How much VRAM does the NVIDIA RTX 4000 Ada have?

The NVIDIA RTX 4000 Ada has 20 GB of GDDR6 with 320 GB/s memory bandwidth.

What is the NVIDIA RTX 4000 Ada best for?

With 20 GB of VRAM, the NVIDIA RTX 4000 Ada 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 4000 Ada run locally?

The NVIDIA RTX 4000 Ada can run 42 of the 70 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 FP32, Llama 3.2 1B Instruct at FP32.

Can the NVIDIA RTX 4000 Ada run Llama 3.3 70B Instruct?

The NVIDIA RTX 4000 Ada can run Llama 3.3 70B Instruct with CPU offload at NVFP4 quantization, but inference will be slower than native VRAM execution.

Can the NVIDIA RTX 4000 Ada run Qwen 3.6 27B?

Yes. The NVIDIA RTX 4000 Ada runs Qwen 3.6 27B natively in VRAM at NVFP4 quantization, achieving approximately 23.7 tokens per second.

Can the NVIDIA RTX 4000 Ada run Llama 3.1 8B Instruct?

Yes. The NVIDIA RTX 4000 Ada runs Llama 3.1 8B Instruct natively in VRAM at NVFP4 quantization, achieving approximately 80 tokens per second.

NVIDIA RTX 4000 Ada

The NVIDIA RTX 4000 Ada has 20 GB VRAM and 320 GB/s memory bandwidth. It can run 42 of our 70 tracked models natively in VRAM at 8k context.

The NVIDIA RTX 4000 Ada is the entry-level Ada Lovelace workstation GPU, built on a 160-bit memory bus with 20GB ECC GDDR6 at 320 GB/s. With 6,144 CUDA cores and a single-slot form factor, it is the most compact professional GPU in the Ada lineup. Its 20GB VRAM comfortably handles 13B models at Q8_0 and 14B–20B models at Q4_K_M, exceeding what typical consumer 16GB cards can hold, while fitting into thermally constrained workstations and SFF builds.

The NVIDIA RTX 4000 Ada is a professional workstation NVIDIA GPU based on the Ada Lovelace architecture. Released in 2023. It features 20 GB of GDDR6 at 320 GB/s memory bandwidth. Full llama.cpp and Ollama support out of the box. CUDA 12.x recommended; driver ≥ 525 required.

For local LLM inference, this GPU runs 42 of the 70 models we track natively in VRAM at 8K context. The largest model it handles in VRAM is Mixtral 8x7B Instruct v0.1 (82.9 t/s at Q2_K). It handles smaller models up to ~7-14B at reasonable precision, with some 27-32B models fitting at lower quantization. On Qwen 3.6 27B, it achieves approximately 23.7 tokens per second at NVFP4 quantization. An additional 9 models fit with CPU offload — slower but usable.

NVIDIA's CUDA ecosystem provides broad out-of-the-box support across llama.cpp, Ollama, vLLM, and TensorRT-LLM. Among workstation GPUs, it sits above Apple M5 Pro (24GB) and NVIDIA RTX 4060 Ti 16GB in performance, but below Intel Arc Pro B60 24GB.

Vendor	NVIDIA
Architecture	Ada Lovelace
VRAM	20 GB
Memory type	GDDR6
Memory bandwidth	320 GB/s
Compute backend	CUDA
Tier	Workstation
Released	2023
Models (native)	42 / 70
Models (offload)	9 / 70

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

Models this GPU runs natively in VRAM (42)

Models that fit with CPU offload (9)

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

Too large for this GPU (19)

Frequently asked questions

How much VRAM does the NVIDIA RTX 4000 Ada have?: The NVIDIA RTX 4000 Ada has 20 GB of GDDR6 with 320 GB/s memory bandwidth.
What is the NVIDIA RTX 4000 Ada best for?: With 20 GB of VRAM, the NVIDIA RTX 4000 Ada 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 4000 Ada run locally?: The NVIDIA RTX 4000 Ada can run 42 of the 70 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 FP32, Llama 3.2 1B Instruct at FP32.
Can the NVIDIA RTX 4000 Ada run Llama 3.3 70B Instruct?: The NVIDIA RTX 4000 Ada can run Llama 3.3 70B Instruct with CPU offload at NVFP4 quantization, but inference will be slower than native VRAM execution.
Can the NVIDIA RTX 4000 Ada run Qwen 3.6 27B?: Yes. The NVIDIA RTX 4000 Ada runs Qwen 3.6 27B natively in VRAM at NVFP4 quantization, achieving approximately 23.7 tokens per second.
Can the NVIDIA RTX 4000 Ada run Llama 3.1 8B Instruct?: Yes. The NVIDIA RTX 4000 Ada runs Llama 3.1 8B Instruct natively in VRAM at NVFP4 quantization, achieving approximately 80 tokens per second.