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

133 models fit comfortably on Lenovo Legion 5 Pro Gen 7 (RTX 3080 16GB) with 32 GB system RAM. 94 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 Lenovo Legion 5 Pro Gen 7 (RTX 3080 16GB) based on windows support and the GPU backend.

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

Build: Lenovo Legion 5 Pro Gen 7 (RTX 3080 16GB)

Memory: 16 GB VRAM + 32 GB system RAM

Runner: llama.cpp / Ollama (CUDA)

Any Chat Coding Agents Reasoning Vision Long context Creative

Runs comfortably
133 models

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

#1Qwen 3 0.6B

0.6B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 2.4 GBHeadroom: 13.6 GB

1256

tok/s

Estimated

Weights

0.30 GB

KV cache

0.30 GB

Activations

0.02 GB

Runtime

1.80 GB

Model details →

#2Llama 3.2 3B Instruct

llama

Commercial OK

Quant: Q8_0Context: 8,192VRAM: 6.9 GBHeadroom: 9.1 GB

ollama run llama3.2:3b

143

tok/s

Estimated

Weights

3.40 GB

KV cache

1.50 GB

Activations

0.18 GB

Runtime

1.80 GB

Model details →

#3Qwen 3 1.7B

1.7B

qwen

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 3.6 GBHeadroom: 12.4 GB

443

tok/s

Estimated

Weights

0.90 GB

KV cache

0.85 GB

Activations

0.05 GB

Runtime

1.80 GB

Model details →

#4TinyLlama 1.1B Chat v1.0

1.1B

llama

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 2.6 GBHeadroom: 13.4 GB

685

tok/s

Estimated

Weights

0.60 GB

KV cache

0.14 GB

Activations

0.03 GB

Runtime

1.80 GB

Model details →

#5Gemma 2 2B Instruct

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 4.0 GBHeadroom: 12.0 GB

377

tok/s

Estimated

Weights

1.10 GB

KV cache

1.00 GB

Activations

0.06 GB

Runtime

1.80 GB

Model details →

#6Kumru 2B

2.4B

mistral

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 4.6 GBHeadroom: 11.4 GB

ollama run alibayram/kumru:latest

314

tok/s

Estimated

Weights

1.50 GB

KV cache

1.20 GB

Activations

0.08 GB

Runtime

1.80 GB

Model details →

#7TinyLlama 1.1B Chat v0.3 AWQ

1.1B

other

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 2.6 GBHeadroom: 13.4 GB

685

tok/s

Estimated

Weights

0.60 GB

KV cache

0.14 GB

Activations

0.03 GB

Runtime

1.80 GB

Model details →

#8TinyLlama 1.1B Chat v0.3 GPTQ

1.1B

other

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 2.6 GBHeadroom: 13.4 GB

685

tok/s

Estimated

Weights

0.60 GB

KV cache

0.14 GB

Activations

0.03 GB

Runtime

1.80 GB

Model details →

#9EXAONE 3.5 2.4B Instruct

2.4B

exaone

Quant: Q4_K_MContext: 8,192VRAM: 4.4 GBHeadroom: 11.6 GB

314

tok/s

Estimated

Weights

1.30 GB

KV cache

1.20 GB

Activations

0.07 GB

Runtime

1.80 GB

Model details →

#10Falcon 3 3B Instruct

falcon

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 5.1 GBHeadroom: 10.9 GB

251

tok/s

Estimated

Weights

1.70 GB

KV cache

1.50 GB

Activations

0.09 GB

Runtime

1.80 GB

Model details →

#11PhoGPT 4B Chat

3.7B

other

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 5.8 GBHeadroom: 10.2 GB

204

tok/s

Estimated

Weights

2.00 GB

KV cache

1.85 GB

Activations

0.11 GB

Runtime

1.80 GB

Model details →

#12Salamandra 2B Instruct

llama

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 4.0 GBHeadroom: 12.0 GB

377

tok/s

Estimated

Weights

1.10 GB

KV cache

1.00 GB

Activations

0.06 GB

Runtime

1.80 GB

Model details →

Runs with tradeoffs
94 models

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

DeepSeek V2 Lite Chat

15.7B

deepseek

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 12.8 GBHeadroom: 3.2 GB

• Tight VRAM fit — only 3.2 GB headroom left for context growth

314

tok/s

Estimated

Weights

8.60 GB

KV cache

1.96 GB

Activations

0.43 GB

Runtime

1.80 GB

Model details →

Gemma 2 9B Instruct

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 12.4 GBHeadroom: 3.6 GB

• Tight VRAM fit — only 3.6 GB headroom left for context growth

ollama run gemma2:9b

tok/s

Estimated

Weights

5.80 GB

KV cache

4.50 GB

Activations

0.30 GB

Runtime

1.80 GB

Model details →

Bielik 11B v3.0 Instruct GGUF

11B

llama

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 13.7 GBHeadroom: 2.3 GB

• Tight VRAM fit — only 2.3 GB headroom left for context growth

tok/s

Estimated

Weights

6.10 GB

KV cache

5.50 GB

Activations

0.31 GB

Runtime

1.80 GB

Model details →

Mistral Nemo 12B Instruct

12B

mistral

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 15.7 GBHeadroom: 0.3 GB

• Tight VRAM fit — only 0.3 GB headroom left for context growth

ollama run mistral-nemo:12b

tok/s

Estimated

Weights

7.50 GB

KV cache

6.00 GB

Activations

0.38 GB

Runtime

1.80 GB

Model details →

Gemma 3 12B

12B

gemma

Commercial OK

Quant: Q4_K_MContext: 8,192VRAM: 15.5 GBHeadroom: 0.5 GB

• Tight VRAM fit — only 0.5 GB headroom left for context growth

ollama run gemma3:12b

tok/s

Estimated

Weights

7.30 GB

KV cache

6.00 GB

Activations

0.37 GB

Runtime

1.80 GB

Model details →

Stable LM 2 12B

12B

other

Quant: Q4_K_MContext: 4,096VRAM: 12.4 GBHeadroom: 3.6 GB

• Tight VRAM fit — only 3.6 GB headroom left for context growth

tok/s

Estimated

Weights

7.20 GB

KV cache

3.00 GB

Activations

0.36 GB

Runtime

1.80 GB

Model details →

OpenThaiGPT 1.0.0 Beta 13B Chat

13B

llama

Commercial OK

Quant: Q4_K_MContext: 4,096VRAM: 12.6 GBHeadroom: 3.4 GB

• Tight VRAM fit — only 3.4 GB headroom left for context growth

tok/s

Estimated

Weights

7.20 GB

KV cache

3.25 GB

Activations

0.36 GB

Runtime

1.80 GB

Model details →

Qwen 3 14B

14B

qwen

Commercial OK

Quant: Q4_K_MContext: 2,048VRAM: 12.4 GBHeadroom: 3.6 GB

• Tight VRAM fit — only 3.6 GB headroom left for context growth

ollama run qwen3:14b

tok/s

Estimated

Weights

8.40 GB

KV cache

1.75 GB

Activations

0.42 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: 9 new comfortable, 112 new tradeoff

• Gemma 3 270M
• SmolLM2 135M Instruct
• SmolLM2 360M Instruct
• VBART Large (Turkish Summarization)

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: 36 new comfortable

• Gemma 4 12B
• Qwen3.5 9B
• Qwen 3 14B
• Gemma 3 270M

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: 83 new comfortable

• Qwen 3 30B-A3B
• Qwen 2.5 Coder 32B Instruct
• Qwen3.6 27B
• Qwen 3 32B

Shop this upgrade↗

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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