What AI models can the NVIDIA GeForce GTX 1660 Ti run?

VRAM: 6.0 GB
Usable for models: 6.0 GB
Memory bandwidth: 288 GB/s
FP16 compute: 5.4 TFLOPS
Released: 2019
Launch price: $279

434 / 622 LLMs run Best: Nemotron Labs Dory 1.0 4B 43 / 61 Image models run Best: FLUX.2 [klein] 4B 3 / 43 Video models run Best: AnimateDiff v3 (SD 1.5) 63 / 70 Decision models run Best: Jina Reranker v3.5

More about this hardware

Maker	NVIDIA
Type	Graphics card
Interface	PCIe 3.0

Apps that run models well on the NVIDIA GeForce GTX 1660 Ti

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Linux

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

Best LLMs for the NVIDIA GeForce GTX 1660 Ti

Quality	Model	Quant	Memory	Speed	Runs
67Good quality (B) · 67 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 85/100 (quality, fit, speed and memory use) sets the ranking.	Nemotron Labs Dory 1.0 4BNVIDIA	`Q6_K`	4.6 GB	61.6 tok/s	Runs great
65Good quality (B) · 65 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking.	Spark-X2.6 4BSparkLLM	`Q6_K`	4.6 GB	59.7 tok/s	Runs great
67Good quality (B) · 67 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 84/100 (quality, fit, speed and memory use) sets the ranking.	Nanbeige4.2 3BBOSS Zhipin (Nanbeige)	`Q6_K`	5.8 GB	58.8 tok/s	Runs welltight fit
66Good quality (B) · 66 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking.	Nemotron 3.5 Bolt E4BNVIDIA	`Q4_K_M`	5.9 GB	78.6 tok/s	Runs welltight fit
65Good quality (B) · 65 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking.	MiMo V2.6 Distill Qwen 9BXiaomi	`Q3_K_M`	5.8 GB	48.6 tok/s	Runs welltight fit
64Good quality (B) · 64 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking.	Ornith 1.5 9BDeepReinforce	`Q3_K_M`	5.9 GB	47.4 tok/s	Runs welltight fit
64Good quality (B) · 64 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 83/100 (quality, fit, speed and memory use) sets the ranking.	Gemma 4 E4BGoogle	`Q4_K_M`	5.9 GB	78.6 tok/s	Runs welltight fit
63Good quality (B) · 63 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking.	Qwen3.5 9BAlibaba	`Q3_K_M`	5.9 GB	47.4 tok/s	Runs welltight fit
61Good quality (B) · 61 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking.	Qwen3.5 4BAlibaba	`Q6_K`	5.0 GB	52.6 tok/s	Runs great
63Good quality (B) · 63 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking.	Ornith 1.0 9BDeepReinforce	`Q3_K_M`	5.9 GB	47.4 tok/s	Runs welltight fit
60Good quality (B) · 60 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking.	TMax 4BAi2	`Q6_K`	4.6 GB	58.3 tok/s	Runs great
60Good quality (B) · 60 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking.	Gemma 4 E2BGoogle	`Q5_K_M`	4.6 GB	127 tok/s	Runs great

Best Image models for the NVIDIA GeForce GTX 1660 Ti

Quality	Model	Quant	Memory	Speed	Runs
72Great quality (A) · 72 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 82/100 (quality, fit, speed and memory use) sets the ranking.	FLUX.2 [klein] 4BBlack Forest Labs	`Q4 GGUF`	4.6 GB	9.7 s	Runs greatencoder in RAM
56Good quality (B) · 56 of 100: how capable the model is at FP8, whatever it runs on. Match score 78/100 (quality, fit, speed and memory use) sets the ranking.	DreamShaper XL v2 TurboLykon	`FP8`	5.6 GB	9.0 s	Runs welltight fit
84Great quality (A) · 84 of 100: how capable the model is at Q4 GGUF, whatever it runs on. Match score 77/100 (quality, fit, speed and memory use) sets the ranking.	FLUX 3 Image KleinBlack Forest Labs	`Q4 GGUF`	5.7 GB	56 s	Slowtight fit · encoder in RAM
52Fair quality (C) · 52 of 100: how capable the model is at FP8, whatever it runs on. Match score 76/100 (quality, fit, speed and memory use) sets the ranking.	SDXL-Lightning (4-step)ByteDance	`FP8`	5.6 GB	4.9 s	Runs welltight fit
82Great quality (A) · 82 of 100: how capable the model is at FP8, whatever it runs on. Match score 74/100 (quality, fit, speed and memory use) sets the ranking.	Z-Image TurboAlibaba (Tongyi)	`FP8`	4.4 GB	23 s	Runs wellCPU offload
60Good quality (B) · 60 of 100: how capable the model is at FP8, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking.	Juggernaut XL v9RunDiffusion	`FP8`	5.6 GB	31 s	Runs welltight fit

Best Video models for the NVIDIA GeForce GTX 1660 Ti

Quality	Model	Quant	Memory	Speed	Runs
35Basic quality (D) · 35 of 100: how capable the model is at FP16, whatever it runs on. Match score 70/100 (quality, fit, speed and memory use) sets the ranking.	AnimateDiff v3 (SD 1.5)Shanghai AI Lab	`FP16`	4.7 GB	2.6 min	Runs great
49Fair quality (C) · 49 of 100: how capable the model is at FP8, whatever it runs on. Match score 62/100 (quality, fit, speed and memory use) sets the ranking.	LTX-Video 2BLightricks	`FP8`	4.0 GB	3.8 min	Runs greatCPU offload
44Fair quality (C) · 44 of 100: how capable the model is at FP8, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking.	CogVideoX 2BZhipu AI	`FP8`	4.7 GB	9.0 min	Runs wellCPU offload

Best Decision models for the NVIDIA GeForce GTX 1660 Ti

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`	2.3 GB	90 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`	2.3 GB	90 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	71 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	44 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`	2.3 GB	90 ms	Runs great
76Great quality (A) · 76 of 100: how capable the model is at F16, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking.	Llama Prompt Guard 2 86MMeta	`F16`	1.2 GB	50 ms	Runs great
76Great quality (A) · 76 of 100: how capable the model is at F16, whatever it runs on. Match score 89/100 (quality, fit, speed and memory use) sets the ranking.	mxbai-rerank-base v2Mixedbread	`F16`	1.9 GB	74 ms	Runs great
74Great quality (A) · 74 of 100: how capable the model is at F16, whatever it runs on. Match score 88/100 (quality, fit, speed and memory use) sets the ranking.	GLiClass ModernBERT Large v3Knowledgator	`F16`	1.5 GB	37 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.