Best Local AI Models for NVIDIA RTX 5060 Ti 16 GB (16 GB VRAM)

Q: What is the VRAM and memory bandwidth of the NVIDIA RTX 5060 Ti 16 GB?

The NVIDIA RTX 5060 Ti 16 GB features 16 GB of VRAM and a memory bandwidth of 448 GB/s (GDDR7 128-bit). In local language model inference, VRAM determines which model sizes fit on the card, while memory bandwidth dictates how many tokens per second the GPU generates.

Q: What is the best local AI model to run on a NVIDIA RTX 5060 Ti 16 GB?

The best overall model for the NVIDIA RTX 5060 Ti 16 GB is Qwen/Qwen3-8B-GGUF using the recommended Q8_0 quantization (9.47 GB total memory). With 16 GB of VRAM, this GPU typically runs a 14B at high quality, or a 24–27B at 4-bit at full GPU speed. Check our guide to the best LLMs for 16 GB VRAM for details.

The NVIDIA RTX 5060 Ti 16 GB has 16 GB of VRAM. Here are the popular AI models it can run locally (4,096-token context, ~32.0 GB system RAM assumed), ranked by popularity.

Verdict: 16 GB of GDDR7 on a 128-bit bus: models up to about 14B–20B fit entirely in VRAM in 4-bit quantization, and 448 GB/s keeps token speed solid, though 256-bit cards are faster.

VRAM & Memory

16 GB

GDDR7 128-bit

Bandwidth

448 GB/s

Blackwell

Fits in VRAM

33 models

Zero offload

TDP / Assumed RAM

180W

RAM: 32.0 GB

At a glance — 33 of the 38 most popular models run fully in VRAM on the NVIDIA RTX 5060 Ti 16 GB (16 GB), with about 75 tokens/s estimated on an 8B model in Q4. A solid pick is Qwen/Qwen3-8B-GGUF (Q8_0, 9.47 GB, ~55.2 t/s estimated).

The NVIDIA RTX 5060 Ti 16 GB is built on NVIDIA's Blackwell architecture featuring GDDR7 128-bit delivering 448 GB/s of raw memory bandwidth. 16 GB of GDDR7 on a 128-bit bus: models up to about 14B–20B fit entirely in VRAM in 4-bit quantization, and 448 GB/s keeps token speed solid, though 256-bit cards are faster.

Equipped with 16 GB of dedicated VRAM, the NVIDIA RTX 5060 Ti 16 GB can run 33 popular open-source models completely in GPU memory without offloading. This includes full-speed execution for weights like Qwen3-8B-GGUF, Qwen3-4B-GGUF, Qwen2.5-7B-Instruct-GGUF. For a comprehensive breakdown of compatible model weights, see our guide to the best LLMs for 16 GB VRAM.

With a memory bandwidth of 448 GB/s, this card can generate tokens at an estimated peak rate of ~74.7 tokens/second on an 8B parameter model (Q4_K_M). Its rated power draw is 180W TDP, so ensure your system's power supply and case ventilation are adequate for sustained local inferencing.

New to this? Read: How much VRAM do you need?

How the NVIDIA RTX 5060 Ti 16 GB compares with similar cards

Compared with the NVIDIA RTX 4060 Ti 16 GB, the NVIDIA RTX 5060 Ti 16 GB has the same 16 GB of VRAM, so the same models fit fully on the GPU; about 56% faster token generation (~75 vs ~48 tokens/s on an 8B model in Q4, from 448 vs 288 GB/s); a higher board power (180 vs 165 W) and a newer architecture (Blackwell vs Ada Lovelace). Side-by-side comparison
Compared with the NVIDIA RTX 4070 Ti Super, the NVIDIA RTX 5060 Ti 16 GB has the same 16 GB of VRAM, so the same models fit fully on the GPU; about 33% slower token generation (~75 vs ~112 tokens/s on an 8B model in Q4, from 448 vs 672 GB/s); a lower board power (180 vs 285 W) and a newer architecture (Blackwell vs Ada Lovelace). Side-by-side comparison
Compared with the NVIDIA RTX 4080, the NVIDIA RTX 5060 Ti 16 GB has the same 16 GB of VRAM, so the same models fit fully on the GPU; about 38% slower token generation (~75 vs ~120 tokens/s on an 8B model in Q4, from 448 vs 717 GB/s); a lower board power (180 vs 320 W) and a newer architecture (Blackwell vs Ada Lovelace). Side-by-side comparison

Token speeds here are a conservative estimate (memory bandwidth ÷ 6, for an 8B model in Q4 that reads about 5 GB of weights per token, plus overhead); prompt processing depends on GPU compute instead.

