GeForce RTX 5090 D

The GeForce RTX 5090 D is an NVIDIA GPU built on the Blackwell 2.0 architecture, released 2025-01-30. For running AI locally, the numbers that matter are its 32 GB of GDDR7 and 1790 GB/s of memory bandwidth. VRAM decides which models fit at all; bandwidth sets how fast they generate text.

What you can run on 32 GB

At Q4_K_M quantization (the usual local default), 32 GB holds models up to roughly 50B parameters, leaving headroom for context. On this card you can run, among others:

• Llama-3_3-Nemotron-Super-49B-v1_5 - 49.9B parameters
• Qwen3.5 35B A3B - 36B parameters
• Qwen3.6 35B A3B - 36B parameters
• dolphin-2.9.1-yi-1.5-34b - 34.4B parameters
• EXAONE 4.5 33B - 34.4B parameters

Larger models need a higher-VRAM card, a second GPU, or CPU offload (which is much slower). Check any specific model with the VRAM calculator, or see the full picture on what can I run.

Local LLM speed (LLaMA 3, llama.cpp)

Single-stream token-generation throughput - estimated from memory bandwidth:

Model (quant)	Speed on GeForce RTX 5090 D
Llama 3 8B (Q4_K_M)	209.5 tok/s
Llama 3 8B (F16)	95.1 tok/s
Llama 3 70B (Q4_K_M)	✗ won't fit

Because decode is memory-bandwidth bound, the 1790 GB/s figure is the best single predictor of chat speed on this card. Estimates are calibrated against measured RTX-40-series cards and are typically within ~15%.

Memory and power

• VRAM: 32 GB GDDR7 (512-bit bus)
• Bandwidth: 1790 GB/s
• TDP: 575 W - a 950 W+ power supply is recommended
• Process: 5 nm
• Interface: PCIe 5.0 x16

Quantization and context

Quantization trades a little quality for a lot of VRAM. On 32 GB you can fit roughly a 50B model at Q4_K_M, about a 27B model at the higher-quality Q8, or a smaller model at full FP16. Longer context windows also consume VRAM (the KV cache grows with context length), so leave a few GB of headroom if you plan to use large prompts or many concurrent requests. For most chat and coding use, Q4_K_M on this card is the sweet spot between speed, quality, and the 32 GB budget.

How it compares

Similar cards for local AI, by VRAM and 8B-Q4 speed:

GPU	VRAM	Bandwidth	Llama 3 8B Q4
GeForce RTX 5090 D	32 GB	1790 GB/s	209.5 tok/s
NVIDIA RTX 5000 Ada Generation	32 GB	768 GB/s	89.9 tok/s
NVIDIA RTX 5090	32 GB	1792 GB/s	209.7 tok/s
RTX PRO 4500 Blackwell	32 GB	896 GB/s	104.9 tok/s

Bottom line

The GeForce RTX 5090 D is best for llm-inference, image-gen, comfyui. With 24 GB+ it comfortably handles most open models, including 30B-class at Q4. If you need more, compare with NVIDIA RTX 5000 Ada Generation and NVIDIA RTX 5090.

Sources

• Specifications: RightNow GPU Database (TechPowerUp data)
• Benchmarks: GPU-Benchmarks-on-LLM-Inference (basis for the bandwidth estimate)

Specs and benchmarks last checked 2026-06-08. Verify current pricing before buying.