What can NVIDIA GeForce RTX 5080 run for vision?
Build: RTX 5080 + Ryzen 9 9950X + 32GB DDR5
Runs comfortably13 models
Ranked by fit for vision use case + predicted speed. Click a row for VRAM breakdown.
Quant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 7.6 GBTTFT: fastollama run gemma4:e4b147tok/sEstimated
ollama run gemma4:e4bQuant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 7.6 GBTTFT: fastollama run gemma3:4b147tok/sEstimated
ollama run gemma3:4bQuant: Q8_0Context: 8,192VRAM: 5.1 GBHeadroom: 10.9 GBTTFT: instantollama run gemma4:e2b294tok/sEstimated
ollama run gemma4:e2bQuant: Q4_K_MContext: 8,192VRAM: 6.5 GBHeadroom: 9.5 GBTTFT: fast246tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 11.1 GBHeadroom: 4.9 GBTTFT: fast129tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 9.9 GBHeadroom: 6.1 GBTTFT: fast148tok/sEstimated
Quant: Q4_K_MContext: 2,048VRAM: 3.3 GBHeadroom: 12.7 GBTTFT: instant544tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 10.1 GBHeadroom: 5.9 GBTTFT: fast148tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 10.0 GBHeadroom: 6.0 GBTTFT: fast148tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 10.0 GBHeadroom: 6.0 GBTTFT: fast148tok/sEstimated
Quant: Q4_K_MContext: 4,096VRAM: 9.3 GBHeadroom: 6.7 GBTTFT: fast129tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 5.4 GBHeadroom: 10.6 GBTTFT: fast345tok/sEstimated
Runs with tradeoffs12 models
Tight VRAM, partial CPU offload, or context-limited.
Quant: Q4_K_MContext: 8,192VRAM: 15.6 GBHeadroom: 0.4 GBTTFT: noticeable- • Tight VRAM fit — only 0.4 GB headroom left for context growth
ollama run llama3.2-vision:11b94tok/sEstimated
- • Tight VRAM fit — only 0.4 GB headroom left for context growth
ollama run llama3.2-vision:11bQuant: Q4_K_MContext: 8,192VRAM: 14.1 GBHeadroom: 1.9 GBTTFT: noticeable- • Tight VRAM fit — only 1.9 GB headroom left for context growth
94tok/sEstimated
- • Tight VRAM fit — only 1.9 GB headroom left for context growth
Quant: Q4_K_MContext: 8,192VRAM: 15.5 GBHeadroom: 0.5 GBTTFT: noticeable- • Tight VRAM fit — only 0.5 GB headroom left for context growth
ollama run gemma3:12b86tok/sEstimated
- • Tight VRAM fit — only 0.5 GB headroom left for context growth
ollama run gemma3:12bQuant: Q4_K_MContext: 8,192VRAM: 15.2 GBHeadroom: 0.8 GBTTFT: noticeable- • Tight VRAM fit — only 0.8 GB headroom left for context growth
ollama run pixtral:12b86tok/sEstimated
- • Tight VRAM fit — only 0.8 GB headroom left for context growth
ollama run pixtral:12bQuant: Q4_K_MContext: 2,048VRAM: 12.5 GBHeadroom: 3.5 GBTTFT: noticeable- • Tight VRAM fit — only 3.5 GB headroom left for context growth
74tok/sEstimated
- • Tight VRAM fit — only 3.5 GB headroom left for context growth
Quant: GGUF_UNKNOWNContext: 8,192VRAM: 12.7 GBHeadroom: 3.3 GBTTFT: noticeable- • Tight VRAM fit — only 3.3 GB headroom left for context growth
ollama run alibayram/Trendyol-LLM-Asure-12B:latest53tok/sEstimated
- • Tight VRAM fit — only 3.3 GB headroom left for context growth
ollama run alibayram/Trendyol-LLM-Asure-12B:latestQuant: Q4_K_MContext: 2,048VRAM: 24.6 GBHeadroom: 10.6 GBTTFT: noticeable- • Partial CPU offload: ~35% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run gemma4:31b5tok/sEstimated
- • Partial CPU offload: ~35% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run gemma4:31bQuant: Q4_K_MContext: 8,192VRAM: 31.6 GBHeadroom: 3.6 GBTTFT: noticeable- • Partial CPU offload: ~49% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
4tok/sEstimated
- • Partial CPU offload: ~49% of layers run on CPU
- • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
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: 75 new comfortable, 57 new tradeoff
- • Qwen 3 0.6B
- • Llama 3.1 8B Instruct
- • Qwen 3 8B
- • Llama 3.2 3B Instruct
Upgrade to NVIDIA RTX 2080 Ti 22GB (China-mod)
Check the current price
22 GB VRAM (vs your 16 GB) plus a bandwidth jump from ~960 GB/s to ~616 GB/s.
Unlocks: 94 new comfortable
- • Qwen 3 0.6B
- • Llama 3.1 8B Instruct
- • Qwen 3 8B
- • Qwen 3 14B
Add a second NVIDIA GeForce RTX 5080
Launch MSRP $999 (2025). 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: 115 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 (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.
Not enough VRAM for these models? Rent a GPU by the hour
The models above need more memory than 16 GB VRAM + 32 GB system RAM. You can rent a GPU with enough VRAM by the hour to run one of them, or to test it before you buy hardware. For daily use, owning a card usually costs less over time.
RunPod
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The two tiers run on different hosts and terms. Check which tier a pod is on before a long job.
Rent on RunPodVast.ai
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Host quality varies. Filter for verified hosts and check the host's reliability before a long run.
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How to read these numbers
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