What can NVIDIA GeForce RTX 3090 run for long context?
Build: NVIDIA GeForce RTX 3090 + — + 32 GB RAM (windows)
Runs comfortably85 models
Ranked by fit for long context use case + predicted speed. Click a row for VRAM breakdown.
Quant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 15.6 GBollama run gemma4:e4b145tok/sEstimated
ollama run gemma4:e4bQuant: Q8_0Context: 8,192VRAM: 8.0 GBHeadroom: 16.0 GBollama run phi3.5:3.8b153tok/sEstimated
ollama run phi3.5:3.8bQuant: Q4_K_MContext: 8,192VRAM: 10.4 GBHeadroom: 13.6 GB128tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 14.3 GB146tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 14.3 GB146tok/sEstimated
Quant: Q4_K_MContext: 8,192VRAM: 11.1 GBHeadroom: 12.9 GB128tok/sEstimated
Quant: Q8_0Context: 8,192VRAM: 10.8 GBHeadroom: 13.2 GBollama run qwen2.5:7b83tok/sEstimated
ollama run qwen2.5:7bQuant: Q5_K_MContext: 8,192VRAM: 16.9 GBHeadroom: 7.1 GBollama run mistral-nemo:12b75tok/sEstimated
ollama run mistral-nemo:12bQuant: Q4_K_MContext: 8,192VRAM: 17.6 GBHeadroom: 6.4 GBollama run qwen3:14b73tok/sEstimated
ollama run qwen3:14bQuant: Q8_0Context: 8,192VRAM: 14.4 GBHeadroom: 9.6 GBollama run qwen3:8b73tok/sEstimated
ollama run qwen3:8bQuant: Q5_K_MContext: 8,192VRAM: 19.8 GBHeadroom: 4.2 GBollama run qwen2.5:14b64tok/sEstimated
ollama run qwen2.5:14bQuant: Q4_K_MContext: 8,192VRAM: 9.9 GBHeadroom: 14.1 GB146tok/sEstimated
Runs with tradeoffs23 models
Tight VRAM, partial CPU offload, or context-limited.
Quant: Q4_K_MContext: 2,048VRAM: 39.8 GBHeadroom: 3.4 GB- • Partial CPU offload: ~40% of layers run on CPU
85tok/sEstimated
- • Partial CPU offload: ~40% of layers run on CPU
Quant: Q4_K_MContext: 2,048VRAM: 20.1 GBHeadroom: 3.9 GB- • Tight VRAM fit — only 3.9 GB headroom left for context growth
39tok/sEstimated
- • Tight VRAM fit — only 3.9 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 gemma3:27b38tok/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 qwen3.8:27b38tok/sEstimated
- • Tight VRAM fit — only 1.5 GB headroom left for context growth
ollama run qwen3.8:27bQuant: Q4_K_MContext: 8,192VRAM: 35.7 GBHeadroom: 7.5 GB- • Partial CPU offload: ~33% of layers run on CPU
ollama run qwen3:30b34tok/sEstimated
- • Partial CPU offload: ~33% of layers run on CPU
ollama run qwen3:30bQuant: Q4_K_MContext: 8,192VRAM: 36.2 GBHeadroom: 7.0 GB- • Partial CPU offload: ~34% of layers run on CPU
ollama run gemma4:31b33tok/sEstimated
- • Partial CPU offload: ~34% of layers run on CPU
ollama run gemma4:31bQuant: Q4_K_MContext: 8,192VRAM: 37.8 GBHeadroom: 5.4 GB- • Partial CPU offload: ~36% of layers run on CPU
ollama run qwen3:32b32tok/sEstimated
- • Partial CPU offload: ~36% of layers run on CPU
ollama run qwen3:32bQuant: Q4_K_MContext: 8,192VRAM: 37.8 GBHeadroom: 5.4 GB- • Partial CPU offload: ~36% of layers run on CPU
ollama run qwen2.5:32b32tok/sEstimated
- • Partial CPU offload: ~36% of layers run on CPU
ollama run qwen2.5:32bWhat 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, 35 new tradeoff
- • Qwen 3 0.6B
- • Qwen 3 1.7B
- • Gemma 3 270M
- • SmolLM2 135M Instruct
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: 45 new comfortable
- • Qwen 3 0.6B
- • Qwen 3 30B-A3B
- • Qwen 2.5 Coder 32B Instruct
- • Qwen 3 32B
Add a second NVIDIA GeForce RTX 3090
Launch MSRP $1,499 (2020). Now sells well below its launch price on the used market. 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: 51 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 (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.
Not enough VRAM for these models? Rent a GPU by the hour
The models above need more memory than 24 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
billed per GPU-hourHourly GPU pods, in community-cloud and secure-cloud tiers.
The two tiers run on different hosts and terms. Check which tier a pod is on before a long job.
Rent on RunPodVast.ai
billed per GPU-hourMarketplace of host-listed GPUs; price and reliability vary by host.
Host quality varies. Filter for verified hosts and check the host's reliability before a long run.
Rent on Vast.aiReferral links: RunLocalAI may earn a commission or account credits if you sign up and rent, at no extra cost to you. Hourly rates and which GPUs are listed change all the time, so check the provider's page before you start. Editorial opinions are independent of the referral relationship; see how we make money.
How to read these numbers
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