What can NVIDIA GeForce RTX 5060 Ti 16GB run locally?

133 models fit comfortably on NVIDIA GeForce RTX 5060 Ti 16GB with 32 GB system RAM. 94 more run with tradeoffs. Top pick: Qwen 3 0.6B.

What's the recommended runner for this hardware?

llama.cpp / Ollama (CUDA). This is the runner that gives best tokens-per-second on NVIDIA GeForce RTX 5060 Ti 16GB based on windows support and the GPU backend.

What can NVIDIA GeForce RTX 5060 Ti 16GB run for chat?

Build: RTX 5060 Ti 16GB + Ryzen 5 9600X + 32GB DDR5

Memory: 16 GB VRAM + 32 GB system RAM

Runner: llama.cpp / Ollama (CUDA)

Any Chat Coding Agents Reasoning Vision Long context Creative

Runs comfortably
133 models

Ranked by fit for chat use case + predicted speed. Click a row for VRAM breakdown.

#1Qwen 3 0.6B

0.6B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 2.4 GBHeadroom: 13.6 GB

804

tok/s

Estimated

Weights

0.30 GB

KV cache

0.30 GB

Activations

0.02 GB

Runtime

1.80 GB

Model details →

#2Qwen 3 1.7B

1.7B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 3.6 GBHeadroom: 12.4 GB

284

tok/s

Estimated

Weights

0.90 GB

KV cache

0.85 GB

Activations

0.05 GB

Runtime

1.80 GB

Model details →

#3TinyLlama 1.1B Chat v1.0

1.1B

llama

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 2.6 GBHeadroom: 13.4 GB

438

tok/s

Estimated

Weights

0.60 GB

KV cache

0.14 GB

Activations

0.03 GB

Runtime

1.80 GB

Model details →

#4Gemma 2 2B Instruct

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 4.0 GBHeadroom: 12.0 GB

241

tok/s

Estimated

Weights

1.10 GB

KV cache

1.00 GB

Activations

0.06 GB

Runtime

1.80 GB

Model details →

#5Kumru 2B

2.4B

mistral

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 4.6 GBHeadroom: 11.4 GB

ollama run alibayram/kumru:latest

201

tok/s

Estimated

Weights

1.50 GB

KV cache

1.20 GB

Activations

0.08 GB

Runtime

1.80 GB

Model details →

#6TinyLlama 1.1B Chat v0.3 AWQ

1.1B

other

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 2.6 GBHeadroom: 13.4 GB

438

tok/s

Estimated

Weights

0.60 GB

KV cache

0.14 GB

Activations

0.03 GB

Runtime

1.80 GB

Model details →

#7TinyLlama 1.1B Chat v0.3 GPTQ

1.1B

other

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 2.6 GBHeadroom: 13.4 GB

438

tok/s

Estimated

Weights

0.60 GB

KV cache

0.14 GB

Activations

0.03 GB

Runtime

1.80 GB

Model details →

#8EXAONE 3.5 2.4B Instruct

2.4B

exaone

Quant: Q4_K_MContext: 8,192VRAM: 4.4 GBHeadroom: 11.6 GB

201

tok/s

Estimated

Weights

1.30 GB

KV cache

1.20 GB

Activations

0.07 GB

Runtime

1.80 GB

Model details →

#9Falcon 3 3B Instruct

falcon

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 5.1 GBHeadroom: 10.9 GB

161

tok/s

Estimated

Weights

1.70 GB

KV cache

1.50 GB

Activations

0.09 GB

Runtime

1.80 GB

Model details →

#10PhoGPT 4B Chat

3.7B

other

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 5.8 GBHeadroom: 10.2 GB

130

tok/s

Estimated

Weights

2.00 GB

KV cache

1.85 GB

Activations

0.11 GB

Runtime

1.80 GB

Model details →

#11Salamandra 2B Instruct

llama

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 4.0 GBHeadroom: 12.0 GB

241

tok/s

Estimated

Weights

1.10 GB

KV cache

1.00 GB

Activations

0.06 GB

Runtime

1.80 GB

Model details →

#12PhoGPT 4B

3.7B

other

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 5.8 GBHeadroom: 10.2 GB

130

tok/s

Estimated

Weights

2.00 GB

KV cache

1.85 GB

Activations

0.11 GB

Runtime

1.80 GB

Model details →

Runs with tradeoffs
94 models

Tight VRAM, partial CPU offload, or context-limited.

