What AI models can the NVIDIA Quadro RTX 5000 run?

VRAM: 16.0 GB
Usable for models: 16.0 GB
Memory bandwidth: 448 GB/s
FP16 compute: 11.2 TFLOPS
Released: 2018
Launch price: $2,300

491 / 622 LLMs run Best: Gemma 4 12B 55 / 61 Image models run Best: Z-Image Turbo 17 / 43 Video models run Best: LTX-Video 2B 68 / 70 Decision models run Best: Jina Reranker v3.5

More about this hardware

Maker	NVIDIA
Type	Workstation / datacenter graphics card
Interface	PCIe 3.0

Apps that run models well on the NVIDIA Quadro RTX 5000

Windows

Linux

For a typical model that fits in memory. Each model page picks the best app for that model on this hardware. All apps

1 product with the NVIDIA Quadro RTX 5000

ThinkStation P620 Tower WorkstationLenovo · Workstation · 4, 8, 12, 16, 20, 24 or 48 GBProduct page (opens in a new tab)

Best LLMs for the NVIDIA Quadro RTX 5000

Quality	Model	Quant	Memory	Speed	Runs
80Great quality (A) · 80 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking.	Gemma 4 12BGoogle	`Q5_K_M`	10.6 GB	38.0 tok/s	Runs great
79Great quality (A) · 79 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking.	K2 Horizon 7BInstitute of Foundation Models	`Q6_K`	10.0 GB	42.4 tok/s	Runs great
77Great quality (A) · 77 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking.	Gemma 4 26B-A4BGoogle	`Q3_K_M`	14.4 GB	116 tok/s	Runs welltight fit
77Great quality (A) · 77 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking.	SenseNova Loop 9B PreviewSenseTime	`Q6_K`	9.8 GB	39.5 tok/s	Runs great
77Great quality (A) · 77 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking.	MiMo V2.6 Distill Qwen 9BXiaomi	`Q6_K`	9.4 GB	40.5 tok/s	Runs great
76Great quality (A) · 76 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking.	Ornith 1.5 9BDeepReinforce	`Q6_K`	9.6 GB	39.5 tok/s	Runs great
75Great quality (A) · 75 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking.	Qwen3.5 9BAlibaba	`Q6_K`	9.6 GB	39.5 tok/s	Runs great
73Great quality (A) · 73 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking.	Raon-1 mini Preview (30B-A4B)KRAFTON	`Q3_K_M`	15.6 GB	108 tok/s	Runs welltight fit
74Great quality (A) · 74 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking.	Ornith 1.0 9BDeepReinforce	`Q6_K`	9.6 GB	39.5 tok/s	Runs great
80Great quality (A) · 80 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking.	Qwen3.8 27BAlibaba	`Q3_K_M`	15.5 GB	25.6 tok/s	Runs welltight fit
78Great quality (A) · 78 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	Qwen3.6 27BAlibaba	`Q3_K_M`	15.5 GB	25.6 tok/s	Runs welltight fit
70Great quality (A) · 70 of 100: how capable the model is at MXFP4, whatever it runs on. Match score 86/100 (quality, fit, speed and memory use) sets the ranking.	gpt-oss 20BOpenAI	`MXFP4`	13.5 GB	105 tok/s	Runs great

Best Image models for the NVIDIA Quadro RTX 5000

Quality	Model	Quant	Memory	Speed	Runs
82Great quality (A) · 82 of 100: how capable the model is at FP8, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking.	Z-Image TurboAlibaba (Tongyi)	`FP8`	12.6 GB	10 s	Runs great
89Excellent quality (A+) · 89 of 100: how capable the model is at FP8, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking.	FLUX 3 Image KleinBlack Forest Labs	`FP8`	12.4 GB	23 s	Runs well
77Great quality (A) · 77 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking.	FLUX.2 [klein] 4BBlack Forest Labs	`Q8 GGUF`	10.5 GB	5.1 s	Runs great
83Great quality (A) · 83 of 100: how capable the model is at FP8, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking.	ERNIE-Image TurboBaidu	`FP8`	14.5 GB	13 s	Runs welltight fit
83Great quality (A) · 83 of 100: how capable the model is at Q8 GGUF, whatever it runs on. Match score 87/100 (quality, fit, speed and memory use) sets the ranking.	FLUX.2 [klein] 9BBlack Forest Labs	`Q8 GGUF`	12.3 GB	9.7 s	Runs greatencoder in RAM
65Good quality (B) · 65 of 100: how capable the model is at NF4, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking.	FLUX.1 [schnell]Black Forest Labs	`NF4`	14.4 GB	9.8 s	Runs welltight fit

Best Video models for the NVIDIA Quadro RTX 5000

Quality	Model	Quant	Memory	Speed	Runs
49Fair quality (C) · 49 of 100: how capable the model is at FP8, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking.	LTX-Video 2BLightricks	`FP8`	10.8 GB	1.7 min	Runs great
44Fair quality (C) · 44 of 100: how capable the model is at FP8, whatever it runs on. Match score 72/100 (quality, fit, speed and memory use) sets the ranking.	CogVideoX 2BZhipu AI	`FP8`	11.0 GB	4.0 min	Runs well
53Fair quality (C) · 53 of 100: how capable the model is at FP8, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking.	Wan 2.1 T2V 1.3BAlibaba	`FP8`	12.8 GB	16.1 min	Slow
53Fair quality (C) · 53 of 100: how capable the model is at FP8, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking.	CogVideoX 5BZhipu AI	`FP8`	14.3 GB	12.1 min	Slowtight fit
53Fair quality (C) · 53 of 100: how capable the model is at FP8, whatever it runs on. Match score 69/100 (quality, fit, speed and memory use) sets the ranking.	CogVideoX 5B I2VZhipu AI	`FP8`	14.9 GB	12.1 min	Slowtight fit

Best Decision models for the NVIDIA Quadro RTX 5000

Quality	Model	Quant	Memory	Speed	Runs
86Excellent quality (A+) · 86 of 100: how capable the model is at F16, whatever it runs on. Match score 93/100 (quality, fit, speed and memory use) sets the ranking.	Jina Reranker v3.5Jina AI	`F16`	2.8 GB	47 ms	Runs great
84Great quality (A) · 84 of 100: how capable the model is at F16, whatever it runs on. Match score 92/100 (quality, fit, speed and memory use) sets the ranking.	Jina Reranker v3Jina AI	`F16`	2.8 GB	47 ms	Runs great
82Great quality (A) · 82 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking.	mxbai-rerank-large v2Mixedbread	`Q8_0`	3.1 GB	107 ms	Runs great
80Great quality (A) · 80 of 100: how capable the model is at F16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking.	FRIDA-DecisionsSber AI	`F16`	2.4 GB	37 ms	Runs great
80Great quality (A) · 80 of 100: how capable the model is at F16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking.	GLiNER2.5 DecideFastino	`F16`	1.7 GB	23 ms	Runs great
80Great quality (A) · 80 of 100: how capable the model is at F16, whatever it runs on. Match score 91/100 (quality, fit, speed and memory use) sets the ranking.	Qwen3 Reranker 0.6BAlibaba	`F16`	2.8 GB	47 ms	Runs great
82Great quality (A) · 82 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 90/100 (quality, fit, speed and memory use) sets the ranking.	d1 3BLiquid AI	`Q8_0`	4.7 GB	114 ms	Runs great
80Great quality (A) · 80 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking.	zerank-1 smallZeroEntropy	`Q8_0`	3.5 GB	119 ms	Runs great

Best LLMs by task on this hardware Open in Model Finder

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.