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

What can Intel Arc A770 16GB run for chat?

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
56 models

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

#1Granite 3.0 2B Instruct
2B
granite
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 6.9 GBHeadroom: 9.1 GB
208
tok/s
E
Weights
1.21 GB
KV cache
0.50 GB
Activations
4.16 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →
#2Mistral 7B Instruct v0.3
7B
mistral
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 8.4 GBHeadroom: 7.6 GB
ollama run mistral:7b
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 3 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 →
#4InternLM 2.5 7B Chat
7B
internlm
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 →
#5Falcon 3 7B Instruct
7B
falcon
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 →
#6Qwen 3 8B
8B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.1 GBHeadroom: 6.9 GB
ollama run qwen3:8b
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 →
#7Llama 3.3 8B Instruct
8B
llama
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 →
#8Tulu 3 8B
8B
other
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 →
#9Ministral 8B Instruct
8B
mistral
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 →
#10Gemma 2 9B Instruct
9B
gemma
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 9.9 GBHeadroom: 6.1 GB
ollama run gemma2:9b
46
tok/s
E
Weights
5.43 GB
KV cache
1.13 GB
Activations
2.32 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →
#11Gemma 3 1B
1B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 10.3 GBHeadroom: 5.7 GB
ollama run gemma3:1b
417
tok/s
E
Weights
0.60 GB
KV cache
0.50 GB
Activations
8.22 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →
#12Llama 3.2 1B Instruct
1B
llama
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 10.8 GBHeadroom: 5.2 GB
ollama run llama3.2:1b
237
tok/s
E
Weights
1.06 GB
KV cache
0.50 GB
Activations
8.25 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →

Runs with tradeoffs
69 models

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

Dolphin 3.0 Llama 3.2 3B
3B
dolphin
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 →
Hermes 3 Llama 3.2 3B
3B
hermes
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 →
Llama 3.2 3B Instruct
3B
llama
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 14.0 GBHeadroom: 2.0 GB
  • • Tight VRAM fit — only 2.0 GB headroom left for context growth
ollama run llama3.2:3b
79
tok/s
E
Weights
3.19 GB
KV cache
1.50 GB
Activations
8.35 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →
Llama 3.1 8B Instruct
8B
llama
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 15.3 GBHeadroom: 0.7 GB
  • • Tight VRAM fit — only 0.7 GB headroom left for context growth
ollama run llama3.1:8b
52
tok/s
E
Weights
4.83 GB
KV cache
1.07 GB
Activations
8.43 GB
Runtime
1.00 GB
Model details →Run-on benchmark page →
Granite 3.0 8B Instruct
8B
granite
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 →
Mistral Nemo 12B Instruct
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 mistral-nemo: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 →
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 →
Stable LM 2 12B
12B
other
Quant: Q4_K_MContext: 4,096VRAM: 15.7 GBHeadroom: 0.3 GB
  • • Tight VRAM fit — only 0.3 GB headroom left for context growth
35
tok/s
E
Weights
7.25 GB
KV cache
3.00 GB
Activations
4.46 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: 1 new comfortable, 78 new tradeoff

  • • SmolLM 2 360M Instruct
  • • Llama 3.1 8B Instruct
  • • Qwen 3 30B-A3B
  • • Qwen 2.5 Coder 32B Instruct
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: 57 new comfortable

  • • Gemma 4 E2B (Effective 2B)
  • • Llama 3.2 3B Instruct
  • • Phi-3.5 Vision
  • • Phi-3.5 Mini 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.