RUNLOCALAIv38
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RUNLOCALAI · v38
Will it run? / AMD Instinct MI300A (APU) / vision

What can AMD Instinct MI300A (APU) run for vision?

Build: AMD Instinct MI300A (APU) + — + 32 GB RAM (windows)

Memory: 128 GB VRAM + 32 GB system RAM
Runner: llama.cpp (CPU only)
AnyChatCodingAgentsReasoningVisionLong contextCreative

Runs comfortably
33 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: 11.9 GBHeadroom: 116.1 GB
ollama run gemma4:e2b
318
tok/s
E
Weights
2.13 GB
KV cache
1.00 GB
Activations
8.30 GB
Runtime
0.50 GB
Model details →Run-on benchmark page →
#2Phi-3.5 Vision
4.2B
phi
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 13.5 GBHeadroom: 114.5 GB
266
tok/s
E
Weights
2.54 GB
KV cache
2.10 GB
Activations
8.32 GB
Runtime
0.50 GB
Model details →Run-on benchmark page →
#3Gemma 4 E4B (Effective 4B)
4B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 15.2 GBHeadroom: 112.8 GB
ollama run gemma4:e4b
159
tok/s
E
Weights
4.25 GB
KV cache
2.00 GB
Activations
8.40 GB
Runtime
0.50 GB
Model details →Run-on benchmark page →
#4Gemma 3 4B
4B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 15.2 GBHeadroom: 112.8 GB
ollama run gemma3:4b
159
tok/s
E
Weights
4.25 GB
KV cache
2.00 GB
Activations
8.40 GB
Runtime
0.50 GB
Model details →Run-on benchmark page →
#5LLaVA 1.6 Mistral 7B
7B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 16.6 GBHeadroom: 111.4 GB
160
tok/s
E
Weights
4.23 GB
KV cache
3.50 GB
Activations
8.40 GB
Runtime
0.50 GB
Model details →Run-on benchmark page →
#6Qwen 2.5-VL 3B
3B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 12.1 GBHeadroom: 115.9 GB
373
tok/s
E
Weights
1.81 GB
KV cache
1.50 GB
Activations
8.28 GB
Runtime
0.50 GB
Model details →Run-on benchmark page →
#7Qwen 2.5-VL 7B
7B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 16.6 GBHeadroom: 111.4 GB
160
tok/s
E
Weights
4.23 GB
KV cache
3.50 GB
Activations
8.40 GB
Runtime
0.50 GB
Model details →Run-on benchmark page →
#8Molmo 7B-D
8B
other
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 11.7 GBHeadroom: 116.3 GB
140
tok/s
E
Weights
4.83 GB
KV cache
2.00 GB
Activations
4.34 GB
Runtime
0.50 GB
Model details →Run-on benchmark page →
#9MiniCPM-V 2.6 8B
8B
minicpm
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.8 GBHeadroom: 110.2 GB
140
tok/s
E
Weights
4.83 GB
KV cache
4.00 GB
Activations
8.43 GB
Runtime
0.50 GB
Model details →Run-on benchmark page →
#10Janus-Pro 7B
7B
janus
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 10.8 GBHeadroom: 117.2 GB
160
tok/s
E
Weights
4.23 GB
KV cache
1.75 GB
Activations
4.31 GB
Runtime
0.50 GB
Model details →Run-on benchmark page →
#11Qwen 2-VL 7B
7B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 16.6 GBHeadroom: 111.4 GB
160
tok/s
E
Weights
4.23 GB
KV cache
3.50 GB
Activations
8.40 GB
Runtime
0.50 GB
Model details →Run-on benchmark page →
#12MiniCPM-V 3 8B
8B
minicpm
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.8 GBHeadroom: 110.2 GB
140
tok/s
E
Weights
4.83 GB
KV cache
4.00 GB
Activations
8.43 GB
Runtime
0.50 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: 127 new comfortable, 4 new tradeoff

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Llama 3.2 3B Instruct
  • • Phi-3.5 Mini Instruct
Shop this upgrade↗

Upgrade to AMD Instinct MI300X

see current pricing

192 GB VRAM (vs your 128 GB) plus a bandwidth jump from ~? GB/s to ~5325 GB/s.

Unlocks: 135 new comfortable

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Llama 3.2 3B Instruct
  • • Phi-3.5 Mini Instruct
Shop this upgrade↗

Add a second AMD Instinct MI300A (APU)

see current pricing

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: 138 new comfortable

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Llama 3.2 3B Instruct
  • • 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 (128 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 (128 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 (128 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 (128 GB) + 60% of system RAM (19 GB) combined.

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

Even with CPU offload, needs more memory than your VRAM (128 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.