What can NVIDIA GeForce RTX 4090 run for creative?

Build: RTX 4090 + Ryzen 9 7950X + 64GB DDR5

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

Runs comfortably
100 models

Ranked by fit for creative use case + predicted speed. Click a row for VRAM breakdown.

#1Hermes 3 Llama 3.1 8B
8B
hermes
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 14.7 GBHeadroom: 9.3 GBTTFT: fast
ollama run hermes3:8b
77
tok/s
Estimated
Weights
8.50 GB
KV cache
4.00 GB
Activations
0.43 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~248 ms (fast)
Model details →
#2Gemma 2 9B Instruct
9B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 16.6 GBHeadroom: 7.4 GBTTFT: fast
ollama run gemma2:9b
69
tok/s
Estimated
Weights
9.80 GB
KV cache
4.50 GB
Activations
0.50 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~279 ms (fast)
Model details →
#3Dolphin 3.0 Llama 3.2 3B
3B
dolphin
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 5.2 GBHeadroom: 18.8 GBTTFT: instant
362
tok/s
Estimated
Weights
1.80 GB
KV cache
1.50 GB
Activations
0.10 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~93 ms (instant)
Model details →
#4Hermes 3 Llama 3.2 3B
3B
hermes
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 5.2 GBHeadroom: 18.8 GBTTFT: instant
362
tok/s
Estimated
Weights
1.80 GB
KV cache
1.50 GB
Activations
0.10 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~93 ms (instant)
Model details →
#5Gemma 4 E4B (Effective 4B)
4B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 15.6 GBTTFT: fast
ollama run gemma4:e4b
154
tok/s
Estimated
Weights
4.40 GB
KV cache
2.00 GB
Activations
0.23 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~124 ms (fast)
Model details →
#6Gemma 2 2B Instruct
2B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 4.0 GBHeadroom: 20.0 GBTTFT: instant
543
tok/s
Estimated
Weights
1.10 GB
KV cache
1.00 GB
Activations
0.06 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~62 ms (instant)
Model details →
#7Turkish Gemma 9B T1
9B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 11.6 GBHeadroom: 12.4 GBTTFT: fast
121
tok/s
Estimated
Weights
5.00 GB
KV cache
4.50 GB
Activations
0.26 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~279 ms (fast)
Model details →
#8Gemma 3 4B
4B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 15.6 GBTTFT: fast
ollama run gemma3:4b
154
tok/s
Estimated
Weights
4.40 GB
KV cache
2.00 GB
Activations
0.23 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~124 ms (fast)
Model details →
#9Gemma 4 E2B (Effective 2B)
2B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 5.1 GBHeadroom: 18.9 GBTTFT: instant
ollama run gemma4:e2b
308
tok/s
Estimated
Weights
2.20 GB
KV cache
1.00 GB
Activations
0.12 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~62 ms (instant)
Model details →
#10CodeGemma 7B
7B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 14.3 GBTTFT: fast
ollama run codegemma:7b
155
tok/s
Estimated
Weights
4.20 GB
KV cache
3.50 GB
Activations
0.22 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~217 ms (fast)
Model details →
#11YTU Turkish Gemma 9B v0.1
9.2B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 12.5 GBHeadroom: 11.5 GBTTFT: fast
ollama run alibayram/turkish-gemma-9b-v0.1:latest
118
tok/s
Estimated
Weights
5.80 GB
KV cache
4.60 GB
Activations
0.30 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~285 ms (fast)
Model details →
#12PaliGemma 2 3B
3B
gemma
Commercial OK
Quant: BF16Context: 8,192VRAM: 9.6 GBHeadroom: 14.4 GBTTFT: instant
109
tok/s
Estimated
Weights
6.00 GB
KV cache
1.50 GB
Activations
0.31 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~93 ms (instant)
Model details →

