RUNLOCALAIv38
→WILL IT RUNBEST GPUCOMPARETROUBLESHOOTSTARTPULSEMODELSHARDWARETOOLSBENCH
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RUNLOCALAI · v38
Will it run? / NVIDIA RTX 2080 Ti 22GB (China-mod) / creative

What can NVIDIA RTX 2080 Ti 22GB (China-mod) run for creative?

Build: NVIDIA RTX 2080 Ti 22GB (China-mod) + — + 32 GB RAM (windows)

Memory: 22 GB VRAM + 32 GB system RAM
Runner: llama.cpp / Ollama (CUDA)
AnyChatCodingAgentsReasoningVisionLong contextCreative

Runs comfortably
65 models

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

#1Dolphin 3.0 Llama 3.2 3B
3B
dolphin
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 13.4 GBHeadroom: 8.6 GBTTFT: fast
221
tok/s
E
Weights
1.81 GB
KV cache
1.50 GB
Activations
8.28 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~286 ms (fast)
Model details →Run-on benchmark page →
#2Hermes 3 Llama 3.2 3B
3B
hermes
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 13.4 GBHeadroom: 8.6 GBTTFT: fast
221
tok/s
E
Weights
1.81 GB
KV cache
1.50 GB
Activations
8.28 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~286 ms (fast)
Model details →Run-on benchmark page →
#3Gemma 4 E2B (Effective 2B)
2B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 13.2 GBHeadroom: 8.8 GBTTFT: fast
ollama run gemma4:e2b
188
tok/s
E
Weights
2.13 GB
KV cache
1.00 GB
Activations
8.30 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~190 ms (fast)
Model details →Run-on benchmark page →
#4CodeGemma 7B
7B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.9 GBHeadroom: 4.1 GBTTFT: noticeable
ollama run codegemma:7b
95
tok/s
E
Weights
4.23 GB
KV cache
3.50 GB
Activations
8.40 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~666 ms (noticeable)
Model details →Run-on benchmark page →
#5Gemma 4 E4B (Effective 4B)
4B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 16.5 GBHeadroom: 5.5 GBTTFT: fast
ollama run gemma4:e4b
94
tok/s
E
Weights
4.25 GB
KV cache
2.00 GB
Activations
8.40 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~381 ms (fast)
Model details →Run-on benchmark page →
#6Gemma 3 4B
4B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 16.5 GBHeadroom: 5.5 GBTTFT: fast
ollama run gemma3:4b
94
tok/s
E
Weights
4.25 GB
KV cache
2.00 GB
Activations
8.40 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~381 ms (fast)
Model details →Run-on benchmark page →
#7Llama 3.2 3B Instruct
3B
llama
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 14.8 GBHeadroom: 7.2 GBTTFT: fast
ollama run llama3.2:3b
126
tok/s
E
Weights
3.19 GB
KV cache
1.50 GB
Activations
8.35 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~286 ms (fast)
Model details →Run-on benchmark page →
#8Phi-3.5 Vision
4.2B
phi
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 14.8 GBHeadroom: 7.2 GBTTFT: fast
158
tok/s
E
Weights
2.54 GB
KV cache
2.10 GB
Activations
8.32 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~400 ms (fast)
Model details →Run-on benchmark page →
#9StarCoder 2 3B
3B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 13.4 GBHeadroom: 8.6 GBTTFT: fast
221
tok/s
E
Weights
1.81 GB
KV cache
1.50 GB
Activations
8.28 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~286 ms (fast)
Model details →Run-on benchmark page →
#10Whisper Large v3
1.55B
other
Commercial OK
Quant: FP16Context: 0VRAM: 5.1 GBHeadroom: 16.9 GBTTFT: fast
129
tok/s
E
Weights
3.10 GB
KV cache
0.00 GB
Activations
0.16 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~148 ms (fast)
Model details →Run-on benchmark page →
#11Nemotron Mini 4B Instruct
4B
other
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 9.4 GBHeadroom: 12.6 GBTTFT: fast
166
tok/s
E
Weights
2.42 GB
KV cache
1.00 GB
Activations
4.22 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~381 ms (fast)
Model details →Run-on benchmark page →
#12SmolLM 2 1.7B Instruct
1.7B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 11.9 GBHeadroom: 10.1 GBTTFT: fast
390
tok/s
E
Weights
1.03 GB
KV cache
0.85 GB
Activations
8.24 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~162 ms (fast)
Model details →Run-on benchmark page →

