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

103 models fit comfortably on NVIDIA RTX 2080 Ti 22GB (China-mod) with 32 GB system RAM. 46 more run with tradeoffs. Top pick: Hermes 3 Llama 3.1 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 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)

Any Chat Coding Agents Reasoning Vision Long context Creative

Runs comfortably
103 models

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

#1Hermes 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 →

#2Gemma 2 9B Instruct

gemma

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 16.6 GBHeadroom: 5.4 GBTTFT: noticeable

ollama run gemma2:9b

tok/s

Estimated

Weights

9.80 GB

KV cache

4.50 GB

Activations

0.50 GB

Runtime

1.80 GB

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

Model details →

#3Dolphin 3.0 Llama 3.2 3B

dolphin

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 5.2 GBHeadroom: 16.8 GBTTFT: fast

221

tok/s

Estimated

Weights

1.80 GB

KV cache

1.50 GB

Activations

0.10 GB

Runtime

1.80 GB

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

Model details →

#4Hermes 3 Llama 3.2 3B

hermes

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 5.2 GBHeadroom: 16.8 GBTTFT: fast

221

tok/s

Estimated

Weights

1.80 GB

KV cache

1.50 GB

Activations

0.10 GB

Runtime

1.80 GB

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

Model details →

#5Gemma 2 2B Instruct

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 4.0 GBHeadroom: 18.0 GBTTFT: fast

332

tok/s

Estimated

Weights

1.10 GB

KV cache

1.00 GB

Activations

0.06 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~190 ms (fast)

Model details →

#6Gemma 4 E2B (Effective 2B)

gemma

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 5.1 GBHeadroom: 16.9 GBTTFT: fast

ollama run gemma4:e2b

188

tok/s

Estimated

Weights

2.20 GB

KV cache

1.00 GB

Activations

0.12 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~190 ms (fast)

Model details →

#7CodeGemma 7B

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 12.3 GBTTFT: noticeable

ollama run codegemma:7b

tok/s

Estimated

Weights

4.20 GB

KV cache

3.50 GB

Activations

0.22 GB

Runtime

1.80 GB

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

Model details →

#8Gemma 4 E4B (Effective 4B)

gemma

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 13.6 GBTTFT: fast

ollama run gemma4:e4b

tok/s

Estimated

Weights

4.40 GB

KV cache

2.00 GB

Activations

0.23 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~381 ms (fast)

Model details →

#9Gemma 3 4B

gemma

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 13.6 GBTTFT: fast

ollama run gemma3:4b

tok/s

Estimated

Weights

4.40 GB

KV cache

2.00 GB

Activations

0.23 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~381 ms (fast)

Model details →

#10Qwen 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 →

#11Llama 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 →

#12Kumru 2B

2.4B

mistral

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 4.6 GBHeadroom: 17.4 GBTTFT: fast

ollama run alibayram/kumru:latest

276

tok/s

Estimated

Weights

1.50 GB

KV cache

1.20 GB

Activations

0.08 GB

Runtime

1.80 GB

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

Model details →

Runs with tradeoffs
46 models

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

DeepSeek V2 Lite Chat

15.7B

deepseek

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 18.7 GBHeadroom: 3.3 GBTTFT: fast

• Tight VRAM fit — only 3.3 GB headroom left for context growth

276

tok/s

Estimated

Weights

8.60 GB

KV cache

7.85 GB

Activations

0.44 GB

Runtime

1.80 GB

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

Model details →

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 →

Granite 3 MoE (3B active)

16B

granite

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

221

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): ~286 ms (fast)

Model details →

Dolphin 3.0 Mistral 24B

24B

dolphin

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 dolphin-mistral: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 →

Jamba 1.5 Mini

52B

other

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 39.8 GBHeadroom: 1.4 GBTTFT: noticeable

• Partial CPU offload: ~45% of layers run on CPU

tok/s

Estimated

Weights

30.00 GB

KV cache

6.50 GB

Activations

1.50 GB

Runtime

1.80 GB

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

Model details →

Qwen 2.5 14B Instruct

14B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 18.2 GBHeadroom: 3.8 GBTTFT: noticeable

• Tight VRAM fit — only 3.8 GB headroom left for context growth

ollama run qwen2.5:14b

tok/s

Estimated

Weights

8.90 GB

KV cache

7.00 GB

Activations

0.45 GB

Runtime

1.80 GB

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

Model details →

Phi-4 Multimodal

14B

phi

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 18.3 GBHeadroom: 3.7 GBTTFT: noticeable

• Tight VRAM fit — only 3.7 GB headroom left for context growth

tok/s

Estimated

Weights

9.00 GB

KV cache

7.00 GB

Activations

0.46 GB

Runtime

1.80 GB

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

Model details →

StarCoder 2 15B

15B

other

Commercial OK

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

tok/s

Estimated

Weights

9.00 GB

KV cache

7.50 GB

Activations

0.46 GB

Runtime

1.80 GB

Time to first token (prefill, 512-token prompt): ~1428 ms (noticeable)

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: 13 new comfortable, 62 new tradeoff

• Qwen 3 0.6B
• Gemma 3 270M
• SmolLM2 135M Instruct
• TinyLlama 1.1B Chat v1.0

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: 28 new comfortable

• Qwen 3 0.6B
• Gemma 3 270M
• Mistral Small 3 24B
• Qwen 2.5 14B Instruct

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: 56 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