RUNLOCALAIv38
→WILL IT RUNBEST GPUCOMPARETROUBLESHOOTSTARTPULSEMODELSHARDWARETOOLSBENCH
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RUNLOCALAI · v38
Will it run? / NVIDIA GB200 NVL72 / coding

What can NVIDIA GB200 NVL72 run for coding?

Build: NVIDIA GB200 NVL72 + — + 32 GB RAM (windows)

Memory: 13824 GB VRAM + 32 GB system RAM
Runner: llama.cpp / Ollama (CUDA)
AnyChatCodingAgentsReasoningVisionLong contextCreative

Runs comfortably
166 models

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

#1CodeGemma 7B
7B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 17.9 GBHeadroom: 13806.1 GB
ollama run codegemma:7b
1230
tok/s
E
Weights
4.23 GB
KV cache
3.50 GB
Activations
8.40 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#2DeepSeek Coder V2 Lite (16B)
16B
deepseek
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 28.1 GBHeadroom: 13795.9 GB
ollama run deepseek-coder-v2:16b
538
tok/s
E
Weights
9.66 GB
KV cache
8.00 GB
Activations
8.68 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#3Codestral 22B
22B
mistral
Quant: Q8_0Context: 8,192VRAM: 45.5 GBHeadroom: 13778.5 GB
ollama run codestral:22b
222
tok/s
E
Weights
23.38 GB
KV cache
11.00 GB
Activations
9.36 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#4Qwen 2.5 Coder 32B Instruct
32B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 47.8 GBHeadroom: 13776.2 GB
ollama run qwen2.5-coder:32b
153
tok/s
E
Weights
34.00 GB
KV cache
2.15 GB
Activations
9.89 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#5Qwen 3 30B-A3B
30B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 58.5 GBHeadroom: 13765.5 GB
ollama run qwen3:30b
163
tok/s
E
Weights
31.88 GB
KV cache
15.00 GB
Activations
9.79 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#6Gemma 4 31B Dense
31B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 60.1 GBHeadroom: 13763.9 GB
ollama run gemma4:31b
158
tok/s
E
Weights
32.94 GB
KV cache
15.50 GB
Activations
9.84 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#7Qwen 3 32B
32B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 61.7 GBHeadroom: 13762.3 GB
ollama run qwen3:32b
153
tok/s
E
Weights
34.00 GB
KV cache
16.00 GB
Activations
9.89 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#8Qwen 2.5 32B Instruct
32B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 61.7 GBHeadroom: 13762.3 GB
ollama run qwen2.5:32b
153
tok/s
E
Weights
34.00 GB
KV cache
16.00 GB
Activations
9.89 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#9Llama 3.1 70B Instruct
70B
llama
Commercial OK
Quant: Q5_K_MContext: 8,192VRAM: 95.5 GBHeadroom: 13728.5 GB
ollama run llama3.1:70b
108
tok/s
E
Weights
48.13 GB
KV cache
35.00 GB
Activations
10.60 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#10Llama 3.3 70B Instruct
70B
llama
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 90.8 GBHeadroom: 13733.2 GB
ollama run llama3.3:70b
70
tok/s
E
Weights
74.38 GB
KV cache
2.68 GB
Activations
11.91 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#11Qwen 3 14B
14B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 32.6 GBHeadroom: 13791.4 GB
ollama run qwen3:14b
350
tok/s
E
Weights
14.88 GB
KV cache
7.00 GB
Activations
8.94 GB
Runtime
1.80 GB
Model details →Run-on benchmark page →
#12Qwen 2.5 14B Instruct
14B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 32.6 GBHeadroom: 13791.4 GB
ollama run qwen2.5:14b
350
tok/s
E
Weights
14.88 GB
KV cache
7.00 GB
Activations
8.94 GB
Runtime
1.80 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: 17 new comfortable

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Gemma 4 E2B (Effective 2B)
  • • Whisper Large v3
Shop this upgrade↗

Add a second NVIDIA GB200 NVL72

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: 17 new comfortable

  • • Gemma 3 1B
  • • Llama 3.2 1B Instruct
  • • Gemma 4 E2B (Effective 2B)
  • • Whisper Large v3
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 3 popular models

Need more memory than you have. Shown for orientation.

Qwen 3.6 35B-A3B (MTP)
35B
qwen
Commercial OK

Even with CPU offload, needs more memory than your VRAM (13824 GB) + 60% of system RAM (19 GB) combined.

—
Qwen 3.6 27B (MTP)
27B
qwen
Commercial OK

Even with CPU offload, needs more memory than your VRAM (13824 GB) + 60% of system RAM (19 GB) combined.

—
Ring-2.6-1T
1000B
other
Commercial OK

Even with CPU offload, needs more memory than your VRAM (13824 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.