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

72 models fit comfortably on NVIDIA RTX 2080 Ti 22GB (China-mod) with 32 GB system RAM. 55 more run with tradeoffs. Top pick: Gemma 3 1B.

What's the recommended runner for this hardware?

llama.cpp / Ollama (CUDA). This is the runner that gives best tokens-per-second on NVIDIA RTX 2080 Ti 22GB (China-mod) based on windows support and the GPU backend.

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

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)

Any Chat Coding Agents Reasoning Vision Long context Creative

Runs comfortably
72 models

Full-VRAM resident, with room for context. No compromises.

#1Gemma 3 1B

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 11.1 GBHeadroom: 10.9 GBTTFT: instant

ollama run gemma3:1b

663

tok/s

Weights

0.60 GB

KV cache

0.50 GB

Activations

8.22 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~95 ms (instant)

Model details →Run-on benchmark page →

#2Llama 3.2 1B Instruct

llama

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 11.6 GBHeadroom: 10.4 GBTTFT: instant

ollama run llama3.2:1b

377

tok/s

Weights

1.06 GB

KV cache

0.50 GB

Activations

8.25 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~95 ms (instant)

Model details →Run-on benchmark page →

#3Gemma 4 E2B (Effective 2B)

gemma

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 13.2 GBHeadroom: 8.8 GBTTFT: fast

ollama run gemma4:e2b

188

tok/s

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 →

#4Llama 3.2 3B Instruct

llama

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 14.8 GBHeadroom: 7.2 GBTTFT: fast

ollama run llama3.2:3b

126

tok/s

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 →

#5Phi-3.5 Vision

4.2B

phi

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 14.8 GBHeadroom: 7.2 GBTTFT: fast

158

tok/s

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 →

#6Phi-3.5 Mini Instruct

3.8B

phi

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 16.1 GBHeadroom: 5.9 GBTTFT: fast

ollama run phi3.5:3.8b

tok/s

Weights

4.04 GB

KV cache

1.90 GB

Activations

8.39 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~362 ms (fast)

Model details →Run-on benchmark page →

#7Gemma 4 E4B (Effective 4B)

gemma

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 16.5 GBHeadroom: 5.5 GBTTFT: fast

ollama run gemma4:e4b

tok/s

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 →

#8Qwen 3 4B

qwen

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 16.5 GBHeadroom: 5.5 GBTTFT: fast

ollama run qwen3:4b

tok/s

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 →

#9Gemma 3 4B

gemma

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 16.5 GBHeadroom: 5.5 GBTTFT: fast

ollama run gemma3:4b

tok/s

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 →

#10CodeGemma 7B

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 17.9 GBHeadroom: 4.1 GBTTFT: noticeable

ollama run codegemma:7b

tok/s

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 →

#11Llama 3.2 11B Vision Instruct

11B

llama

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 12.2 GBHeadroom: 9.8 GBTTFT: noticeable

ollama run llama3.2-vision:11b

tok/s

Weights

6.64 GB

KV cache

1.38 GB

Activations

2.38 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~1047 ms (noticeable)

Model details →Run-on benchmark page →

#12Mistral Nemo 12B Instruct

12B

mistral

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 13.0 GBHeadroom: 9.0 GBTTFT: noticeable

ollama run mistral-nemo:12b

tok/s

Weights

7.25 GB

KV cache

1.50 GB

Activations

2.41 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~1142 ms (noticeable)

Model details →Run-on benchmark page →

Runs with tradeoffs
55 models

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

Llama 3.1 8B Instruct

llama

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 20.0 GBHeadroom: 2.0 GBTTFT: noticeable

• Tight VRAM fit — only 2.0 GB headroom left for context growth

ollama run llama3.1:8b

tok/s

Weights

8.50 GB

KV cache

1.07 GB

Activations

8.62 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~761 ms (noticeable)

Model details →Run-on benchmark page →

Qwen 3 30B-A3B

30B

qwen

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 26.6 GBHeadroom: 14.6 GBTTFT: slow

• Partial CPU offload: ~17% of layers run on CPU

ollama run qwen3:30b

tok/s

Weights

18.11 GB

KV cache

3.75 GB

Activations

2.95 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~2855 ms (slow)

Model details →Run-on benchmark page →

Qwen 2.5 Coder 32B Instruct

32B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 32.4 GBHeadroom: 8.8 GBTTFT: slow

• Partial CPU offload: ~32% of layers run on CPU

ollama run qwen2.5-coder:32b

tok/s

Weights

19.32 GB

KV cache

2.15 GB

Activations

9.16 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~3045 ms (slow)

Model details →Run-on benchmark page →

Qwen 3 32B

32B

qwen

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 28.1 GBHeadroom: 13.1 GBTTFT: slow

• Partial CPU offload: ~22% of layers run on CPU

ollama run qwen3:32b

tok/s

Weights

19.32 GB

KV cache

4.00 GB

Activations

3.01 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~3045 ms (slow)

Model details →Run-on benchmark page →

Gemma 4 31B Dense

31B

gemma

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 27.4 GBHeadroom: 13.8 GBTTFT: slow

• Partial CPU offload: ~20% of layers run on CPU

ollama run gemma4:31b

tok/s

Weights

18.72 GB

KV cache

3.88 GB

Activations

2.98 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~2950 ms (slow)

Model details →Run-on benchmark page →

Qwen 3 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

tok/s

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 Qwen 32B

32B

deepseek

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 28.1 GBHeadroom: 13.1 GBTTFT: slow

• Partial CPU offload: ~22% of layers run on CPU

ollama run deepseek-r1:32b

tok/s

Weights

19.32 GB

KV cache

4.00 GB

Activations

3.01 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~3045 ms (slow)

Model details →Run-on benchmark page →

Gemma 4 26B MoE

26B

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 39.5 GBHeadroom: 1.7 GBTTFT: slow

• Partial CPU offload: ~44% of layers run on CPU

ollama run gemma4:26b-moe

tok/s

Weights

15.70 GB

KV cache

13.00 GB

Activations

8.98 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~2474 ms (slow)

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: 72 new tradeoff

• Llama 3.1 8B Instruct
• Qwen 3 30B-A3B
• Qwen 2.5 Coder 32B Instruct
• Llama 3.3 70B 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: 17 new comfortable

• Llama 3.1 Nemotron Nano 8B
• Mistral 7B Instruct v0.3
• Qwen 2.5 7B Instruct
• Llama 3.1 8B Instruct

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: 53 new comfortable

• Llama 3.1 Nemotron Nano 8B
• Mistral 7B Instruct v0.3
• Qwen 2.5 7B Instruct
• DeepSeek R1 Distill Qwen 7B

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