How much VRAM does the NVIDIA RTX 5000 Ada have?

The NVIDIA RTX 5000 Ada has 32 GB of GDDR6 with 576 GB/s memory bandwidth.

What is the NVIDIA RTX 5000 Ada best for?

With 32 GB of VRAM, the NVIDIA RTX 5000 Ada is well-suited for running 7B–32B models at Q4 with room for context, making it a great all-rounder for local LLM inference.

What LLMs can the NVIDIA RTX 5000 Ada run locally?

The NVIDIA RTX 5000 Ada can run 47 of the 71 open-weight models tracked by CanItRun natively in VRAM at 8k context. Top options include: Llama 3.3 70B Instruct at Q2_K, Llama 3.1 8B Instruct at BF16, Llama 3.2 3B Instruct at FP32.

Can the NVIDIA RTX 5000 Ada run Llama 3.3 70B Instruct?

Yes. The NVIDIA RTX 5000 Ada runs Llama 3.3 70B Instruct natively in VRAM at Q2_K quantization, achieving approximately 25 tokens per second.

Can the NVIDIA RTX 5000 Ada run Qwen 3.6 27B?

Yes. The NVIDIA RTX 5000 Ada runs Qwen 3.6 27B natively in VRAM at NVFP4 quantization, achieving approximately 42.7 tokens per second.

Can the NVIDIA RTX 5000 Ada run Llama 3.1 8B Instruct?

Yes. The NVIDIA RTX 5000 Ada runs Llama 3.1 8B Instruct natively in VRAM at BF16 quantization, achieving approximately 36 tokens per second.

NVIDIA RTX 5000 Ada

The NVIDIA RTX 5000 Ada has 32 GB VRAM and 576 GB/s memory bandwidth. It can run 47 of our 71 tracked models natively in VRAM at 8k context.

With 32 GB GDDR6, the NVIDIA RTX 5000 Ada is a workstation-tier GPU that can run 47 models natively. It handles 70B-class models at Q4 quantization.

The NVIDIA RTX 5000 Ada fills the gap between the RTX 4500 Ada and RTX 6000 Ada, featuring 32GB ECC GDDR6 on a 256-bit bus at 576 GB/s with 12,800 CUDA cores. This VRAM headroom enables 34B models at Q4_K_M and most 27B models at Q8_0 to run entirely in memory. Professional ECC memory and certified drivers make it a reliable choice for on-prem AI inference deployments.

The NVIDIA RTX 5000 Ada is a professional workstation NVIDIA GPU based on the Ada Lovelace architecture. Released in 2023. It features 32 GB of GDDR6 at 576 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 47 of the 71 models we track natively in VRAM at 8K context. The largest model it handles in VRAM is Qwen 2.5 72B Instruct (24.3 t/s at Q2_K). It comfortably runs models up to ~27-32B parameters at Q4. Larger models need CPU offload or multi-GPU. On Llama 3.3 70B Instruct, it achieves approximately 25 tokens per second at Q2_K quantization. An additional 7 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 AMD Radeon RX 7900 GRE and Apple M2 Max (32GB) in performance, but below AMD Radeon AI Pro 9700 32GB.

Vendor	NVIDIA
Architecture	Ada Lovelace
VRAM	32 GB
Memory type	GDDR6
Memory bandwidth	576 GB/s
Compute backend	CUDA
Tier	Workstation
Released	2023
Models (native)	47 / 71
Models (offload)	7 / 71

Popular models for this GPU

Models this GPU runs natively in VRAM (47)

Models that fit with CPU offload (7)

Too large for this GPU (17)

Models mentioned

Frequently asked questions