What can NVIDIA GeForce RTX 3090 run?

Build: RTX 3090 + Ryzen 9 5950X + 64GB DDR4 (used market)

Memory: 24 GB VRAM + 64 GB system RAM
Runner: llama.cpp / Ollama (CUDA)

Runs comfortably
184 models

Full-VRAM resident, with room for context. No compromises.

#1Qwen 3 0.6B
0.6B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 2.4 GBHeadroom: 21.6 GB
1680
tok/s
Estimated
Weights
0.30 GB
KV cache
0.30 GB
Activations
0.02 GB
Runtime
1.80 GB
#2Llama 3.1 8B Instruct
8B
llama
Commercial OK
Quant: FP16Context: 8,192VRAM: 19.8 GBHeadroom: 4.2 GB
ollama run llama3.1:8b
38
tok/s
Estimated
Weights
16.10 GB
KV cache
1.07 GB
Activations
0.81 GB
Runtime
1.80 GB
#3Qwen 3 8B
8B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 14.4 GBHeadroom: 9.6 GB
ollama run qwen3:8b
72
tok/s
Estimated
Weights
8.20 GB
KV cache
4.00 GB
Activations
0.42 GB
Runtime
1.80 GB
#4Gemma 4 12B
12B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 15.8 GBHeadroom: 8.2 GB
ollama run gemma4:12b
84
tok/s
Estimated
Weights
7.60 GB
KV cache
6.00 GB
Activations
0.39 GB
Runtime
1.80 GB
#5Qwen3.5 9B
9B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 13.2 GBHeadroom: 10.8 GB
ollama run qwen3.5:9b
112
tok/s
Estimated
Weights
6.60 GB
KV cache
4.50 GB
Activations
0.34 GB
Runtime
1.80 GB
#6GPT-OSS 20B
20.9B
other
Commercial OK
Quant: MXFP4Context: 2,048VRAM: 19.1 GBHeadroom: 4.9 GB
ollama run gpt-oss:20b
29
tok/s
Estimated
Weights
14.00 GB
KV cache
2.61 GB
Activations
0.70 GB
Runtime
1.80 GB
#7Llama 3.2 3B Instruct
3B
llama
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 6.9 GBHeadroom: 17.1 GB
ollama run llama3.2:3b
191
tok/s
Estimated
Weights
3.40 GB
KV cache
1.50 GB
Activations
0.18 GB
Runtime
1.80 GB
#8Qwen 3 1.7B
1.7B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 3.6 GBHeadroom: 20.4 GB
593
tok/s
Estimated
Weights
0.90 GB
KV cache
0.85 GB
Activations
0.05 GB
Runtime
1.80 GB
#9Qwen 3 14B
14B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.6 GBHeadroom: 6.4 GB
ollama run qwen3:14b
72
tok/s
Estimated
Weights
8.40 GB
KV cache
7.00 GB
Activations
0.43 GB
Runtime
1.80 GB
#10Qwen 2.5 7B Instruct
7B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 10.8 GBHeadroom: 13.2 GB
ollama run qwen2.5:7b
82
tok/s
Estimated
Weights
8.10 GB
KV cache
0.47 GB
Activations
0.41 GB
Runtime
1.80 GB
#11Gemma 3 270M
0.27B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 2.1 GBHeadroom: 21.9 GB
3732
tok/s
Estimated
Weights
0.16 GB
KV cache
0.14 GB
Activations
0.02 GB
Runtime
1.80 GB
#12Mistral Small 3 24B
24B
mistral
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 19.5 GBHeadroom: 4.5 GB
ollama run mistral-small:24b
42
tok/s
Estimated
Weights
14.00 GB
KV cache
3.00 GB
Activations
0.70 GB
Runtime
1.80 GB

Runs with tradeoffs
73 models

Tight VRAM, partial CPU offload, or context-limited.

