What can NVIDIA L4 run for vision?
Build: NVIDIA L4 + — + 32 GB RAM (windows)
Runs comfortably20 models
Ranked by fit for vision use case + predicted speed. Click a row for VRAM breakdown.
Quant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 15.6 GBollama run gemma4:e4b229tok/sEstimated
ollama run gemma4:e4bQuant: Q4_K_MContext: 8,192VRAM: 15.5 GBHeadroom: 8.5 GBollama run gemma3:12b135tok/sEstimated
ollama run gemma3:12bQuant: Q4_K_MContext: 8,192VRAM: 15.2 GBHeadroom: 8.8 GBollama run pixtral:12b135tok/sEstimated
ollama run pixtral:12bQuant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 15.6 GBollama run gemma3:4b229tok/sEstimated
ollama run gemma3:4bQuant: Q8_0Context: 8,192VRAM: 5.1 GBHeadroom: 18.9 GBollama run gemma4:e2b459tok/sEstimated
ollama run gemma4:e2bQuant: Q4_K_MContext: 8,192VRAM: 6.5 GBHeadroom: 17.5 GB385tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 11.1 GBHeadroom: 12.9 GB202tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 9.9 GBHeadroom: 14.1 GB231tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 11.1 GBHeadroom: 12.9 GB202tok/sEstimated
Quant: Q4_K_MContext: 2,048VRAM: 3.3 GBHeadroom: 20.7 GB850tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 10.1 GBHeadroom: 13.9 GB231tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 10.0 GBHeadroom: 14.0 GB231tok/sEstimated
Runs with tradeoffs8 models
Tight VRAM, partial CPU offload, or context-limited.
Quant: Q8_0Context: 8,192VRAM: 20.4 GBHeadroom: 3.6 GB- • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run llama3.2-vision:11b83tok/sEstimated
- • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run llama3.2-vision:11bQuant: Q4_K_MContext: 2,048VRAM: 21.9 GBHeadroom: 2.1 GB- • Tight VRAM fit — only 2.1 GB headroom left for context growth
ollama run gemma4:26b-moe62tok/sEstimated
- • Tight VRAM fit — only 2.1 GB headroom left for context growth
ollama run gemma4:26b-moeQuant: Q4_K_MContext: 2,048VRAM: 21.9 GBHeadroom: 2.1 GB- • Tight VRAM fit — only 2.1 GB headroom left for context growth
62tok/sEstimated
- • Tight VRAM fit — only 2.1 GB headroom left for context growth
Quant: Q4_K_MContext: 2,048VRAM: 22.0 GBHeadroom: 2.0 GB- • Tight VRAM fit — only 2.0 GB headroom left for context growth
ollama run gemma3:27b60tok/sEstimated
- • Tight VRAM fit — only 2.0 GB headroom left for context growth
ollama run gemma3:27bQuant: Q4_K_MContext: 2,048VRAM: 22.5 GBHeadroom: 1.5 GB- • Tight VRAM fit — only 1.5 GB headroom left for context growth
ollama run qwen3.8:27b60tok/sEstimated
- • Tight VRAM fit — only 1.5 GB headroom left for context growth
ollama run qwen3.8:27bQuant: Q4_K_MContext: 2,048VRAM: 22.0 GBHeadroom: 2.0 GB- • Tight VRAM fit — only 2.0 GB headroom left for context growth
60tok/sEstimated
- • Tight VRAM fit — only 2.0 GB headroom left for context growth
Quant: Q4_K_MContext: 8,192VRAM: 36.2 GBHeadroom: 7.0 GB- • Partial CPU offload: ~34% of layers run on CPU
ollama run gemma4:31b52tok/sEstimated
- • Partial CPU offload: ~34% of layers run on CPU
ollama run gemma4:31bQuant: BF16Context: 8,192VRAM: 27.8 GBHeadroom: 15.4 GB- • Partial CPU offload: ~14% of layers run on CPU
49tok/sEstimated
- • Partial CPU offload: ~14% of layers run on CPU
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: 110 new comfortable, 48 new tradeoff
- • Qwen 3 0.6B
- • Llama 3.1 8B Instruct
- • Qwen 3 8B
- • Qwen 3 1.7B
Upgrade to NVIDIA GeForce RTX 5090
Launch MSRP $1,999 (2025). Check the current price.
32 GB VRAM (vs your 24 GB) plus a bandwidth jump from ~? GB/s to ~1792 GB/s.
Unlocks: 127 new comfortable
- • Qwen 3 0.6B
- • Llama 3.1 8B Instruct
- • Qwen 3 30B-A3B
- • Qwen 2.5 Coder 32B Instruct
Add a second NVIDIA L4
Launch MSRP $2,500 (2023). 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: 142 new comfortable
- • Qwen 3 0.6B
- • Llama 3.1 8B Instruct
- • Qwen 3 30B-A3B
- • Qwen 2.5 Coder 32B 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 (24 GB) + 60% of system RAM (19 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
—
Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (19 GB) combined.
How to read these numbers
Want a specific benchmark we don't have? Email Contact support and we'll prioritize it.