coder-v2:16b

Best general models under 16GB

Model	VRAM (Q4)	Best for	Setup
Qwen 3.5 27B	16GB	Best all-rounder	ollama pull qwen3.5:27b
Gemma 4 27B	16GB	Google quality	ollama pull gemma4:27b
Qwen 3.5 9B	5GB	Fast + good	ollama pull qwen3.5:9b
Gemma 4 12B	7GB	Efficient	ollama pull gemma4:12b
Llama 4 Scout 17B	10GB	Large context	ollama pull llama4-scout

Best reasoning models under 16GB

Model	VRAM (Q4)	Best for	Setup
DeepSeek R1 14B	8GB	Math/logic	ollama pull deepseek-r1:14b
Qwen 3.5 14B	8GB	General reasoning	ollama pull qwen3.5:14b
MiMo V2 Pro 8B	5GB	Reasoning/coding	ollama pull mimo-v2-pro

The recommended 16GB setup

Run two models — one for autocomplete, one for chat:

// Continue.dev config
{
  "models": [{"provider": "ollama", "model": "qwen3.5:27b"}],
  "tabAutocompleteModel": {"provider": "ollama", "model": "codestral:22b"}
}

You can’t run both simultaneously on 16GB — Ollama swaps models automatically. For simultaneous use, you need 32GB+.

Quantization matters

All VRAM numbers above assume Q4_K_M quantization — the sweet spot between quality and size. Here’s how quantization affects a 27B model:

Quantization	VRAM	Quality loss
FP16	54GB	None
Q8_0	28GB	Negligible
Q4_K_M	16GB	~2-3%
Q4_0	15GB	~5%
IQ3_XS	11GB	~8-10%

For 16GB VRAM, Q4_K_M is the right choice. Going lower than Q4 introduces noticeable quality degradation, especially for coding tasks where precision matters.

Tips for maximizing 16GB VRAM

1. Close other GPU-hungry apps — Browsers with hardware acceleration, games, and video editors all eat VRAM. Close them before running models.
2. Use --num-gpu layers wisely — If a model barely doesn’t fit, you can offload a few layers to CPU. It’s slower but works.
3. Monitor with nvidia-smi — Check actual VRAM usage. Ollama’s estimates are sometimes off.
4. Consider context length — Longer contexts use more VRAM. A 27B model at 4K context fits in 16GB, but at 32K context it might not.

# Check VRAM usage
watch -n 1 nvidia-smi

# Limit context length in Ollama
ollama run qwen3.5:27b --num-ctx 4096

GPU recommendations for 16GB VRAM

The most common 16GB VRAM GPUs in 2026:

• RTX 4080 (16GB) — Best price/performance for local AI
• RTX 5060 Ti (16GB) — Newer, slightly faster
• RTX 4090 (24GB) — Overkill but future-proof (see our GPU guide)
• Mac with 32GB unified — Shares 16GB+ with GPU, great for Mac AI setups

Under 8GB VRAM?

See our best AI models under 4GB RAM guide for even smaller options.

FAQ

What AI models can I run with 16GB VRAM?

You can run most 27B parameter models at Q4 quantization, including Qwen 3.5 27B, Gemma 4 27B, and Devstral Small 24B. For coding specifically, Codestral 22B fits comfortably at 12GB. Smaller models like Qwen 3.5 9B only need 5GB, leaving room for other applications.

Can I run two models at once with 16GB VRAM?

Not simultaneously — you’d need 32GB+ for that. Ollama handles this by swapping models in and out of VRAM automatically. The swap takes 1-2 seconds, which is fine for alternating between autocomplete and chat models but not ideal for real-time parallel use.

Is 16GB VRAM enough for AI coding?

Yes, 16GB is the sweet spot for local AI coding in 2026. You can run high-quality coding models like Codestral 22B or Qwen 2.5 Coder 14B with excellent results. The only limitation is you can’t run the largest 70B+ models without heavy quantization. For those larger models, cloud GPU providers offer 48GB–80GB instances at a few dollars per hour.

What quantization should I use for 16GB VRAM?

Q4_K_M is the recommended quantization for 16GB setups. It reduces model size by roughly 75% compared to full precision while only losing 2-3% quality. Going below Q4 (like IQ3) introduces noticeable degradation, especially for coding tasks.

Related: Best GPU for AI Locally · Best AI Models for Mac · Cheapest Way to Run AI Locally