RUNLOCALAIv38
→WILL IT RUNBEST GPUCOMPARETROUBLESHOOTSTARTPULSEMODELSHARDWARETOOLSBENCH
RUNLOCALAI

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SYS · ONLINEUPTIME · 100%2026 · operator-owned
RUNLOCALAI · v38
Will it run? / NVIDIA GeForce RTX 4070 Ti Super / creative

What can NVIDIA GeForce RTX 4070 Ti Super run for creative?

Build: NVIDIA GeForce RTX 4070 Ti Super + — + 32 GB RAM (windows)

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

Runs comfortably
46 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: 2,048VRAM: 9.9 GBHeadroom: 6.1 GB
ollama run hermes3:8b
94
tok/s
E
Weights
4.83 GB
KV cache
1.00 GB
Activations
2.29 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#2Gemma 2 9B Instruct
9B
gemma
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 10.7 GBHeadroom: 5.3 GB
ollama run gemma2:9b
84
tok/s
E
Weights
5.43 GB
KV cache
1.13 GB
Activations
2.32 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#3CodeGemma 7B
7B
gemma
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GB
ollama run codegemma:7b
108
tok/s
E
Weights
4.23 GB
KV cache
0.88 GB
Activations
2.26 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#4Moondream 2
1.9B
other
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 5.3 GBHeadroom: 10.7 GB
397
tok/s
E
Weights
1.15 GB
KV cache
0.24 GB
Activations
2.11 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#5Whisper Large v3
1.55B
other
Commercial OK
Quant: FP16Context: 0VRAM: 5.1 GBHeadroom: 10.9 GB
147
tok/s
E
Weights
3.10 GB
KV cache
0.00 GB
Activations
0.16 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#6SmolLM 2 1.7B Instruct
1.7B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 11.9 GBHeadroom: 4.1 GB
443
tok/s
E
Weights
1.03 GB
KV cache
0.85 GB
Activations
8.24 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#7Nemotron Mini 4B Instruct
4B
other
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 9.4 GBHeadroom: 6.6 GB
188
tok/s
E
Weights
2.42 GB
KV cache
1.00 GB
Activations
4.22 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#8Qwen 2.5 1.5B Instruct
1.5B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 11.7 GBHeadroom: 4.3 GB
502
tok/s
E
Weights
0.91 GB
KV cache
0.75 GB
Activations
8.24 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#9Granite 3.0 2B Instruct
2B
granite
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 7.7 GBHeadroom: 8.3 GB
377
tok/s
E
Weights
1.21 GB
KV cache
0.50 GB
Activations
4.16 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#10DeepSeek R1 Distill Qwen 1.5B
1.5B
deepseek
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 11.7 GBHeadroom: 4.3 GB
502
tok/s
E
Weights
0.91 GB
KV cache
0.75 GB
Activations
8.24 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#11Qwen 2.5 Coder 1.5B
1.5B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 11.7 GBHeadroom: 4.3 GB
502
tok/s
E
Weights
0.91 GB
KV cache
0.75 GB
Activations
8.24 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#12RWKV 7 'Goose' 1.5B
1.5B
rwkv
Commercial OK
Quant: Q5_K_MContext: 8,192VRAM: 11.8 GBHeadroom: 4.2 GB
441
tok/s
E
Weights
1.03 GB
KV cache
0.75 GB
Activations
8.24 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →

Runs with tradeoffs
73 models

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

Dolphin 3.0 Llama 3.2 3B
3B
dolphin
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 13.4 GBHeadroom: 2.6 GB
  • • Tight VRAM fit — only 2.6 GB headroom left for context growth
251
tok/s
E
Weights
1.81 GB
KV cache
1.50 GB
Activations
8.28 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Hermes 3 Llama 3.2 3B
3B
hermes
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 13.4 GBHeadroom: 2.6 GB
  • • Tight VRAM fit — only 2.6 GB headroom left for context growth
251
tok/s
E
Weights
1.81 GB
KV cache
1.50 GB
Activations
8.28 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Gemma 4 E4B (Effective 4B)
4B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 14.5 GBHeadroom: 1.5 GB
  • • Tight VRAM fit — only 1.5 GB headroom left for context growth
ollama run gemma4:e4b
188
tok/s
E
Weights
2.42 GB
KV cache
2.00 GB
Activations
8.31 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Gemma 3 4B
4B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 14.5 GBHeadroom: 1.5 GB
  • • Tight VRAM fit — only 1.5 GB headroom left for context growth
ollama run gemma3:4b
188
tok/s
E
Weights
2.42 GB
KV cache
2.00 GB
Activations
8.31 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Gemma 4 E2B (Effective 2B)
2B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 13.2 GBHeadroom: 2.8 GB
  • • Tight VRAM fit — only 2.8 GB headroom left for context growth
ollama run gemma4:e2b
214
tok/s
E
Weights
2.13 GB
KV cache
1.00 GB
Activations
8.30 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Llama 3.2 3B Instruct
3B
llama
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 14.8 GBHeadroom: 1.2 GB
  • • Tight VRAM fit — only 1.2 GB headroom left for context growth
ollama run llama3.2:3b
143
tok/s
E
Weights
3.19 GB
KV cache
1.50 GB
Activations
8.35 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Qwen 3 4B
4B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 14.5 GBHeadroom: 1.5 GB
  • • Tight VRAM fit — only 1.5 GB headroom left for context growth
ollama run qwen3:4b
188
tok/s
E
Weights
2.42 GB
KV cache
2.00 GB
Activations
8.31 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
Phi-3.5 Mini Instruct
3.8B
phi
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 14.3 GBHeadroom: 1.7 GB
  • • Tight VRAM fit — only 1.7 GB headroom left for context growth
ollama run phi3.5:3.8b
198
tok/s
E
Weights
2.29 GB
KV cache
1.90 GB
Activations
8.31 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →

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: 7 new comfortable, 82 new tradeoff

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Whisper Large v3 Turbo
  • • BGE M3
Shop this upgrade↗

Upgrade to NVIDIA GeForce RTX 3090 Ti

~$1199

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

Unlocks: 46 new comfortable

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Gemma 4 E2B (Effective 2B)
  • • Llama 3.2 3B Instruct
Shop this upgrade↗

Add a second NVIDIA GeForce RTX 4070 Ti Super

~$829

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: 63 new comfortable

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Gemma 4 E2B (Effective 2B)
  • • Llama 3.2 3B Instruct
Shop this upgrade↗

Some links above are affiliate links. We may earn a commission at no extra cost to you. How we make money.

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 3.5 235B-A17B (MoE)
397B
qwen
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 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.

—
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.

—

How to read these numbers

M
Measured — we ran this exact combo on owner hardware.

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

E
Estimated — pure formula based on VRAM bandwidth and model architecture.

Full methodology →

Want a specific benchmark we don't have? Email support@runlocalai.co and we'll prioritize it.