Runs with tradeoffs
55 models

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

Hermes 3 Llama 3.1 8B
8B
hermes
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 19.1 GBHeadroom: 2.9 GBTTFT: noticeable
  • • Tight VRAM fit — only 2.9 GB headroom left for context growth
ollama run hermes3:8b
83
tok/s
E
Weights
4.83 GB
KV cache
4.00 GB
Activations
8.43 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~761 ms (noticeable)
Model details →Run-on benchmark page →
Gemma 2 9B Instruct
9B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 20.2 GBHeadroom: 1.8 GBTTFT: noticeable
  • • Tight VRAM fit — only 1.8 GB headroom left for context growth
ollama run gemma2:9b
74
tok/s
E
Weights
5.43 GB
KV cache
4.50 GB
Activations
8.46 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~857 ms (noticeable)
Model details →Run-on benchmark page →
DeepSeek MoE 16B Base
16B
deepseek
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 20.0 GBHeadroom: 2.0 GBTTFT: fast
  • • Tight VRAM fit — only 2.0 GB headroom left for context growth
276
tok/s
E
Weights
9.66 GB
KV cache
4.00 GB
Activations
4.58 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~228 ms (fast)
Model details →Run-on benchmark page →
EXAONE 3.5 8B
7.8B
exaone
Quant: Q4_K_MContext: 8,192VRAM: 18.8 GBHeadroom: 3.2 GBTTFT: noticeable
  • • Tight VRAM fit — only 3.2 GB headroom left for context growth
85
tok/s
E
Weights
4.71 GB
KV cache
3.90 GB
Activations
8.43 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~742 ms (noticeable)
Model details →Run-on benchmark page →
Mistral 7B Instruct v0.3
7B
mistral
Commercial OK
Quant: Q5_K_MContext: 8,192VRAM: 18.5 GBHeadroom: 3.5 GBTTFT: noticeable
  • • Tight VRAM fit — only 3.5 GB headroom left for context growth
ollama run mistral:7b
83
tok/s
E
Weights
4.81 GB
KV cache
3.50 GB
Activations
8.43 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~666 ms (noticeable)
Model details →Run-on benchmark page →
Qwen 3 8B
8B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 19.1 GBHeadroom: 2.9 GBTTFT: noticeable
  • • Tight VRAM fit — only 2.9 GB headroom left for context growth
ollama run qwen3:8b
83
tok/s
E
Weights
4.83 GB
KV cache
4.00 GB
Activations
8.43 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~761 ms (noticeable)
Model details →Run-on benchmark page →
Llama 3.1 Nemotron Nano 8B
8B
llama
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 19.1 GBHeadroom: 2.9 GBTTFT: noticeable
  • • Tight VRAM fit — only 2.9 GB headroom left for context growth
83
tok/s
E
Weights
4.83 GB
KV cache
4.00 GB
Activations
8.43 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~761 ms (noticeable)
Model details →Run-on benchmark page →
DeepSeek R1 Distill Llama 8B
8B
deepseek
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 19.1 GBHeadroom: 2.9 GBTTFT: noticeable
  • • Tight VRAM fit — only 2.9 GB headroom left for context growth
83
tok/s
E
Weights
4.83 GB
KV cache
4.00 GB
Activations
8.43 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~761 ms (noticeable)
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, 72 new tradeoff

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Whisper Large v3 Turbo
  • • SmolLM 2 360M Instruct
Shop this upgrade↗

Upgrade to NVIDIA GeForce RTX 5090 Mobile

see current pricing

24 GB VRAM (vs your 22 GB) plus a bandwidth jump from ~616 GB/s to ~896 GB/s.

Unlocks: 24 new comfortable

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Llama 3.1 Nemotron Nano 8B
  • • Mistral 7B Instruct v0.3
Shop this upgrade↗

Add a second NVIDIA RTX 2080 Ti 22GB (China-mod)

~$350

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: 60 new comfortable

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Llama 3.1 Nemotron Nano 8B
  • • Mistral 7B Instruct v0.3
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 (22 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 (22 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 (22 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 (22 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 (22 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.