RUNLOCALAIv38
→WILL IT RUNBEST GPUCOMPARETROUBLESHOOTSTARTPULSEMODELSHARDWARETOOLSBENCH
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RUNLOCALAI · v38
Will it run? / Intel Arc A770 16GB / vision

What can Intel Arc A770 16GB run for vision?

Build: Intel Arc A770 16GB + — + 32 GB RAM (windows)

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

Runs comfortably
12 models

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

#1Moondream 2
1.9B
other
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 4.5 GBHeadroom: 11.5 GB
219
tok/s
E
Weights
1.15 GB
KV cache
0.24 GB
Activations
2.11 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →
#2LLaVA 1.6 Mistral 7B
7B
other
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 8.4 GBHeadroom: 7.6 GB
60
tok/s
E
Weights
4.23 GB
KV cache
0.88 GB
Activations
2.26 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →
#3Qwen 2.5-VL 7B
7B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 8.4 GBHeadroom: 7.6 GB
60
tok/s
E
Weights
4.23 GB
KV cache
0.88 GB
Activations
2.26 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →
#4Janus-Pro 7B
7B
janus
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 11.3 GBHeadroom: 4.7 GB
60
tok/s
E
Weights
4.23 GB
KV cache
1.75 GB
Activations
4.31 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →
#5Qwen 2-VL 7B
7B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 8.4 GBHeadroom: 7.6 GB
60
tok/s
E
Weights
4.23 GB
KV cache
0.88 GB
Activations
2.26 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →
#6LLaVA-OneVision 7B
7B
other
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 8.4 GBHeadroom: 7.6 GB
60
tok/s
E
Weights
4.23 GB
KV cache
0.88 GB
Activations
2.26 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →
#7MiniCPM-V 2.6 8B
8B
minicpm
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.1 GBHeadroom: 6.9 GB
52
tok/s
E
Weights
4.83 GB
KV cache
1.00 GB
Activations
2.29 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →
#8MiniCPM-V 3 8B
8B
minicpm
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.1 GBHeadroom: 6.9 GB
52
tok/s
E
Weights
4.83 GB
KV cache
1.00 GB
Activations
2.29 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →
#9PaliGemma 2 3B
3B
gemma
Commercial OK
Quant: BF16Context: 2,048VRAM: 9.7 GBHeadroom: 6.3 GB
42
tok/s
E
Weights
6.00 GB
KV cache
0.38 GB
Activations
2.35 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →
#10Llama 3.2 11B Vision Instruct
11B
llama
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 11.4 GBHeadroom: 4.6 GB
ollama run llama3.2-vision:11b
38
tok/s
E
Weights
6.64 GB
KV cache
1.38 GB
Activations
2.38 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →
#11Llama 3.2 11B Vision
11B
llama
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 11.4 GBHeadroom: 4.6 GB
38
tok/s
E
Weights
6.64 GB
KV cache
1.38 GB
Activations
2.38 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →
#12Whisper Large v3
1.55B
other
Commercial OK
Quant: FP16Context: 0VRAM: 4.3 GBHeadroom: 11.7 GB
81
tok/s
E
Weights
3.10 GB
KV cache
0.00 GB
Activations
0.16 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →

Runs with tradeoffs
16 models

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

Qwen 2.5-VL 3B
3B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 12.6 GBHeadroom: 3.4 GB
  • • Tight VRAM fit — only 3.4 GB headroom left for context growth
139
tok/s
E
Weights
1.81 GB
KV cache
1.50 GB
Activations
8.28 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →
Gemma 4 E2B (Effective 2B)
2B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 12.4 GBHeadroom: 3.6 GB
  • • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run gemma4:e2b
118
tok/s
E
Weights
2.13 GB
KV cache
1.00 GB
Activations
8.30 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →
Phi-3.5 Vision
4.2B
phi
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 14.0 GBHeadroom: 2.0 GB
  • • Tight VRAM fit — only 2.0 GB headroom left for context growth
99
tok/s
E
Weights
2.54 GB
KV cache
2.10 GB
Activations
8.32 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →
Gemma 4 E4B (Effective 4B)
4B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 15.7 GBHeadroom: 0.3 GB
  • • Tight VRAM fit — only 0.3 GB headroom left for context growth
ollama run gemma4:e4b
59
tok/s
E
Weights
4.25 GB
KV cache
2.00 GB
Activations
8.40 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →
Gemma 3 4B
4B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 15.7 GBHeadroom: 0.3 GB
  • • Tight VRAM fit — only 0.3 GB headroom left for context growth
ollama run gemma3:4b
59
tok/s
E
Weights
4.25 GB
KV cache
2.00 GB
Activations
8.40 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →
Molmo 7B-D
8B
other
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 12.2 GBHeadroom: 3.8 GB
  • • Tight VRAM fit — only 3.8 GB headroom left for context growth
52
tok/s
E
Weights
4.83 GB
KV cache
2.00 GB
Activations
4.34 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →
Gemma 3 12B
12B
gemma
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 12.2 GBHeadroom: 3.8 GB
  • • Tight VRAM fit — only 3.8 GB headroom left for context growth
ollama run gemma3:12b
35
tok/s
E
Weights
7.25 GB
KV cache
1.50 GB
Activations
2.41 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →
Pixtral 12B
12B
mistral
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 12.2 GBHeadroom: 3.8 GB
  • • Tight VRAM fit — only 3.8 GB headroom left for context growth
ollama run pixtral:12b
35
tok/s
E
Weights
7.25 GB
KV cache
1.50 GB
Activations
2.41 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →

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: 45 new comfortable, 78 new tradeoff

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • DeepSeek R1 Distill Qwen 7B
  • • Qwen 3 8B
Shop this upgrade↗

Add a second Intel Arc A770 16GB

~$269

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: 101 new comfortable

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Gemma 4 E2B (Effective 2B)
  • • Llama 3.2 3B Instruct
Shop this upgrade↗

Some links above are affiliate links. We may earn a commission at no extra cost to you. How we make money.

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.

—
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.

—
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.

—

How to read these numbers

M
Measured — we ran this exact combo on owner hardware.

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

E
Estimated — pure formula based on VRAM bandwidth and model architecture.

Full methodology →

Want a specific benchmark we don't have? Email support@runlocalai.co and we'll prioritize it.