RUNLOCALAIv38
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RUNLOCALAI · v38
Will it run? / Lenovo Legion 5 Pro Gen 7 (RTX 3080 16GB) / creative

What can Lenovo Legion 5 Pro Gen 7 (RTX 3080 16GB) run for creative?

Build: Lenovo Legion 5 Pro Gen 7 (RTX 3080 16GB) + — + 32 GB RAM (windows)

Memory: 16 GB VRAM + 32 GB system RAM
Runner: llama.cpp / Ollama (CUDA)
AnyChatCodingAgentsReasoningVisionLong contextCreative

Runs comfortably
123 models

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

#1Hermes 3 Llama 3.2 3B
3B
hermes
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 5.2 GBHeadroom: 10.8 GB
251
tok/s
Estimated
Weights
1.81 GB
KV cache
1.50 GB
Activations
0.10 GB
Runtime
1.80 GB
Model details →
#2Dolphin 3.0 Llama 3.2 3B
3B
dolphin
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 5.2 GBHeadroom: 10.8 GB
251
tok/s
Estimated
Weights
1.81 GB
KV cache
1.50 GB
Activations
0.10 GB
Runtime
1.80 GB
Model details →
#3Gemma 2 2B Instruct
2B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 4.1 GBHeadroom: 11.9 GB
377
tok/s
Estimated
Weights
1.21 GB
KV cache
1.00 GB
Activations
0.07 GB
Runtime
1.80 GB
Model details →
#4Gemma 4 E2B (Effective 2B)
2B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 5.0 GBHeadroom: 11.0 GB
ollama run gemma4:e2b
214
tok/s
Estimated
Weights
2.13 GB
KV cache
1.00 GB
Activations
0.11 GB
Runtime
1.80 GB
Model details →
#5ColPali v1.3
3B
gemma
Commercial OK
Quant: Q4_K_MContext: 0VRAM: 3.7 GBHeadroom: 12.3 GB
251
tok/s
Estimated
Weights
1.81 GB
KV cache
0.00 GB
Activations
0.09 GB
Runtime
1.80 GB
Model details →
#6CodeGemma 7B
7B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 6.3 GB
ollama run codegemma:7b
108
tok/s
Estimated
Weights
4.23 GB
KV cache
3.50 GB
Activations
0.22 GB
Runtime
1.80 GB
Model details →
#7Gemma 4 E4B (Effective 4B)
4B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 8.3 GBHeadroom: 7.7 GB
ollama run gemma4:e4b
107
tok/s
Estimated
Weights
4.25 GB
KV cache
2.00 GB
Activations
0.22 GB
Runtime
1.80 GB
Model details →
#8Gemma 3 4B
4B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 8.3 GBHeadroom: 7.7 GB
ollama run gemma3:4b
107
tok/s
Estimated
Weights
4.25 GB
KV cache
2.00 GB
Activations
0.22 GB
Runtime
1.80 GB
Model details →
#9Qwen 3 1.7B
1.7B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 3.7 GBHeadroom: 12.3 GB
443
tok/s
Estimated
Weights
1.03 GB
KV cache
0.85 GB
Activations
0.06 GB
Runtime
1.80 GB
Model details →
#10mxbai-rerank-large-v2
1.54B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 3.6 GBHeadroom: 12.4 GB
489
tok/s
Estimated
Weights
0.93 GB
KV cache
0.77 GB
Activations
0.05 GB
Runtime
1.80 GB
Model details →
#11Qwen2-VL 2B Instruct
2B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 4.1 GBHeadroom: 11.9 GB
377
tok/s
Estimated
Weights
1.21 GB
KV cache
1.00 GB
Activations
0.07 GB
Runtime
1.80 GB
Model details →
#12Qwen 3.5 2B Turkish SFT
2B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 4.1 GBHeadroom: 11.9 GB
377
tok/s
Estimated
Weights
1.21 GB
KV cache
1.00 GB
Activations
0.07 GB
Runtime
1.80 GB
Model details →