33 fit fully in VRAM · 5 run with offload

Only show models that fit fully in VRAM

Model	Size	Quant.	Quality	Memory	Speed~	Verdict
Qwen/Qwen3-8B-GGUF	8.19B	Q8_0	Excellent	9.47 GB	55.2 t/s	Fits in VRAM
Qwen/Qwen3-4B-GGUF	4.02B	Q8_0	Excellent	5.35 GB	112.4 t/s	Fits in VRAM
Qwen/Qwen2.5-7B-Instruct-GGUF	7.62B	GGUF	Excellent	15.21 GB	31.6 t/s	Fits in VRAM
unsloth/NVIDIA-Nemotron-3.5-Bolt-E4B-GGUF	8.0B	BF16	Excellent	14.87 GB	32.0 t/s	Fits in VRAM
unsloth/gpt-oss-20b-GGUF	20.91B	F16	Very good	13.74 GB	34.9 t/s	Fits in VRAM
bartowski/Qwen2.5-32B-Instruct-GGUF	32.76B	IQ3_M	Fair	15.59 GB	32.5 t/s	Fits in VRAM
Qwen/Qwen2.5-1.5B-Instruct-GGUF	1.54B	GGUF	Excellent	4.23 GB	135.1 t/s	Fits in VRAM
unsloth/LFM2.5-24B-A2B-GGUF	23.97B	Q4_K_XL	Good	14.73 GB	32.3 t/s	Fits in VRAM
unsloth/Qwen3-Coder-30B-A3B-Instruct-GGUF	30.53B	Q3_K_M	Fair	14.88 GB	32.7 t/s	Fits in VRAM
Qwen/Qwen2.5-0.5B-Instruct-GGUF	0.49B	GGUF	Excellent	2.03 GB	379.8 t/s	Fits in VRAM
unsloth/Ornith-1.0-9B-GGUF	—	Q8_K_XL	Excellent	13.02 GB	37.0 t/s	Fits in VRAM
Qwen/Qwen3-14B-GGUF	14.77B	Q6_K	Excellent	12.71 GB	39.7 t/s	Fits in VRAM
Qwen/Qwen2.5-Coder-7B-Instruct-GGUF	7.62B	GGUF	Excellent	15.21 GB	31.6 t/s	Fits in VRAM
ggml-org/gpt-oss-120b-GGUF	116.83B	BF16	Very low	2.42 GB	303.2 t/s	Fits in VRAM
LiquidAI/LFM3-1.7B-GGUF	1.73B	BF16	Excellent	4.08 GB	138.8 t/s	Fits in VRAM
unsloth/flint-GGUF	39.79B	IQ3_XXS	Low	14.45 GB	33.1 t/s	Fits in VRAM
janhq/Jan-v3.5-4B-gguf	4.41B	GGUF	Excellent	9.59 GB	54.5 t/s	Fits in VRAM
Qwen/Qwen2.5-3B-Instruct-GGUF	3.09B	GGUF	Excellent	7.27 GB	70.7 t/s	Fits in VRAM
unsloth/GLM-4.7-Flash-GGUF	31.22B	Q3_K_M	Fair	14.62 GB	32.9 t/s	Fits in VRAM
bartowski/Meta-Llama-3.1-8B-Instruct-GGUF	8.03B	Q8_0	Excellent	9.23 GB	56.3 t/s	Fits in VRAM
unsloth/Llama-3.2-3B-Instruct-GGUF	3.21B	F16	Excellent	7.23 GB	74.8 t/s	Fits in VRAM
LiquidAI/LFM2.5-1.2B-Instruct-GGUF	1.17B	BF16	Excellent	3.03 GB	205.3 t/s	Fits in VRAM
lmstudio-community/gemma-3-1b-it-GGUF	1.0B	Q8_0	Excellent	1.96 GB	449.9 t/s	Fits in VRAM
bartowski/Kwaipilot_KAT-Coder-V2.5-Dev-GGUF	34.66B	Q3_K_M	Fair	15.99 GB	29.6 t/s	Fits in VRAM
Qwen/Qwen3-0.6B-GGUF	0.75B	Q8_0	Excellent	1.83 GB	752.3 t/s	Fits in VRAM
bartowski/Qwen_Qwen3-0.6B-GGUF	0.75B	BF16	Excellent	2.64 GB	318.7 t/s	Fits in VRAM
unsloth/Qwen3-1.7B-GGUF	1.72B	BF16	Excellent	4.45 GB	139.5 t/s	Fits in VRAM
LiquidAI/LFM2.5-350M-GGUF	0.35B	BF16	Excellent	1.51 GB	676.1 t/s	Fits in VRAM
Qwen/Qwen2.5-Coder-14B-Instruct-GGUF	14.77B	Q6_K	Excellent	12.84 GB	39.7 t/s	Fits in VRAM
unsloth/DeepSeek-R1-0528-Qwen3-8B-GGUF	8.19B	Q8_K_XL	Excellent	11.44 GB	44.4 t/s	Fits in VRAM
bartowski/Nanbeige_Nanbeige4.3-33B-GGUF	33.12B	Q3_K_S	Fair	15.78 GB	31.6 t/s	Fits in VRAM
Qwen/Qwen2.5-Coder-1.5B-Instruct-GGUF	1.54B	Q8_0	Excellent	2.67 GB	253.9 t/s	Fits in VRAM
bartowski/Qwen2.5-14B-Instruct-GGUF	14.77B	Q6_K_L	Excellent	13.19 GB	38.5 t/s	Fits in VRAM
unsloth/Ornith-1.5-122B-GGUF	125.09B	IQ3_XXS	Low	47.01 GB	1.1 t/s	Offload
ggml-org/NVIDIA-Nemotron-3.5-Lightning-30B-A3B-GGUF	31.58B	Q8_0	Excellent	32.1 GB	1.6 t/s	Offload
bartowski/Llama-3.3-70B-Instruct-GGUF	70.55B	Q5_K_S	Very good	47.53 GB	1.1 t/s	Offload
google/gemma-7b	8.54B	GGUF	Excellent	33.11 GB	1.6 t/s	Offload
unsloth/Qwen3-Coder-Next-GGUF	79.67B	Q4_1	Very good	47.79 GB	1.1 t/s	Offload

