RUNLOCALAIv38
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RUNLOCALAI · v38
Will it run? / NVIDIA RTX PRO 4500 Blackwell / reasoning

What can NVIDIA RTX PRO 4500 Blackwell run for reasoning?

Build: NVIDIA RTX PRO 4500 Blackwell + — + 32 GB RAM (windows)

Memory: 32 GB VRAM + 32 GB system RAM
Runner: llama.cpp / Ollama (CUDA)
AnyChatCodingAgentsReasoningVisionLong contextCreative

Runs comfortably
136 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: 10.9 GBHeadroom: 21.1 GB
121
tok/s
Estimated
Weights
4.83 GB
KV cache
4.00 GB
Activations
0.25 GB
Runtime
1.80 GB
Model details →
#2RefinedNeuro RN TR R1
8B
llama
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 10.9 GBHeadroom: 21.1 GB
ollama run RefinedNeuro/RN_TR_R1:latest
121
tok/s
Estimated
Weights
4.83 GB
KV cache
4.00 GB
Activations
0.25 GB
Runtime
1.80 GB
Model details →
#3DeepSeek R1 Distill Llama 8B
8B
deepseek
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 10.9 GBHeadroom: 21.1 GB
121
tok/s
Estimated
Weights
4.83 GB
KV cache
4.00 GB
Activations
0.25 GB
Runtime
1.80 GB
Model details →
#4NVIDIA Nemotron Nano 9B v2 Japanese
9B
other
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 12.0 GBHeadroom: 20.0 GB
107
tok/s
Estimated
Weights
5.43 GB
KV cache
4.50 GB
Activations
0.28 GB
Runtime
1.80 GB
Model details →
#5DeepSeek R1 Distill Qwen 7B
7B
deepseek
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 13.1 GBHeadroom: 18.9 GB
ollama run deepseek-r1:7b
78
tok/s
Estimated
Weights
7.44 GB
KV cache
3.50 GB
Activations
0.38 GB
Runtime
1.80 GB
Model details →
#6Phi-4 Reasoning 14B
14B
phi
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.7 GBHeadroom: 14.3 GB
ollama run phi4-reasoning:14b
69
tok/s
Estimated
Weights
8.45 GB
KV cache
7.00 GB
Activations
0.43 GB
Runtime
1.80 GB
Model details →
#7DeepSeek R1 Distill Qwen 14B
14B
deepseek
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.7 GBHeadroom: 14.3 GB
ollama run deepseek-r1:14b
69
tok/s
Estimated
Weights
8.45 GB
KV cache
7.00 GB
Activations
0.43 GB
Runtime
1.80 GB
Model details →
#8DeepSeek V3 Lite (16B MoE)
16B
deepseek
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 20.0 GBHeadroom: 12.0 GB
402
tok/s
Estimated
Weights
9.66 GB
KV cache
8.00 GB
Activations
0.49 GB
Runtime
1.80 GB
Model details →
#9Omni 31B Turkish Reasoning
31B
other
Quant: Q4_K_MContext: 2,048VRAM: 25.3 GBHeadroom: 6.7 GB
31
tok/s
Estimated
Weights
18.72 GB
KV cache
3.88 GB
Activations
0.94 GB
Runtime
1.80 GB
Model details →
#10DeepSeek R1 Distill Qwen 32B
32B
deepseek
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 26.1 GBHeadroom: 5.9 GB
ollama run deepseek-r1:32b
30
tok/s
Estimated
Weights
19.32 GB
KV cache
4.00 GB
Activations
0.97 GB
Runtime
1.80 GB
Model details →
#11QwQ 32B Preview
32B
qwen
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 26.1 GBHeadroom: 5.9 GB
ollama run qwq:32b
30
tok/s
Estimated
Weights
19.32 GB
KV cache
4.00 GB
Activations
0.97 GB
Runtime
1.80 GB
Model details →
#12EXAONE Deep 7.8B
7.8B
other
Quant: Q4_K_MContext: 8,192VRAM: 10.7 GBHeadroom: 21.3 GB
124
tok/s
Estimated
Weights
4.71 GB
KV cache
3.90 GB
Activations
0.24 GB
Runtime
1.80 GB
Model details →