Runs with tradeoffs
79 models

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

Hermes 3 Llama 3.1 8B
8B
hermes
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 14.7 GBHeadroom: 1.3 GB
  • • Tight VRAM fit — only 1.3 GB headroom left for context growth
ollama run hermes3:8b
54
tok/s
Estimated
Weights
8.50 GB
KV cache
4.00 GB
Activations
0.43 GB
Runtime
1.80 GB
Model details →
Gemma 2 9B Instruct
9B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 12.0 GBHeadroom: 4.0 GB
  • • Tight VRAM fit — only 4.0 GB headroom left for context growth
ollama run gemma2:9b
84
tok/s
Estimated
Weights
5.43 GB
KV cache
4.50 GB
Activations
0.28 GB
Runtime
1.80 GB
Model details →
DeepSeek V2 Lite Chat
15.7B
deepseek
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 13.7 GBHeadroom: 2.3 GB
  • • Tight VRAM fit — only 2.3 GB headroom left for context growth
314
tok/s
Estimated
Weights
9.48 GB
KV cache
1.96 GB
Activations
0.48 GB
Runtime
1.80 GB
Model details →
DeepSeek V3 Lite (16B MoE)
16B
deepseek
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 13.9 GBHeadroom: 2.1 GB
  • • Tight VRAM fit — only 2.1 GB headroom left for context growth
314
tok/s
Estimated
Weights
9.66 GB
KV cache
2.00 GB
Activations
0.49 GB
Runtime
1.80 GB
Model details →
Granite 3 MoE (3B active)
16B
granite
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 13.9 GBHeadroom: 2.1 GB
  • • Tight VRAM fit — only 2.1 GB headroom left for context growth
251
tok/s
Estimated
Weights
9.66 GB
KV cache
2.00 GB
Activations
0.49 GB
Runtime
1.80 GB
Model details →
DeepSeek MoE 16B Base
16B
deepseek
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 15.9 GBHeadroom: 0.1 GB
  • • Tight VRAM fit — only 0.1 GB headroom left for context growth
314
tok/s
Estimated
Weights
9.66 GB
KV cache
4.00 GB
Activations
0.49 GB
Runtime
1.80 GB
Model details →
Turkish Gemma 9B T1
9B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 12.0 GBHeadroom: 4.0 GB
  • • Tight VRAM fit — only 4.0 GB headroom left for context growth
84
tok/s
Estimated
Weights
5.43 GB
KV cache
4.50 GB
Activations
0.28 GB
Runtime
1.80 GB
Model details →
YTU Turkish Gemma 9B v0.1
9.2B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 12.2 GBHeadroom: 3.8 GB
  • • Tight VRAM fit — only 3.8 GB headroom left for context growth
ollama run alibayram/turkish-gemma-9b-v0.1:latest
82
tok/s
Estimated
Weights
5.55 GB
KV cache
4.60 GB
Activations
0.29 GB
Runtime
1.80 GB
Model details →

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: 49 new comfortable, 89 new tradeoff

  • • all-MiniLM-L6-v2
  • • Piper
  • • Whisper Tiny
  • • Whisper Base
Shop this upgrade↗

Upgrade to NVIDIA RTX 2080 Ti 22GB (China-mod)

~$350

22 GB VRAM (vs your 16 GB) plus a bandwidth jump from ~? GB/s to ~616 GB/s.

Unlocks: 77 new comfortable

  • • all-MiniLM-L6-v2
  • • Piper
  • • Whisper Tiny
  • • Whisper Base
Shop this upgrade↗

Add a second Lenovo Legion 5 Pro Gen 7 (RTX 3080 16GB)

~$1499

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: 111 new comfortable

  • • all-MiniLM-L6-v2
  • • Piper
  • • Whisper Tiny
  • • Whisper Base
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 (16 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 (16 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 (16 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 (16 GB) + 60% of system RAM (19 GB) combined.

—
DeepSeek V4 Flash (284B MoE)
284B
deepseek
Commercial OK

Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.

—

How to read these numbers

Measured here
Measured here - RunLocalAI ran this exact combo on owner hardware with public evidence.

Source-backed
Source-backed / community - a reproduced public source supports the speed, but it is not labeled as owner-measured.

Extrapolated
Extrapolated - predicted from a measured benchmark on similar-bandwidth hardware.

Estimated
Estimated - formula based on VRAM bandwidth and model architecture; not a benchmark row.

RunLocalAI Will-It-Run Framework →

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