"Fits in VRAM" = fast, fully on GPU. "Offload" = part on system RAM, slower. Speed is a rough estimate from the NVIDIA RTX 5060 Ti 16 GB's 448 GB/s memory bandwidth (offloaded models are limited by system RAM).

Frequently asked questions

What is the VRAM and memory bandwidth of the NVIDIA RTX 5060 Ti 16 GB?

The NVIDIA RTX 5060 Ti 16 GB features 16 GB of VRAM and a memory bandwidth of 448 GB/s (GDDR7 128-bit). In local language model inference, VRAM determines which model sizes fit on the card, while memory bandwidth dictates how many tokens per second the GPU generates.

What is the best local AI model to run on a NVIDIA RTX 5060 Ti 16 GB?

The best overall model for the NVIDIA RTX 5060 Ti 16 GB is Qwen/Qwen3-8B-GGUF using the recommended Q8_0 quantization (9.47 GB total memory). With 16 GB of VRAM, this GPU typically runs a 14B at high quality, or a 24–27B at 4-bit at full GPU speed. Check our guide to the best LLMs for 16 GB VRAM for details.

Can the NVIDIA RTX 5060 Ti 16 GB run 14B and 32B models?

Yes. A 16 GB VRAM buffer allows you to run 14B models (like Qwen 2.5 14B or DeepSeek-R1 14B) in high-fidelity Q8_0 quantizations, or 32B models (like Qwen 2.5 32B) in Q3_K_M or Q4_K_M quantizations fully on the GPU.