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

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)
AnyChatCodingAgentsReasoningVisionLong contextCreative

Runs comfortably
36 models

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

#1DeepSeek V3 Lite (16B MoE)
16B
deepseek
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 16.0 GBHeadroom: 6.0 GBTTFT: fast
276
tok/s
E
Weights
9.66 GB
KV cache
2.00 GB
Activations
2.53 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~228 ms (fast)
Model details →Run-on benchmark page →
#2Phi-4 Reasoning 14B
14B
phi
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 14.5 GBHeadroom: 7.5 GBTTFT: noticeable
ollama run phi4-reasoning:14b
47
tok/s
E
Weights
8.45 GB
KV cache
1.75 GB
Activations
2.47 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~1332 ms (noticeable)
Model details →Run-on benchmark page →
#3DeepSeek R1 Distill Qwen 14B
14B
deepseek
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 14.5 GBHeadroom: 7.5 GBTTFT: noticeable
ollama run deepseek-r1:14b
47
tok/s
E
Weights
8.45 GB
KV cache
1.75 GB
Activations
2.47 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~1332 ms (noticeable)
Model details →Run-on benchmark page →
#4Qwen 2.5 Math 7B
7B
qwen
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 12.1 GBHeadroom: 9.9 GBTTFT: noticeable
95
tok/s
E
Weights
4.23 GB
KV cache
1.75 GB
Activations
4.31 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~666 ms (noticeable)
Model details →Run-on benchmark page →
#5Qwen 3 7B
7B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.9 GBHeadroom: 4.1 GBTTFT: noticeable
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 →
#6InternLM 2.5 7B Chat
7B
internlm
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.9 GBHeadroom: 4.1 GBTTFT: noticeable
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 →
#7Phi-4 14B
14B
phi
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 14.5 GBHeadroom: 7.5 GBTTFT: noticeable
ollama run phi4:14b
47
tok/s
E
Weights
8.45 GB
KV cache
1.75 GB
Activations
2.47 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~1332 ms (noticeable)
Model details →Run-on benchmark page →
#8Granite 3 MoE (3B active)
16B
granite
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 16.0 GBHeadroom: 6.0 GBTTFT: fast
221
tok/s
E
Weights
9.66 GB
KV cache
2.00 GB
Activations
2.53 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~286 ms (fast)
Model details →Run-on benchmark page →
#9CodeGemma 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 →
#10LLaVA 1.6 Mistral 7B
7B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.9 GBHeadroom: 4.1 GBTTFT: noticeable
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 →
#11StarCoder 2 7B
7B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.9 GBHeadroom: 4.1 GBTTFT: noticeable
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 →
#12Falcon 3 7B Instruct
7B
falcon
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.9 GBHeadroom: 4.1 GBTTFT: noticeable
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 →

Runs with tradeoffs
55 models

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

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 →
DeepSeek R1 Distill Qwen 7B
7B
deepseek
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 21.3 GBHeadroom: 0.7 GBTTFT: noticeable
  • • Tight VRAM fit — only 0.7 GB headroom left for context growth
ollama run deepseek-r1:7b
54
tok/s
E
Weights
7.44 GB
KV cache
3.50 GB
Activations
8.56 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~666 ms (noticeable)
Model details →Run-on benchmark page →
DeepSeek R1 Distill Mistral 24B
24B
deepseek
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 37.2 GBHeadroom: 4.0 GBTTFT: slow
  • • Partial CPU offload: ~41% of layers run on CPU
28
tok/s
E
Weights
14.49 GB
KV cache
12.00 GB
Activations
8.92 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~2284 ms (slow)
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
21
tok/s
E
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 →
QwQ 32B Preview
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 qwq:32b
21
tok/s
E
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 →
Nemotron 3 Nano (30B-A3B)
30B
other
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 nemotron3:nano
22
tok/s
E
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 →
Nemotron 3 Nano 9B
9B
other
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
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 →

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: 36 new comfortable, 72 new tradeoff

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Gemma 4 E2B (Effective 2B)
  • • Llama 3.2 3B 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: 53 new comfortable

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Gemma 4 E2B (Effective 2B)
  • • Llama 3.2 3B 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: 89 new comfortable

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Gemma 4 E2B (Effective 2B)
  • • Llama 3.2 3B Instruct
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.