Runs with tradeoffs
35 models

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

Gemma 4 Turkish 26B (4B active)
26B
gemma
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 20.1 GBHeadroom: 3.9 GBTTFT: noticeable
  • • Tight VRAM fit — only 3.9 GB headroom left for context growth
42
tok/s
Estimated
Weights
14.30 GB
KV cache
3.25 GB
Activations
0.72 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~806 ms (noticeable)
Model details →
Gemma 3 27B
27B
gemma
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 22.0 GBHeadroom: 2.0 GBTTFT: noticeable
  • • Tight VRAM fit — only 2.0 GB headroom left for context growth
ollama run gemma3:27b
40
tok/s
Estimated
Weights
16.00 GB
KV cache
3.38 GB
Activations
0.80 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~837 ms (noticeable)
Model details →
MedGemma 27B
27B
gemma
Quant: Q4_K_MContext: 2,048VRAM: 22.0 GBHeadroom: 2.0 GBTTFT: noticeable
  • • Tight VRAM fit — only 2.0 GB headroom left for context growth
40
tok/s
Estimated
Weights
16.00 GB
KV cache
3.38 GB
Activations
0.80 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~837 ms (noticeable)
Model details →
Llama 3.2 11B Vision Instruct
11B
llama
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 20.4 GBHeadroom: 3.6 GBTTFT: fast
  • • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run llama3.2-vision:11b
56
tok/s
Estimated
Weights
12.50 GB
KV cache
5.50 GB
Activations
0.63 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~341 ms (fast)
Model details →
Hermes 3 Llama 3.1 70B
70B
hermes
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 52.6 GBHeadroom: 9.8 GBTTFT: slow
  • • Partial CPU offload: ~54% of layers run on CPU
  • • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run hermes3:70b
1
tok/s
Estimated
Weights
40.00 GB
KV cache
8.75 GB
Activations
2.00 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~2169 ms (slow)
Model details →
InternVL 2.5 26B
26B
other
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 21.9 GBHeadroom: 2.1 GBTTFT: noticeable
  • • Tight VRAM fit — only 2.1 GB headroom left for context growth
42
tok/s
Estimated
Weights
16.00 GB
KV cache
3.25 GB
Activations
0.80 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~806 ms (noticeable)
Model details →
Qwen3.8 27B
27B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 22.5 GBHeadroom: 1.5 GBTTFT: noticeable
  • • Tight VRAM fit — only 1.5 GB headroom left for context growth
ollama run qwen3.8:27b
40
tok/s
Estimated
Weights
16.50 GB
KV cache
3.38 GB
Activations
0.83 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~837 ms (noticeable)
Model details →
Omni 31B Turkish Reasoning
31B
other
Quant: Q4_K_MContext: 2,048VRAM: 23.6 GBHeadroom: 0.4 GBTTFT: noticeable
  • • Tight VRAM fit — only 0.4 GB headroom left for context growth
35
tok/s
Estimated
Weights
17.10 GB
KV cache
3.88 GB
Activations
0.86 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~961 ms (noticeable)
Model details →

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: 13 new comfortable, 39 new tradeoff

  • • Qwen 3 0.6B
  • • Gemma 3 270M
  • • SmolLM2 135M Instruct
  • • TinyLlama 1.1B Chat v1.0
Shop this upgrade↗

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 ~1008 GB/s to ~1792 GB/s.

Unlocks: 30 new comfortable

  • • Qwen 3 0.6B
  • • Qwen 3 30B-A3B
  • • Qwen 2.5 Coder 32B Instruct
  • • Qwen 3 32B
Shop this upgrade↗

Add a second NVIDIA GeForce RTX 4090

Launch MSRP $1,599 (2022). 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: 36 new comfortable

  • • Qwen 3 0.6B
  • • Qwen 3 30B-A3B
  • • Qwen 2.5 Coder 32B Instruct
  • • Qwen 3 32B
Shop this upgrade↗

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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 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.

—
DeepSeek V4 Flash (284B MoE)
284B
deepseek
Commercial OK

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

—
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Not enough VRAM for these models? Rent a GPU by the hour

The models above need more memory than 24 GB VRAM + 64 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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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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