What can Lenovo Legion 5 Pro Gen 7 (RTX 3080 16GB) run for agents?
Build: Lenovo Legion 5 Pro Gen 7 (RTX 3080 16GB) + — + 32 GB RAM (windows)
Runs comfortably10 models
Ranked by fit for agents use case + predicted speed. Click a row for VRAM breakdown.
Quant: Q4_K_MContext: 8,192VRAM: 11.1 GBHeadroom: 4.9 GBollama run gemma3:1b754tok/sE
ollama run gemma3:1bQuant: Q8_0Context: 8,192VRAM: 11.6 GBHeadroom: 4.4 GBollama run llama3.2:1b428tok/sE
ollama run llama3.2:1bQuant: Q4_K_MContext: 2,048VRAM: 9.9 GBHeadroom: 6.1 GBollama run hermes3:8b94tok/sE
ollama run hermes3:8bQuant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GBollama run mistral:7b108tok/sE
ollama run mistral:7bQuant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GBollama run llama3.1:8b94tok/sE
ollama run llama3.1:8bQuant: Q4_K_MContext: 2,048VRAM: 9.9 GBHeadroom: 6.1 GB94tok/sE
Quant: Q4_K_MContext: 2,048VRAM: 9.9 GBHeadroom: 6.1 GBollama run qwen3:8b94tok/sE
ollama run qwen3:8bQuant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GBollama run deepseek-r1:7b108tok/sE
ollama run deepseek-r1:7bQuant: Q4_K_MContext: 2,048VRAM: 9.2 GBHeadroom: 6.8 GBollama run codegemma:7b108tok/sE
ollama run codegemma:7bQuant: Q4_K_MContext: 2,048VRAM: 10.7 GBHeadroom: 5.3 GBollama run gemma2:9b84tok/sE
ollama run gemma2:9bRuns with tradeoffs35 models
Tight VRAM, partial CPU offload, or context-limited.
Quant: Q8_0Context: 8,192VRAM: 13.2 GBHeadroom: 2.8 GB- • Tight VRAM fit — only 2.8 GB headroom left for context growth
ollama run gemma4:e2b214tok/sE
- • Tight VRAM fit — only 2.8 GB headroom left for context growth
ollama run gemma4:e2bQuant: Q4_K_MContext: 8,192VRAM: 14.5 GBHeadroom: 1.5 GB- • Tight VRAM fit — only 1.5 GB headroom left for context growth
ollama run qwen3:4b188tok/sE
- • Tight VRAM fit — only 1.5 GB headroom left for context growth
ollama run qwen3:4bQuant: Q4_K_MContext: 8,192VRAM: 14.3 GBHeadroom: 1.7 GB- • Tight VRAM fit — only 1.7 GB headroom left for context growth
ollama run phi3.5:3.8b198tok/sE
- • Tight VRAM fit — only 1.7 GB headroom left for context growth
ollama run phi3.5:3.8bQuant: Q4_K_MContext: 8,192VRAM: 14.5 GBHeadroom: 1.5 GB- • Tight VRAM fit — only 1.5 GB headroom left for context growth
ollama run gemma4:e4b188tok/sE
- • Tight VRAM fit — only 1.5 GB headroom left for context growth
ollama run gemma4:e4bQuant: Q4_K_MContext: 8,192VRAM: 14.5 GBHeadroom: 1.5 GB- • Tight VRAM fit — only 1.5 GB headroom left for context growth
ollama run gemma3:4b188tok/sE
- • Tight VRAM fit — only 1.5 GB headroom left for context growth
ollama run gemma3:4bQuant: Q4_K_MContext: 8,192VRAM: 14.8 GBHeadroom: 1.2 GB- • Tight VRAM fit — only 1.2 GB headroom left for context growth
179tok/sE
- • Tight VRAM fit — only 1.2 GB headroom left for context growth
Quant: Q5_K_MContext: 8,192VRAM: 15.5 GBHeadroom: 0.5 GB- • Tight VRAM fit — only 0.5 GB headroom left for context growth
ollama run qwen2.5:7b95tok/sE
- • Tight VRAM fit — only 0.5 GB headroom left for context growth
ollama run qwen2.5:7bQuant: Q8_0Context: 8,192VRAM: 14.8 GBHeadroom: 1.2 GB- • Tight VRAM fit — only 1.2 GB headroom left for context growth
ollama run llama3.2:3b143tok/sE
- • Tight VRAM fit — only 1.2 GB headroom left for context growth
ollama run llama3.2:3bWhat 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: 37 new tradeoff
- • Qwen 3 30B-A3B
- • Qwen 2.5 Coder 32B Instruct
- • Llama 3.3 70B Instruct
- • Qwen 3 32B
Upgrade to NVIDIA GeForce RTX 3090
~$899
24 GB VRAM (vs your 16 GB) plus a bandwidth jump from ~? GB/s to ~? GB/s.
Unlocks: 14 new comfortable
- • Gemma 4 E2B (Effective 2B)
- • Llama 3.2 3B Instruct
- • Phi-3.5 Vision
- • Phi-3.5 Mini Instruct
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: 24 new comfortable
- • Gemma 4 E2B (Effective 2B)
- • Llama 3.2 3B Instruct
- • Phi-3.5 Vision
- • Phi-3.5 Mini Instruct
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Won't runtop 5 popular models
Need more memory than you have. Shown for orientation.
Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.
Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.
Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.
Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.
Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.
How to read these numbers
Want a specific benchmark we don't have? Email benchmarks@runlocalai.co and we'll prioritize it.