What can NVIDIA GeForce RTX 5080 run for long context?
Build: NVIDIA GeForce RTX 5080 + — + 32 GB RAM (windows)
Runs comfortably60 models
Ranked by fit for long context use case + predicted speed. Click a row for VRAM breakdown.
Quant: Q4_K_MContext: 8,192VRAM: 10.8 GBHeadroom: 5.2 GBTTFT: fastollama run qwen3:8b129tok/sEstimated
ollama run qwen3:8bQuant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 7.6 GBTTFT: fastollama run gemma4:e4b147tok/sEstimated
ollama run gemma4:e4bQuant: Q8_0Context: 8,192VRAM: 8.0 GBHeadroom: 8.0 GBTTFT: fastollama run phi3.5:3.8b155tok/sEstimated
ollama run phi3.5:3.8bQuant: Q4_K_MContext: 8,192VRAM: 10.4 GBHeadroom: 5.6 GBTTFT: fast129tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 6.3 GBTTFT: fast148tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 6.3 GBTTFT: fast148tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 11.1 GBHeadroom: 4.9 GBTTFT: fast129tok/sEstimated
Quant: Q8_0Context: 8,192VRAM: 10.8 GBHeadroom: 5.2 GBTTFT: fastollama run qwen2.5:7b84tok/sEstimated
ollama run qwen2.5:7bQuant: Q8_0Context: 8,192VRAM: 11.8 GBHeadroom: 4.2 GBTTFT: fastollama run llama3.1:8b73tok/sEstimated
ollama run llama3.1:8bQuant: Q4_K_MContext: 8,192VRAM: 9.9 GBHeadroom: 6.1 GBTTFT: fast148tok/sEstimated
Quant: Q5_K_MContext: 8,192VRAM: 10.8 GBHeadroom: 5.2 GBTTFT: fastollama run koezgen/malhajar-mistral-tur7b:latest126tok/sEstimated
ollama run koezgen/malhajar-mistral-tur7b:latestQuant: Q8_0Context: 8,192VRAM: 6.9 GBHeadroom: 9.1 GBTTFT: fastollama run llama3.2:3b196tok/sEstimated
ollama run llama3.2:3bRuns with tradeoffs46 models
Tight VRAM, partial CPU offload, or context-limited.
Quant: Q4_K_MContext: 8,192VRAM: 15.7 GBHeadroom: 0.3 GBTTFT: noticeable- • Tight VRAM fit — only 0.3 GB headroom left for context growth
ollama run mistral-nemo:12b86tok/sEstimated
- • Tight VRAM fit — only 0.3 GB headroom left for context growth
ollama run mistral-nemo:12bQuant: Q4_K_MContext: 2,048VRAM: 12.4 GBHeadroom: 3.6 GBTTFT: noticeable- • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run qwen3:14b74tok/sEstimated
- • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run qwen3:14bQuant: Q4_K_MContext: 2,048VRAM: 12.9 GBHeadroom: 3.1 GBTTFT: noticeable- • Tight VRAM fit — only 3.1 GB headroom left for context growth
ollama run qwen2.5:14b74tok/sEstimated
- • Tight VRAM fit — only 3.1 GB headroom left for context growth
ollama run qwen2.5:14bQuant: Q4_K_MContext: 2,048VRAM: 12.8 GBHeadroom: 3.2 GBTTFT: fast- • Tight VRAM fit — only 3.2 GB headroom left for context growth
431tok/sEstimated
- • Tight VRAM fit — only 3.2 GB headroom left for context growth
Quant: Q4_K_MContext: 8,192VRAM: 29.8 GBHeadroom: 5.4 GBTTFT: noticeable- • Partial CPU offload: ~46% of layers run on CPU
40tok/sEstimated
- • Partial CPU offload: ~46% of layers run on CPU
Quant: Q4_K_MContext: 8,192VRAM: 32.1 GBHeadroom: 3.1 GBTTFT: noticeable- • Partial CPU offload: ~50% of layers run on CPU
ollama run gemma3:27b38tok/sEstimated
- • Partial CPU offload: ~50% of layers run on CPU
ollama run gemma3:27bQuant: Q4_K_MContext: 8,192VRAM: 32.6 GBHeadroom: 2.6 GBTTFT: noticeable- • Partial CPU offload: ~51% of layers run on CPU
ollama run qwen3.8:27b38tok/sEstimated
- • Partial CPU offload: ~51% of layers run on CPU
ollama run qwen3.8:27bQuant: Q4_K_MContext: 2,048VRAM: 24.5 GBHeadroom: 10.7 GBTTFT: noticeable- • Partial CPU offload: ~35% of layers run on CPU
ollama run qwen3:30b34tok/sEstimated
- • Partial CPU offload: ~35% of layers run on CPU
ollama run qwen3:30bWhat 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: 28 new comfortable, 57 new tradeoff
- • Qwen 3 0.6B
- • Qwen 3 1.7B
- • Gemma 3 270M
- • SmolLM2 135M 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: 47 new comfortable
- • Qwen 3 0.6B
- • Qwen 3 14B
- • Qwen 3 1.7B
- • Gemma 3 270M
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: 68 new comfortable
- • Qwen 3 0.6B
- • Qwen 3 30B-A3B
- • Qwen 2.5 Coder 32B Instruct
- • Qwen 3 32B
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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.
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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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How to read these numbers
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