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

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

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
124 models

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

#1Llama 3.1 Nemotron Nano 8B
8B
llama
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 →
#2DeepSeek R1 Distill Llama 8B
8B
deepseek
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 →
#3DeepSeek R1 Distill Qwen 7B
7B
deepseek
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 20.0 GBHeadroom: 108.0 GB
ollama run deepseek-r1:7b
91
tok/s
E
Weights
7.44 GB
KV cache
3.50 GB
Activations
8.56 GB
Runtime
0.50 GB
Model details →Run-on benchmark page →
#4Phi-4 Reasoning 14B
14B
phi
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 24.6 GBHeadroom: 103.4 GB
ollama run phi4-reasoning:14b
80
tok/s
E
Weights
8.45 GB
KV cache
7.00 GB
Activations
8.61 GB
Runtime
0.50 GB
Model details →Run-on benchmark page →
#5DeepSeek R1 Distill Qwen 14B
14B
deepseek
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 24.6 GBHeadroom: 103.4 GB
ollama run deepseek-r1:14b
80
tok/s
E
Weights
8.45 GB
KV cache
7.00 GB
Activations
8.61 GB
Runtime
0.50 GB
Model details →Run-on benchmark page →
#6DeepSeek V3 Lite (16B MoE)
16B
deepseek
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 26.8 GBHeadroom: 101.2 GB
466
tok/s
E
Weights
9.66 GB
KV cache
8.00 GB
Activations
8.68 GB
Runtime
0.50 GB
Model details →Run-on benchmark page →
#7DeepSeek R1 Distill Mistral 24B
24B
deepseek
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 35.9 GBHeadroom: 92.1 GB
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 →
#8QwQ 32B Preview
32B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 45.0 GBHeadroom: 83.0 GB
ollama run qwq:32b
35
tok/s
E
Weights
19.32 GB
KV cache
16.00 GB
Activations
9.16 GB
Runtime
0.50 GB
Model details →Run-on benchmark page →
#9Qwen 2.5 Math 7B
7B
qwen
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 →
#10Qwen 3 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 →
#11InternLM 2.5 7B Chat
7B
internlm
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 →
#12DeepSeek R1 Distill Qwen 3 32B
32B
deepseek
Commercial OK
Quant: AWQ-INT4Context: 8,192VRAM: 58.3 GBHeadroom: 69.7 GB
21
tok/s
E
Weights
32.00 GB
KV cache
16.00 GB
Activations
9.79 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: 36 new comfortable, 4 new tradeoff

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Gemma 4 E2B (Effective 2B)
  • • Llama 3.2 3B 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: 44 new comfortable

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Gemma 4 E2B (Effective 2B)
  • • Llama 3.2 3B 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: 47 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 (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.