DeepSeek V2 Lite Chat

15.7B

deepseek

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 12.8 GBHeadroom: 3.2 GB

• Tight VRAM fit — only 3.2 GB headroom left for context growth

201

tok/s

Estimated

Weights

8.60 GB

KV cache

1.96 GB

Activations

0.43 GB

Runtime

1.80 GB

Model details →

Gemma 2 9B Instruct

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 12.4 GBHeadroom: 3.6 GB

• Tight VRAM fit — only 3.6 GB headroom left for context growth

ollama run gemma2:9b

tok/s

Estimated

Weights

5.80 GB

KV cache

4.50 GB

Activations

0.30 GB

Runtime

1.80 GB

Model details →

Bielik 11B v3.0 Instruct GGUF

11B

llama

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 13.7 GBHeadroom: 2.3 GB

• Tight VRAM fit — only 2.3 GB headroom left for context growth

tok/s

Estimated

Weights

6.10 GB

KV cache

5.50 GB

Activations

0.31 GB

Runtime

1.80 GB

Model details →

Mistral Nemo 12B Instruct

12B

mistral

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 15.7 GBHeadroom: 0.3 GB

• Tight VRAM fit — only 0.3 GB headroom left for context growth

ollama run mistral-nemo:12b

tok/s

Estimated

Weights

7.50 GB

KV cache

6.00 GB

Activations

0.38 GB

Runtime

1.80 GB

Model details →

Gemma 3 12B

12B

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 15.5 GBHeadroom: 0.5 GB

• Tight VRAM fit — only 0.5 GB headroom left for context growth

ollama run gemma3:12b

tok/s

Estimated

Weights

7.30 GB

KV cache

6.00 GB

Activations

0.37 GB

Runtime

1.80 GB

Model details →

Stable LM 2 12B

12B

other

Quant: Q4_K_MContext: 4,096VRAM: 12.4 GBHeadroom: 3.6 GB

• Tight VRAM fit — only 3.6 GB headroom left for context growth

tok/s

Estimated

Weights

7.20 GB

KV cache

3.00 GB

Activations

0.36 GB

Runtime

1.80 GB

Model details →

OpenThaiGPT 1.0.0 Beta 13B Chat

13B

llama

Commercial OK

Quant: Q4_K_MContext: 4,096VRAM: 12.6 GBHeadroom: 3.4 GB

• Tight VRAM fit — only 3.4 GB headroom left for context growth

tok/s

Estimated

Weights

7.20 GB

KV cache

3.25 GB

Activations

0.36 GB

Runtime

1.80 GB

Model details →

Qwen 3 14B

14B

qwen

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 12.4 GBHeadroom: 3.6 GB

• Tight VRAM fit — only 3.6 GB headroom left for context growth

ollama run qwen3:14b

tok/s

Estimated

Weights

8.40 GB

KV cache

1.75 GB

Activations

0.42 GB

Runtime

1.80 GB

Model details →

What if you upgraded?

Hypothetical scenarios. We re-ran the compatibility engine for each.

+32 GB system RAM

~$80–150

Doubles your CPU-offload working set. Helps when models don't quite fit in VRAM.

Unlocks: 9 new comfortable, 112 new tradeoff

• Gemma 3 270M
• SmolLM2 135M Instruct
• SmolLM2 360M Instruct
• VBART Large (Turkish Summarization)

Shop this upgrade↗

Upgrade to NVIDIA RTX 2080 Ti 22GB (China-mod)

~$350

22 GB VRAM (vs your 16 GB) plus a bandwidth jump from ~448 GB/s to ~616 GB/s.

Unlocks: 36 new comfortable

• Gemma 4 12B
• Qwen3.5 9B
• Qwen 3 14B
• Gemma 3 270M

Shop this upgrade↗

Add a second NVIDIA GeForce RTX 5060 Ti 16GB

~$459

Tensor parallelism splits the model across both cards, effectively doubling VRAM. Bandwidth doesn't double — runs ~1.5× the single-card speed in practice.

Unlocks: 83 new comfortable

• Qwen 3 30B-A3B
• Qwen 2.5 Coder 32B Instruct
• Qwen3.6 27B
• Qwen 3 32B

Shop this upgrade↗

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Won't run
top 5 popular models

Need more memory than you have. Shown for orientation.

DeepSeek V4 Pro (1.6T MoE)

1600B

deepseek

Commercial OK