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

67 models fit comfortably on NVIDIA RTX 2080 Ti 22GB (China-mod) with 32 GB system RAM. 46 more run with tradeoffs. Top pick: Llama 3.1 Nemotron Nano 8B.

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 for reasoning?

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
67 models

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

#1Llama 3.1 Nemotron Nano 8B

llama

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 11.0 GBHeadroom: 11.0 GBTTFT: noticeable

tok/s

Estimated

Weights

4.90 GB

KV cache

4.00 GB

Activations

0.25 GB

Runtime

1.80 GB

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

Model details →

#2RefinedNeuro RN TR R1

llama

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 11.0 GBHeadroom: 11.0 GBTTFT: noticeable

ollama run RefinedNeuro/RN_TR_R1:latest

tok/s

Estimated

Weights

4.90 GB

KV cache

4.00 GB

Activations

0.25 GB

Runtime

1.80 GB

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

Model details →

#3DeepSeek R1 Distill Llama 8B

deepseek

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 10.7 GBHeadroom: 11.3 GBTTFT: noticeable

tok/s

Estimated

Weights

4.70 GB

KV cache

4.00 GB

Activations

0.24 GB

Runtime

1.80 GB

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

Model details →

#4DeepSeek 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 →

#5Phi-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 →

#6DeepSeek R1 Distill Qwen 14B

14B

deepseek

Commercial OK

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

ollama run deepseek-r1:14b

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 →

#7Qwen 3 7B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 9.9 GBHeadroom: 12.1 GBTTFT: noticeable

tok/s

Estimated

Weights

4.40 GB

KV cache

3.50 GB

Activations

0.23 GB

Runtime

1.80 GB

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

Model details →

#8Qwen 2.5 Math 7B

qwen

Commercial OK

Quant: Q4_K_MContext: 4,096VRAM: 8.2 GBHeadroom: 13.8 GBTTFT: noticeable

tok/s

Estimated

Weights

4.40 GB

KV cache

1.75 GB

Activations

0.22 GB

Runtime

1.80 GB

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

Model details →

#9InternLM 2.5 7B Chat

internlm

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 9.9 GBHeadroom: 12.1 GBTTFT: noticeable

tok/s

Estimated

Weights

4.40 GB

KV cache

3.50 GB

Activations

0.23 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 →

#11RefinedNeuro RN TR R2

llama

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 11.0 GBHeadroom: 11.0 GBTTFT: noticeable

ollama run RefinedNeuro/RN_TR_R2:latest

tok/s

Estimated

Weights

4.90 GB

KV cache

4.00 GB

Activations

0.25 GB

Runtime

1.80 GB

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

Model details →

#12Nemotron 3 Nano 9B

other

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 11.9 GBHeadroom: 10.1 GBTTFT: noticeable

tok/s

Estimated

Weights

5.30 GB

KV cache

4.50 GB

Activations

0.27 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~857 ms (noticeable)

Model details →

Runs with tradeoffs
46 models

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

DeepSeek V3 Lite (16B MoE)

16B

deepseek

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 19.8 GBHeadroom: 2.2 GBTTFT: fast

• Tight VRAM fit — only 2.2 GB headroom left for context growth

276

tok/s

Estimated

Weights

9.50 GB

KV cache

8.00 GB

Activations

0.48 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~228 ms (fast)

Model details →

DeepSeek R1 Distill Mistral 24B

24B

deepseek

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

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 →

Omni 31B Turkish Reasoning

31B

other

Quant: Q4_K_MContext: 8,192VRAM: 35.3 GBHeadroom: 5.9 GBTTFT: slow

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

tok/s

Estimated

Weights

17.10 GB

KV cache

15.50 GB

Activations

0.86 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 →

QwQ 32B Preview

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 qwq: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 →

DeepSeek R1 Distill Qwen 3 32B

32B

deepseek

Commercial OK

Quant: AWQ-INT4Context: 8,192VRAM: 37.8 GBHeadroom: 3.4 GBTTFT: slow

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

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 →

Magistral 32B

32B

mistral

Quant: AWQ-INT4Context: 8,192VRAM: 37.8 GBHeadroom: 3.4 GBTTFT: slow

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

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 →

Qwen 3 Coder 32B

32B

qwen

Commercial OK

Quant: AWQ-INT4Context: 8,192VRAM: 37.8 GBHeadroom: 3.4 GBTTFT: slow

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

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 →

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: 49 new comfortable, 62 new tradeoff

• Qwen 3 0.6B
• Qwen 3 1.7B
• Llama 3.2 3B Instruct
• Gemma 3 270M

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: 64 new comfortable

• Qwen 3 0.6B
• Qwen 3 1.7B
• Llama 3.2 3B Instruct
• Gemma 3 270M

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: 92 new comfortable

• Qwen 3 0.6B
• Qwen 3 30B-A3B
• Qwen 2.5 Coder 32B Instruct
• Gemma 4 31B Dense

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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