What can NVIDIA GeForce RTX 4080 Super run for vision?

Build: RTX 4080 Super + i7-14700K + 32GB DDR5

Memory: 16 GB VRAM + 32 GB system RAM
Runner: llama.cpp / Ollama (CUDA)
AnyChatCodingAgentsReasoningVisionLong contextCreative

Runs comfortably
14 models

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

#1Gemma 4 E2B (Effective 2B)
2B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 5.1 GBHeadroom: 10.9 GBTTFT: instant
ollama run gemma4:e2b
225
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): ~98 ms (instant)
Model details →
#2Phi-3.5 Vision
4.2B
phi
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 6.5 GBHeadroom: 9.5 GBTTFT: fast
189
tok/s
Estimated
Weights
2.50 GB
KV cache
2.10 GB
Activations
0.13 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~206 ms (fast)
Model details →
#3Moondream 2
1.9B
other
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 3.3 GBHeadroom: 12.7 GBTTFT: instant
417
tok/s
Estimated
Weights
1.20 GB
KV cache
0.24 GB
Activations
0.06 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~93 ms (instant)
Model details →
#4Qwen 2.5-VL 3B
3B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 5.4 GBHeadroom: 10.6 GBTTFT: fast
264
tok/s
Estimated
Weights
2.00 GB
KV cache
1.50 GB
Activations
0.11 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~147 ms (fast)
Model details →
#5LLaVA 1.6 Mistral 7B
7B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 10.0 GBHeadroom: 6.0 GBTTFT: fast
113
tok/s
Estimated
Weights
4.50 GB
KV cache
3.50 GB
Activations
0.23 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~343 ms (fast)
Model details →
#6LLaVA-OneVision 7B
7B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 10.0 GBHeadroom: 6.0 GBTTFT: fast
113
tok/s
Estimated
Weights
4.50 GB
KV cache
3.50 GB
Activations
0.23 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~343 ms (fast)
Model details →
#7Qwen 2-VL 7B
7B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 10.1 GBHeadroom: 5.9 GBTTFT: fast
113
tok/s
Estimated
Weights
4.60 GB
KV cache
3.50 GB
Activations
0.24 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~343 ms (fast)
Model details →
#8Qwen 2.5-VL 7B
7B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 9.9 GBHeadroom: 6.1 GBTTFT: fast
113
tok/s
Estimated
Weights
4.40 GB
KV cache
3.50 GB
Activations
0.23 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~343 ms (fast)
Model details →
#9Gemma 4 E4B (Effective 4B)
4B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 7.6 GBTTFT: fast
ollama run gemma4:e4b
113
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): ~196 ms (fast)
Model details →
#10Gemma 3 4B
4B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 8.4 GBHeadroom: 7.6 GBTTFT: fast
ollama run gemma3:4b
113
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): ~196 ms (fast)
Model details →
#11MiniCPM-V 3 8B
8B
minicpm
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 11.1 GBHeadroom: 4.9 GBTTFT: fast
99
tok/s
Estimated
Weights
5.00 GB
KV cache
4.00 GB
Activations
0.26 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~392 ms (fast)
Model details →
#12MiniCPM-V 2.6 8B
8B
minicpm
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 11.1 GBHeadroom: 4.9 GBTTFT: fast
99
tok/s
Estimated
Weights
5.00 GB
KV cache
4.00 GB
Activations
0.26 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~392 ms (fast)
Model details →

Runs with tradeoffs
17 models

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

Qwen3.5 9B
9B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 13.2 GBHeadroom: 2.8 GBTTFT: fast
  • • Tight VRAM fit — only 2.8 GB headroom left for context growth
ollama run qwen3.5:9b
88
tok/s
Estimated
Weights
6.60 GB
KV cache
4.50 GB
Activations
0.34 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~441 ms (fast)
Model details →
Llama 3.2 11B Vision Instruct
11B
llama
Commercial OK
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:11b
72
tok/s
Estimated
Weights
7.90 GB
KV cache
5.50 GB
Activations
0.40 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~539 ms (noticeable)
Model details →
Llama 3.2 11B Vision
11B
llama
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 14.1 GBHeadroom: 1.9 GBTTFT: noticeable
  • • Tight VRAM fit — only 1.9 GB headroom left for context growth
72
tok/s
Estimated
Weights
6.50 GB
KV cache
5.50 GB
Activations
0.33 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~539 ms (noticeable)
Model details →
Gemma 4 12B
12B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 15.8 GBHeadroom: 0.2 GBTTFT: noticeable
  • • Tight VRAM fit — only 0.2 GB headroom left for context growth
ollama run gemma4:12b
66
tok/s
Estimated
Weights
7.60 GB
KV cache
6.00 GB
Activations
0.39 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~589 ms (noticeable)
Model details →
Gemma 3 12B
12B
gemma
Commercial OK
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 gemma3:12b
66
tok/s
Estimated
Weights
7.30 GB
KV cache
6.00 GB
Activations
0.37 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~589 ms (noticeable)
Model details →
Pixtral 12B
12B
mistral
Commercial OK
Quant: 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:12b
66
tok/s
Estimated
Weights
7.00 GB
KV cache
6.00 GB
Activations
0.36 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~589 ms (noticeable)
Model details →
GLM-4V 9B
13.9B
glm
Quant: Q4_K_MContext: 2,048VRAM: 12.5 GBHeadroom: 3.5 GBTTFT: noticeable
  • • Tight VRAM fit — only 3.5 GB headroom left for context growth
57
tok/s
Estimated
Weights
8.50 GB
KV cache
1.74 GB
Activations
0.43 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~682 ms (noticeable)
Model details →
Ministral 3 14B
14B
mistral
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 13.1 GBHeadroom: 2.9 GBTTFT: noticeable
  • • Tight VRAM fit — only 2.9 GB headroom left for context growth
ollama run ministral-3:14b
57
tok/s
Estimated
Weights
9.10 GB
KV cache
1.75 GB
Activations
0.46 GB
Runtime
1.80 GB
Time to first token (prefill, 512-token prompt): ~687 ms (noticeable)
Model details →

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: 128 new comfortable, 112 new tradeoff

  • • Qwen 3 0.6B
  • • Llama 3.1 8B Instruct
  • • Qwen 3 8B
  • • Llama 3.2 3B Instruct
Shop this upgrade↗

Upgrade to NVIDIA RTX 2080 Ti 22GB (China-mod)

~$350

22 GB VRAM (vs your 16 GB) plus a bandwidth jump from ~736 GB/s to ~616 GB/s.

Unlocks: 155 new comfortable

  • • Qwen 3 0.6B
  • • Llama 3.1 8B Instruct
  • • Qwen 3 8B
  • • Gemma 4 12B
Shop this upgrade↗

Add a second NVIDIA GeForce RTX 4080 Super

~$1099

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: 202 new comfortable

  • • Qwen 3 0.6B
  • • Llama 3.1 8B Instruct
  • • Qwen 3 30B-A3B
  • • Qwen 2.5 Coder 32B Instruct
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 (16 GB) + 60% of system RAM (19 GB) combined.

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

Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.

—
Qwen 3 235B-A22B
235B
qwen
Commercial OK

Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.

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

Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 GB) combined.

—
Llama 4 Scout
109B
llama
Commercial OK

Even with CPU offload, needs more memory than your VRAM (16 GB) + 60% of system RAM (19 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.

RunLocalAI Will-It-Run Framework →

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