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

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

Memory: 16 GB VRAM + 32 GB system RAM
Runner: llama.cpp / Ollama (CUDA)
AnyChatCodingAgentsReasoningVisionLong contextCreative

Runs comfortably
75 models

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

#1Hermes 3 Llama 3.1 8B
8B
hermes
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 11.0 GBHeadroom: 5.0 GB
ollama run hermes3:8b
54
tok/s
Estimated
Weights
4.90 GB
KV cache
4.00 GB
Activations
0.25 GB
Runtime
1.80 GB
Model details →
#2Dolphin 3.0 Llama 3.2 3B
3B
dolphin
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 5.2 GBHeadroom: 10.8 GB
144
tok/s
Estimated
Weights
1.80 GB
KV cache
1.50 GB
Activations
0.10 GB
Runtime
1.80 GB
Model details →
#3Hermes 3 Llama 3.2 3B
3B
hermes
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 5.2 GBHeadroom: 10.8 GB
144
tok/s
Estimated
Weights
1.80 GB
KV cache
1.50 GB
Activations
0.10 GB
Runtime
1.80 GB
Model details →
#4Gemma 2 2B Instruct
2B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 4.0 GBHeadroom: 12.0 GB
215
tok/s
Estimated
Weights
1.10 GB
KV cache
1.00 GB
Activations
0.06 GB
Runtime
1.80 GB
Model details →
#5Gemma 4 E2B (Effective 2B)
2B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 5.1 GBHeadroom: 10.9 GB
ollama run gemma4:e2b
122
tok/s
Estimated
Weights
2.20 GB
KV cache
1.00 GB
Activations
0.12 GB
Runtime
1.80 GB
Model details →
#6Qwen 3 1.7B
1.7B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 3.6 GBHeadroom: 12.4 GB
253
tok/s
Estimated
Weights
0.90 GB
KV cache
0.85 GB
Activations
0.05 GB
Runtime
1.80 GB
Model details →
#7Kumru 2B
2.4B
mistral
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 4.6 GBHeadroom: 11.4 GB
ollama run alibayram/kumru:latest
179
tok/s
Estimated
Weights
1.50 GB
KV cache
1.20 GB
Activations
0.08 GB
Runtime
1.80 GB
Model details →
#8Qwen 3.5 2B Turkish SFT
2B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 4.0 GBHeadroom: 12.0 GB
215
tok/s
Estimated
Weights
1.10 GB
KV cache
1.00 GB
Activations
0.06 GB
Runtime
1.80 GB
Model details →
#9Kanarya 2B
2B
other
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 3.2 GBHeadroom: 12.8 GB
215
tok/s
Estimated
Weights
1.10 GB
KV cache
0.25 GB
Activations
0.06 GB
Runtime
1.80 GB
Model details →
#10Granite 3.1 2B Instruct
2B
granite
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 4.0 GBHeadroom: 12.0 GB
215
tok/s
Estimated
Weights
1.10 GB
KV cache
1.00 GB
Activations
0.06 GB
Runtime
1.80 GB
Model details →
#11Falcon 3 3B Instruct
3B
falcon
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 5.1 GBHeadroom: 10.9 GB
144
tok/s
Estimated
Weights
1.70 GB
KV cache
1.50 GB
Activations
0.09 GB
Runtime
1.80 GB
Model details →
#12OpenELM 3B Instruct
3B
other
Quant: Q4_K_MContext: 2,048VRAM: 4.0 GBHeadroom: 12.0 GB
144
tok/s
Estimated
Weights
1.70 GB
KV cache
0.38 GB
Activations
0.09 GB
Runtime
1.80 GB
Model details →

