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

116 models fit comfortably on NVIDIA RTX 2080 Ti 22GB (China-mod) with 32 GB system RAM. 46 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 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
116 models

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

#1Qwen 3 0.6B

0.6B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 2.4 GBHeadroom: 19.6 GBTTFT: instant

1105

tok/s

Estimated

Weights

0.30 GB

KV cache

0.30 GB

Activations

0.02 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~57 ms (instant)

Model details →

#2Llama 3.1 8B Instruct

llama

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 11.8 GBHeadroom: 10.2 GBTTFT: noticeable

ollama run llama3.1:8b

tok/s

Estimated

Weights

8.50 GB

KV cache

1.07 GB

Activations

0.43 GB

Runtime

1.80 GB

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

Model details →

#3Qwen 3 8B

qwen

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 14.4 GBHeadroom: 7.6 GBTTFT: noticeable

ollama run qwen3:8b

tok/s

Estimated

Weights

8.20 GB

KV cache

4.00 GB

Activations

0.42 GB

Runtime

1.80 GB

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

Model details →

#4Qwen 3 1.7B

1.7B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 3.6 GBHeadroom: 18.4 GBTTFT: fast

390

tok/s

Estimated

Weights

0.90 GB

KV cache

0.85 GB

Activations

0.05 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~162 ms (fast)

Model details →

#5Qwen 3 14B

14B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 17.6 GBHeadroom: 4.4 GBTTFT: noticeable

ollama run qwen3:14b

tok/s

Estimated

Weights

8.40 GB

KV cache

7.00 GB

Activations

0.43 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~1332 ms (noticeable)

Model details →

#6Llama 3.2 3B Instruct

llama

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 6.9 GBHeadroom: 15.1 GBTTFT: fast

ollama run llama3.2:3b

126

tok/s

Estimated

Weights

3.40 GB

KV cache

1.50 GB

Activations

0.18 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~286 ms (fast)

Model details →

#7Gemma 3 270M

0.27B

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 2.1 GBHeadroom: 19.9 GBTTFT: instant

2456

tok/s

Estimated

Weights

0.16 GB

KV cache

0.14 GB

Activations

0.02 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~26 ms (instant)

Model details →

#8Qwen 2.5 7B Instruct

qwen

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 10.8 GBHeadroom: 11.2 GBTTFT: noticeable

ollama run qwen2.5:7b

tok/s

Estimated

Weights

8.10 GB

KV cache

0.47 GB

Activations

0.41 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~666 ms (noticeable)

Model details →

#9DeepSeek R1 Distill Qwen 7B

deepseek

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 13.8 GBHeadroom: 8.2 GBTTFT: noticeable

ollama run deepseek-r1:7b

tok/s

Estimated

Weights

8.10 GB

KV cache

3.50 GB

Activations

0.41 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~666 ms (noticeable)

Model details →

#10Phi-4 14B

14B

phi

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 17.6 GBHeadroom: 4.4 GBTTFT: noticeable

ollama run phi4:14b

tok/s

Estimated

Weights

8.40 GB

KV cache

7.00 GB

Activations

0.43 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~1332 ms (noticeable)

Model details →

#11Hermes 3 Llama 3.1 8B

hermes

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 14.7 GBHeadroom: 7.3 GBTTFT: noticeable

ollama run hermes3:8b

tok/s

Estimated

Weights

8.50 GB

KV cache

4.00 GB

Activations

0.43 GB

Runtime

1.80 GB

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

Model details →

#12Phi-4 Reasoning 14B

14B

phi

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 17.6 GBHeadroom: 4.4 GBTTFT: noticeable

ollama run phi4-reasoning:14b

tok/s

Estimated

Weights

8.40 GB

KV cache

7.00 GB

Activations

0.43 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~1332 ms (noticeable)

Model details →

Runs with tradeoffs
46 models

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

Qwen 3 30B-A3B

30B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 35.7 GBHeadroom: 5.5 GBTTFT: slow

• Partial CPU offload: ~38% of layers run on CPU

ollama run qwen3:30b

tok/s

Estimated

Weights

18.00 GB

KV cache

15.00 GB

Activations

0.91 GB

Runtime

1.80 GB

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

Model details →

Qwen 2.5 Coder 32B Instruct

32B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 23.9 GBHeadroom: 17.3 GBTTFT: slow

• Partial CPU offload: ~8% of layers run on CPU

ollama run qwen2.5-coder:32b

tok/s

Estimated

Weights

19.00 GB

KV cache

2.15 GB

Activations

0.96 GB

Runtime

1.80 GB

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

Model details →

Qwen 3 32B

32B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 37.8 GBHeadroom: 3.4 GBTTFT: slow

• Partial CPU offload: ~42% of layers run on CPU

ollama run qwen3:32b

tok/s

Estimated

Weights

19.00 GB

KV cache

16.00 GB

Activations

0.96 GB

Runtime

1.80 GB

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

Model details →

Gemma 4 31B Dense

31B

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 36.2 GBHeadroom: 5.0 GBTTFT: slow

• Partial CPU offload: ~39% of layers run on CPU

ollama run gemma4:31b

tok/s

Estimated

Weights

18.00 GB

KV cache

15.50 GB

Activations

0.91 GB

Runtime

1.80 GB

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

Model details →

DeepSeek R1 Distill Qwen 32B

32B

deepseek

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 37.8 GBHeadroom: 3.4 GBTTFT: slow

• Partial CPU offload: ~42% of layers run on CPU

ollama run deepseek-r1:32b

tok/s

Estimated

Weights

19.00 GB

KV cache

16.00 GB

Activations

0.96 GB

Runtime

1.80 GB

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

Model details →

Gemma 4 26B MoE

26B

gemma

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 21.9 GBHeadroom: 0.1 GBTTFT: slow

• Tight VRAM fit — only 0.1 GB headroom left for context growth

ollama run gemma4:26b-moe

tok/s

Estimated

Weights

16.00 GB

KV cache

3.25 GB

Activations

0.80 GB

Runtime

1.80 GB

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

Model details →

Nemotron 3 Nano (30B-A3B)

30B

other

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 35.7 GBHeadroom: 5.5 GBTTFT: slow

• Partial CPU offload: ~38% of layers run on CPU

ollama run nemotron3:nano

tok/s

Estimated

Weights

18.00 GB

KV cache

15.00 GB

Activations

0.91 GB

Runtime

1.80 GB

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

Model details →

Mistral Small 3 24B

24B

mistral

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 19.5 GBHeadroom: 2.5 GBTTFT: slow

• Tight VRAM fit — only 2.5 GB headroom left for context growth

ollama run mistral-small:24b

tok/s

Estimated

Weights

14.00 GB

KV cache

3.00 GB

Activations

0.70 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~2284 ms (slow)

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: 62 new tradeoff

• Qwen 3 30B-A3B
• Llama 3.3 70B Instruct
• Qwen 2.5 Coder 32B Instruct
• Qwen 3 32B

Shop this upgrade↗

Upgrade to NVIDIA GeForce RTX 3090 Ti

Launch MSRP $1,999 (2022). Now sells well below its launch price on the used market. Check the current price.

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

Unlocks: 15 new comfortable

• Mistral Small 3 24B
• Qwen 2.5 14B Instruct
• DeepSeek Coder V2 Lite (16B)
• Codestral 22B

Shop this upgrade↗

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

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: 43 new comfortable

• Qwen 3 30B-A3B
• Qwen 2.5 Coder 32B Instruct
• Gemma 4 31B Dense
• DeepSeek R1 Distill Qwen 32B

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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