How much VRAM does the NVIDIA RTX 4080 have?

The NVIDIA RTX 4080 has 16 GB of GDDR6X with 717 GB/s memory bandwidth.

What is the NVIDIA RTX 4080 best for?

With 16 GB of VRAM, the NVIDIA RTX 4080 handles smaller models (7B–14B) at Q4–Q5 quantization — ideal for entry-level local LLM experimentation and lightweight inference.

What LLMs can the NVIDIA RTX 4080 run locally?

The NVIDIA RTX 4080 can run 35 of the 80 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Llama 3.1 8B Instruct at NVFP4, Llama 3.2 3B Instruct at BF16, Llama 3.2 1B Instruct at FP32.

Can the NVIDIA RTX 4080 run Llama 3.3 70B Instruct?

The NVIDIA RTX 4080 can run Llama 3.3 70B Instruct with CPU offload at Q3_K_M quantization, but inference will be slower than native VRAM execution.

Can the NVIDIA RTX 4080 run Qwen 3.6 27B?

Yes. The NVIDIA RTX 4080 runs Qwen 3.6 27B natively in VRAM at Q2_K quantization, achieving approximately 39.2 tokens per second.

Can the NVIDIA RTX 4080 run Llama 3.1 8B Instruct?

Yes. The NVIDIA RTX 4080 runs Llama 3.1 8B Instruct natively in VRAM at NVFP4 quantization, achieving approximately 91.9 tokens per second.

NVIDIA RTX 4080

The NVIDIA RTX 4080 has 16 GB VRAM and 717 GB/s memory bandwidth. It can run 35 of our 80 tracked models natively in VRAM at 8k context.

With 16 GB GDDR6X, the NVIDIA RTX 4080 is a consumer-tier GPU that can run 35 models natively. It handles 30B-class models at Q4 quantization.

The NVIDIA RTX 4080 is an Ada Lovelace GPU with 16GB GDDR6X and 717 GB/s bandwidth. With 9,728 CUDA cores, it handles 7B–14B models at Q4–Q5 quantization entirely in VRAM. The 16GB cap means 27B+ models require aggressive quantization or CPU offload, positioning it as a strong mid-range option for local inference.

NVIDIA RTX 4080: 16GB GDDR6X at 717 GB/s — Ada's mid-range with strong bandwidth.

7B-14B models at Q4 native. 27-32B models fit tight at Q4. ~10-15 t/s for 7B, ~5-8 t/s for 14B.

Full CUDA support. Good 16GB option but 4060-Ti-16GB offers better value for LLM.

Vendor	NVIDIA
Architecture	Ada Lovelace
VRAM	16 GB
Memory type	GDDR6X
Memory bandwidth	717 GB/s
Compute backend	CUDA
Tier	Consumer
Released	2022
Models (native)	35 / 80
Models (offload)	14 / 80

Software: Full llama.cpp and Ollama support out of the box. CUDA 12.x recommended; driver ≥ 525 required.

Popular models for this GPU

Nemotron 3 Nano 30B Qwen3 30B-A3B (MoE)Gemma 2 27B Instruct Gemma 3 27B Instruct Qwen 3.6 27B

Models this GPU runs natively in VRAM (35)

Models that fit with CPU offload (14)

These use system RAM for layers that don't fit in VRAM — expect much slower inference.

Too large for this GPU (31)

Compare NVIDIA RTX 4080 with other GPUs

Continue reading

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Best LLMs for 16 GB VRAM GPUs (2026)

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NVIDIA RTX 40 Series for LLMs: Complete VRAM & Performance Guide

Frequently asked questions

How much VRAM does the NVIDIA RTX 4080 have?: The NVIDIA RTX 4080 has 16 GB of GDDR6X with 717 GB/s memory bandwidth.
What is the NVIDIA RTX 4080 best for?: With 16 GB of VRAM, the NVIDIA RTX 4080 handles smaller models (7B–14B) at Q4–Q5 quantization — ideal for entry-level local LLM experimentation and lightweight inference.
What LLMs can the NVIDIA RTX 4080 run locally?: The NVIDIA RTX 4080 can run 35 of the 80 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Llama 3.1 8B Instruct at NVFP4, Llama 3.2 3B Instruct at BF16, Llama 3.2 1B Instruct at FP32.
Can the NVIDIA RTX 4080 run Llama 3.3 70B Instruct?: The NVIDIA RTX 4080 can run Llama 3.3 70B Instruct with CPU offload at Q3_K_M quantization, but inference will be slower than native VRAM execution.
Can the NVIDIA RTX 4080 run Qwen 3.6 27B?: Yes. The NVIDIA RTX 4080 runs Qwen 3.6 27B natively in VRAM at Q2_K quantization, achieving approximately 39.2 tokens per second.
Can the NVIDIA RTX 4080 run Llama 3.1 8B Instruct?: Yes. The NVIDIA RTX 4080 runs Llama 3.1 8B Instruct natively in VRAM at NVFP4 quantization, achieving approximately 91.9 tokens per second.