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

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

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
153 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: 21.6 GBHeadroom: 106.4 GB
ollama run hermes3:8b
79
tok/s
E
Weights
8.50 GB
KV cache
4.00 GB
Activations
8.62 GB
Runtime
0.50 GB
Model details →Run-on benchmark page →
#2Gemma 2 9B Instruct
9B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 23.2 GBHeadroom: 104.8 GB
ollama run gemma2:9b
71
tok/s
E
Weights
9.56 GB
KV cache
4.50 GB
Activations
8.67 GB
Runtime
0.50 GB
Model details →Run-on benchmark page →
#3Hermes 3 Llama 3.1 70B
70B
hermes
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 88.1 GBHeadroom: 39.9 GB
ollama run hermes3:70b
16
tok/s
E
Weights
42.26 GB
KV cache
35.00 GB
Activations
10.31 GB
Runtime
0.50 GB
Model details →Run-on benchmark page →
#4Dolphin 3.0 Llama 3.2 3B
3B
dolphin
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 →
#5Hermes 3 Llama 3.2 3B
3B
hermes
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 →
#6Gemma 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 →
#7Gemma 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 →
#8Gemma 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 →
#9CodeGemma 7B
7B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 16.6 GBHeadroom: 111.4 GB
ollama run codegemma:7b
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 →
#10PaliGemma 2 3B
3B
gemma
Commercial OK
Quant: BF16Context: 8,192VRAM: 16.5 GBHeadroom: 111.5 GB
113
tok/s
E
Weights
6.00 GB
KV cache
1.50 GB
Activations
8.49 GB
Runtime
0.50 GB
Model details →Run-on benchmark page →
#11Dolphin 3.0 Mistral 24B
24B
dolphin
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 35.9 GBHeadroom: 92.1 GB
ollama run dolphin-mistral:24b
47
tok/s
E
Weights
14.49 GB
KV cache
12.00 GB
Activations
8.92 GB
Runtime
0.50 GB
Model details →Run-on benchmark page →
#12Llama 3.2 3B Instruct
3B
llama
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 13.5 GBHeadroom: 114.5 GB
ollama run llama3.2:3b
212
tok/s
E
Weights
3.19 GB
KV cache
1.50 GB
Activations
8.35 GB
Runtime
0.50 GB
Model details →Run-on benchmark page →

Runs with tradeoffs
2 models

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

Command R+ 104B
104B
command-r
Quant: Q4_K_MContext: 8,192VRAM: 126.6 GBHeadroom: 1.4 GB
  • • Tight VRAM fit — only 1.4 GB headroom left for context growth
ollama run command-r-plus:104b
11
tok/s
E
Weights
62.79 GB
KV cache
52.00 GB
Activations
11.33 GB
Runtime
0.50 GB
Model details →Run-on benchmark page →
Command R+ (Aug 2024)
104B
command-r
Quant: AWQ-INT4Context: 2,048VRAM: 124.7 GBHeadroom: 3.3 GB
  • • Tight VRAM fit — only 3.3 GB headroom left for context growth
6
tok/s
E
Weights
104.00 GB
KV cache
13.00 GB
Activations
7.25 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: 7 new comfortable, 4 new tradeoff

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Whisper Large v3 Turbo
  • • SmolLM 2 360M 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: 15 new comfortable

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Command R+ 104B
  • • GLM-5
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: 18 new comfortable

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • DeepSeek V4 Flash (284B MoE)
  • • Command R+ 104B
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 (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.