Runs with tradeoffs
27 models

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

DeepSeek R1 Distill Mistral 24B
24B
deepseek
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 29.0 GBHeadroom: 3.0 GB
  • • Tight VRAM fit — only 3.0 GB headroom left for context growth
40
tok/s
Estimated
Weights
14.49 GB
KV cache
12.00 GB
Activations
0.73 GB
Runtime
1.80 GB
Model details →
DeepSeek R1 Distill Qwen 3 32B
32B
deepseek
Commercial OK
Quant: AWQ-INT4Context: 2,048VRAM: 39.4 GBHeadroom: 11.8 GB
  • • Partial CPU offload: ~19% of layers run on CPU
18
tok/s
Estimated
Weights
32.00 GB
KV cache
4.00 GB
Activations
1.60 GB
Runtime
1.80 GB
Model details →
Magistral 32B
32B
mistral
Quant: AWQ-INT4Context: 2,048VRAM: 39.4 GBHeadroom: 11.8 GB
  • • Partial CPU offload: ~19% of layers run on CPU
18
tok/s
Estimated
Weights
32.00 GB
KV cache
4.00 GB
Activations
1.60 GB
Runtime
1.80 GB
Model details →
Qwen 3 Coder 32B
32B
qwen
Commercial OK
Quant: AWQ-INT4Context: 2,048VRAM: 39.4 GBHeadroom: 11.8 GB
  • • Partial CPU offload: ~19% of layers run on CPU
18
tok/s
Estimated
Weights
32.00 GB
KV cache
4.00 GB
Activations
1.60 GB
Runtime
1.80 GB
Model details →
Jamba 1.5 Mini
52B
other
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 41.3 GBHeadroom: 9.9 GB
  • • Partial CPU offload: ~22% of layers run on CPU
80
tok/s
Estimated
Weights
31.39 GB
KV cache
6.50 GB
Activations
1.57 GB
Runtime
1.80 GB
Model details →
Sarvam 30B
30B
other
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 28.3 GBHeadroom: 3.7 GB
  • • Tight VRAM fit — only 3.7 GB headroom left for context growth
32
tok/s
Estimated
Weights
18.11 GB
KV cache
7.50 GB
Activations
0.91 GB
Runtime
1.80 GB
Model details →
Pollux Judge 32B
32B
other
Commercial OK
Quant: Q4_K_MContext: 4,096VRAM: 30.1 GBHeadroom: 1.9 GB
  • • Tight VRAM fit — only 1.9 GB headroom left for context growth
30
tok/s
Estimated
Weights
19.32 GB
KV cache
8.00 GB
Activations
0.97 GB
Runtime
1.80 GB
Model details →
Mihenk LLM v2 35B (Turkish Financial)
35B
other
Quant: Q4_K_MContext: 2,048VRAM: 28.4 GBHeadroom: 3.6 GB
  • • Tight VRAM fit — only 3.6 GB headroom left for context growth
28
tok/s
Estimated
Weights
21.13 GB
KV cache
4.38 GB
Activations
1.06 GB
Runtime
1.80 GB
Model details →

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: 98 new comfortable, 40 new tradeoff

  • • all-MiniLM-L6-v2
  • • Piper
  • • Whisper Tiny
  • • Whisper Base
Shop this upgrade↗

Upgrade to NVIDIA A100 40GB

see current pricing

40 GB VRAM (vs your 32 GB) plus a bandwidth jump from ~896 GB/s to ~1555 GB/s.

Unlocks: 115 new comfortable

  • • all-MiniLM-L6-v2
  • • Piper
  • • Whisper Tiny
  • • Whisper Base
Shop this upgrade↗

Add a second NVIDIA RTX PRO 4500 Blackwell

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: 135 new comfortable

  • • all-MiniLM-L6-v2
  • • Piper
  • • Whisper Tiny
  • • Whisper Base
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 (32 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 (32 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 (32 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 (32 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 (32 GB) + 60% of system RAM (19 GB) combined.

—

How to read these numbers

Measured here
Measured here - RunLocalAI ran this exact combo on owner hardware with public evidence.

Source-backed
Source-backed / community - a reproduced public source supports the speed, but it is not labeled as owner-measured.

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

Estimated
Estimated - formula based on VRAM bandwidth and model architecture; not a benchmark row.

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