Qwen 3 30B-A3B
30B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 50.4 GBHeadroom: 12.0 GB
  • Partial CPU offload: ~52% of layers run on CPU
  • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run qwen3:30b
1
tok/s
Estimated
Weights
32.00 GB
KV cache
15.00 GB
Activations
1.61 GB
Runtime
1.80 GB
Qwen 2.5 Coder 32B Instruct
32B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 23.9 GBHeadroom: 0.1 GB
  • Tight VRAM fit — only 0.1 GB headroom left for context growth
ollama run qwen2.5-coder:32b
31
tok/s
Estimated
Weights
19.00 GB
KV cache
2.15 GB
Activations
0.96 GB
Runtime
1.80 GB
Qwen3.6 27B
27B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 23.0 GBHeadroom: 1.0 GB
  • Tight VRAM fit — only 1.0 GB headroom left for context growth
ollama run qwen3.6:27b
37
tok/s
Estimated
Weights
17.00 GB
KV cache
3.38 GB
Activations
0.85 GB
Runtime
1.80 GB
Llama 3.3 70B Instruct
70B
llama
Commercial OK
Quant: Q5_K_MContext: 8,192VRAM: 53.8 GBHeadroom: 8.6 GB
  • Partial CPU offload: ~55% of layers run on CPU
  • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run llama3.3:70b
1
tok/s
Estimated
Weights
47.00 GB
KV cache
2.68 GB
Activations
2.36 GB
Runtime
1.80 GB
Qwen 3 32B
32B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 53.5 GBHeadroom: 8.9 GB
  • Partial CPU offload: ~55% of layers run on CPU
  • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run qwen3:32b
1
tok/s
Estimated
Weights
34.00 GB
KV cache
16.00 GB
Activations
1.71 GB
Runtime
1.80 GB
Gemma 4 31B Dense
31B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 52.0 GBHeadroom: 10.4 GB
  • Partial CPU offload: ~54% of layers run on CPU
  • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run gemma4:31b
1
tok/s
Estimated
Weights
33.00 GB
KV cache
15.50 GB
Activations
1.66 GB
Runtime
1.80 GB
Qwen3 Coder 30B-A3B
30B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 36.8 GBHeadroom: 25.6 GB
  • Partial CPU offload: ~35% of layers run on CPU
  • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run qwen3-coder:30b
3
tok/s
Estimated
Weights
19.00 GB
KV cache
15.00 GB
Activations
0.96 GB
Runtime
1.80 GB
DeepSeek R1 Distill Llama 70B
70B
deepseek
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 52.6 GBHeadroom: 9.8 GB
  • Partial CPU offload: ~54% of layers run on CPU
  • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run deepseek-r1:70b
1
tok/s
Estimated
Weights
40.00 GB
KV cache
8.75 GB
Activations
2.00 GB
Runtime
1.80 GB

What 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: 79 new tradeoff

  • Qwen 3 30B-A3B
  • Qwen 2.5 Coder 32B Instruct
  • Qwen3.6 27B
  • Llama 3.3 70B Instruct

Upgrade to NVIDIA RTX PRO 4500 Blackwell

see current pricing

32 GB VRAM (vs your 24 GB) plus a bandwidth jump from ~936 GB/s to ~896 GB/s.

Unlocks: 39 new comfortable

  • Qwen 3 30B-A3B
  • Qwen 2.5 Coder 32B Instruct
  • Qwen3.6 27B
  • Qwen 3 32B

Add a second NVIDIA GeForce RTX 3090

~$899

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: 52 new comfortable

  • Qwen 3 30B-A3B
  • Qwen 2.5 Coder 32B Instruct
  • Qwen3.6 27B
  • Qwen 3 32B

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Won't run
top 5 popular models

Need more memory than you have. Shown for orientation.

DeepSeek V4 Pro (1.6T MoE)
1600B
deepseek
Commercial OK

Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (38 GB) combined.

Qwen 3.5 235B-A17B (MoE)
397B
qwen
Commercial OK

Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (38 GB) combined.

Qwen 3 235B-A22B
235B
qwen
Commercial OK

Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (38 GB) combined.

DeepSeek R1 (671B reasoning)
671B
deepseek
Commercial OK

Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (38 GB) combined.

Llama 4 Scout
109B
llama
Commercial OK

Even with CPU offload, needs more memory than your VRAM (24 GB) + 60% of system RAM (38 GB) combined.

How to read these numbers

Measured here
Measured here - RunLocalAI ran this exact combo on owner hardware with public evidence.

Source-backed
Source-backed / community - a reproduced public source supports the speed, but it is not labeled as owner-measured.

Extrapolated
Extrapolated - predicted from a measured benchmark on similar-bandwidth hardware.

Estimated
Estimated - formula based on VRAM bandwidth and model architecture; not a benchmark row.

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