Runs with tradeoffs
46 models

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

Gemma 2 9B Instruct
9B
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
48
tok/s
Estimated
Weights
5.80 GB
KV cache
4.50 GB
Activations
0.30 GB
Runtime
1.80 GB
Model details →
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
179
tok/s
Estimated
Weights
8.60 GB
KV cache
1.96 GB
Activations
0.43 GB
Runtime
1.80 GB
Model details →
DeepSeek MoE 16B Base
16B
deepseek
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 15.8 GBHeadroom: 0.2 GB
  • • Tight VRAM fit — only 0.2 GB headroom left for context growth
179
tok/s
Estimated
Weights
9.50 GB
KV cache
4.00 GB
Activations
0.48 GB
Runtime
1.80 GB
Model details →
YTU Turkish Gemma 9B v0.1
9.2B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 12.5 GBHeadroom: 3.5 GB
  • • Tight VRAM fit — only 3.5 GB headroom left for context growth
ollama run alibayram/turkish-gemma-9b-v0.1:latest
47
tok/s
Estimated
Weights
5.80 GB
KV cache
4.60 GB
Activations
0.30 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
36
tok/s
Estimated
Weights
7.30 GB
KV cache
6.00 GB
Activations
0.37 GB
Runtime
1.80 GB
Model details →
GLM-4 9B
9B
glm
Quant: Q4_K_MContext: 8,192VRAM: 12.1 GBHeadroom: 3.9 GB
  • • Tight VRAM fit — only 3.9 GB headroom left for context growth
48
tok/s
Estimated
Weights
5.50 GB
KV cache
4.50 GB
Activations
0.28 GB
Runtime
1.80 GB
Model details →
Falcon 3 10B
10B
falcon
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 13.1 GBHeadroom: 2.9 GB
  • • Tight VRAM fit — only 2.9 GB headroom left for context growth
43
tok/s
Estimated
Weights
6.00 GB
KV cache
5.00 GB
Activations
0.31 GB
Runtime
1.80 GB
Model details →
Llama 3.2 11B Vision Instruct
11B
llama
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 15.6 GBHeadroom: 0.4 GB
  • • Tight VRAM fit — only 0.4 GB headroom left for context growth
ollama run llama3.2-vision:11b
39
tok/s
Estimated
Weights
7.90 GB
KV cache
5.50 GB
Activations
0.40 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

Check the current price

Adds 32 GB to your CPU-offload working set. Helps when models don't quite fit in VRAM.

Unlocks: 13 new comfortable, 57 new tradeoff

  • • Qwen 3 0.6B
  • • Gemma 3 270M
  • • SmolLM2 135M Instruct
  • • TinyLlama 1.1B Chat v1.0
Shop this upgrade↗

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

Check the current price

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

Unlocks: 32 new comfortable

  • • Qwen 3 0.6B
  • • Qwen 3 14B
  • • Gemma 3 270M
  • • Phi-4 14B

Add a second NVIDIA GeForce RTX 5060 Ti 16GB

Launch MSRP $429 (2025). Check the current price.

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: 53 new comfortable

  • • Qwen 3 0.6B
  • • Qwen 3 30B-A3B
  • • Qwen 2.5 Coder 32B Instruct
  • • 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

Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.

—
Qwen 3 235B-A22B
235B
qwen
Commercial OK

Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.

—
DeepSeek R1 (671B reasoning)
671B
deepseek
Commercial OK

Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.

—
Llama 4 Scout
109B
llama
Commercial OK

Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.

—
DeepSeek V4 Flash (284B MoE)
284B
deepseek
Commercial OK

Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.

—
Cloud GPU rental

Not enough VRAM for these models? Rent a GPU by the hour

The models above need more memory than 16 GB VRAM + 32 GB system RAM. You can rent a GPU with enough VRAM by the hour to run one of them, or to test it before you buy hardware. For daily use, owning a card usually costs less over time.

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billed per GPU-hour

Hourly GPU pods, in community-cloud and secure-cloud tiers.

The two tiers run on different hosts and terms. Check which tier a pod is on before a long job.

Rent on RunPod

Vast.ai

billed per GPU-hour

Marketplace of host-listed GPUs; price and reliability vary by host.

Host quality varies. Filter for verified hosts and check the host's reliability before a long run.

Rent on Vast.ai

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How to read these numbers

Measured here
Measured here - RunLocalAI ran this exact combo on owner hardware with public evidence.

Source-backed
Source-backed / community - a reproduced public source supports the speed, but it is not labeled as owner-measured.

Extrapolated
Extrapolated - predicted from a measured benchmark on similar-bandwidth hardware.

Estimated
Estimated - formula based on VRAM bandwidth and model architecture; not a benchmark row.

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