What can NVIDIA RTX PRO 6000 Blackwell run for coding?

Build: RTX PRO 6000 Blackwell + Threadripper PRO + 128GB

Memory: 96 GB VRAM + 128 GB system RAM
Runner: llama.cpp / Ollama (CUDA)

Runs comfortably
223 models

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

#1DeepSeek Coder V2 Lite (16B)
16B
deepseek
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 19.8 GBHeadroom: 76.2 GB
ollama run deepseek-coder-v2:16b
121
tok/s
Estimated
Weights
9.50 GB
KV cache
8.00 GB
Activations
0.48 GB
Runtime
1.80 GB
#2CodeGemma 7B
7B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 9.7 GBHeadroom: 86.3 GB
ollama run codegemma:7b
276
tok/s
Estimated
Weights
4.20 GB
KV cache
3.50 GB
Activations
0.22 GB
Runtime
1.80 GB
#3Codestral 22B
22B
mistral
Quant: Q8_0Context: 8,192VRAM: 37.0 GBHeadroom: 59.0 GB
ollama run codestral:22b
50
tok/s
Estimated
Weights
23.00 GB
KV cache
11.00 GB
Activations
1.16 GB
Runtime
1.80 GB
#4Qwen 3 8B
8B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 14.4 GBHeadroom: 81.6 GB
ollama run qwen3:8b
137
tok/s
Estimated
Weights
8.20 GB
KV cache
4.00 GB
Activations
0.42 GB
Runtime
1.80 GB
#5Qwen 2.5 7B Instruct
7B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 10.8 GBHeadroom: 85.2 GB
ollama run qwen2.5:7b
157
tok/s
Estimated
Weights
8.10 GB
KV cache
0.47 GB
Activations
0.41 GB
Runtime
1.80 GB
#6Qwen 3 14B
14B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 24.6 GBHeadroom: 71.4 GB
ollama run qwen3:14b
78
tok/s
Estimated
Weights
15.00 GB
KV cache
7.00 GB
Activations
0.76 GB
Runtime
1.80 GB
#7Qwen 2.5 14B Instruct
14B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 25.3 GBHeadroom: 70.7 GB
ollama run qwen2.5:14b
78
tok/s
Estimated
Weights
15.70 GB
KV cache
7.00 GB
Activations
0.79 GB
Runtime
1.80 GB
#8Qwen 2.5 Coder 32B Instruct
32B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 39.7 GBHeadroom: 56.3 GB
ollama run qwen2.5-coder:32b
34
tok/s
Estimated
Weights
34.00 GB
KV cache
2.15 GB
Activations
1.71 GB
Runtime
1.80 GB
#9Qwen 3 30B-A3B
30B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 50.4 GBHeadroom: 45.6 GB
ollama run qwen3:30b
37
tok/s
Estimated
Weights
32.00 GB
KV cache
15.00 GB
Activations
1.61 GB
Runtime
1.80 GB
#10Muse Glimmer 30B
30B
other
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 36.1 GBHeadroom: 59.9 GB
ollama run muse-glimmer
37
tok/s
Estimated
Weights
32.00 GB
KV cache
0.71 GB
Activations
1.61 GB
Runtime
1.80 GB
#11Gemma 4 31B Dense
31B
gemma
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 52.0 GBHeadroom: 44.0 GB
ollama run gemma4:31b
35
tok/s
Estimated
Weights
33.00 GB
KV cache
15.50 GB
Activations
1.66 GB
Runtime
1.80 GB
#12Qwen 3 32B
32B
qwen
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 53.5 GBHeadroom: 42.5 GB
ollama run qwen3:32b
34
tok/s
Estimated
Weights
34.00 GB
KV cache
16.00 GB
Activations
1.71 GB
Runtime
1.80 GB

Runs with tradeoffs
12 models

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

DeepSeek Coder V2 236B
236B
deepseek
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 172.0 GBHeadroom: 0.8 GB
  • Partial CPU offload: ~44% of layers run on CPU
  • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
29
tok/s
Estimated
Weights
134.00 GB
KV cache
29.50 GB
Activations
6.70 GB
Runtime
1.80 GB
DeepSeek V2.5 236B
236B
deepseek
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 172.0 GBHeadroom: 0.8 GB
  • Partial CPU offload: ~44% of layers run on CPU
  • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
29
tok/s
Estimated
Weights
134.00 GB
KV cache
29.50 GB
Activations
6.70 GB
Runtime
1.80 GB
GLM-5 Pro
144B
glm
Quant: AWQ-INT4Context: 8,192VRAM: 159.9 GBHeadroom: 12.9 GB
  • Partial CPU offload: ~40% of layers run on CPU
  • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
25
tok/s
Estimated
Weights
82.00 GB
KV cache
72.00 GB
Activations
4.11 GB
Runtime
1.80 GB
DBRX Base
132B
dbrx
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 146.6 GBHeadroom: 26.2 GB
  • Partial CPU offload: ~34% of layers run on CPU
  • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
20
tok/s
Estimated
Weights
75.00 GB
KV cache
66.00 GB
Activations
3.76 GB
Runtime
1.80 GB
Nemotron 3 Super (120B-A12B)
120B
other
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 92.4 GBHeadroom: 3.6 GB
  • Tight VRAM fit — only 3.6 GB headroom left for context growth
ollama run nemotron3:super
16
tok/s
Estimated
Weights
72.00 GB
KV cache
15.00 GB
Activations
3.60 GB
Runtime
1.80 GB
Quant: Q4_K_MContext: 2,048VRAM: 93.8 GBHeadroom: 2.2 GB
  • Tight VRAM fit — only 2.2 GB headroom left for context growth
ollama run mistral-large:123b
16
tok/s
Estimated
Weights
73.00 GB
KV cache
15.38 GB
Activations
3.65 GB
Runtime
1.80 GB
DBRX Instruct
132B
dbrx
Commercial OK
Quant: AWQ-INT4Context: 8,192VRAM: 146.6 GBHeadroom: 26.2 GB
  • Partial CPU offload: ~34% of layers run on CPU
  • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
12
tok/s
Estimated
Weights
75.00 GB
KV cache
66.00 GB
Activations
3.76 GB
Runtime
1.80 GB
Mixtral 8x22B Instruct
141B
mixtral
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 160.5 GBHeadroom: 12.3 GB
  • Partial CPU offload: ~40% of layers run on CPU
  • CPU is the bottleneck — upgrading RAM bandwidth helps more than VRAM here
ollama run mixtral:8x22b
5
tok/s
Estimated
Weights
84.00 GB
KV cache
70.50 GB
Activations
4.21 GB
Runtime
1.80 GB

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: 41 new comfortable, 14 new tradeoff

  • Qwen 3 0.6B
  • Qwen 3 1.7B
  • Gemma 3 270M
  • SmolLM2 135M Instruct

Upgrade to NVIDIA H200 NVL (PCIe)

~$32000

141 GB VRAM (vs your 96 GB) plus a bandwidth jump from ~1792 GB/s to ~4800 GB/s.

Unlocks: 46 new comfortable

  • Qwen 3 0.6B
  • Qwen 3 1.7B
  • Gemma 3 270M
  • SmolLM2 135M Instruct

Add a second NVIDIA RTX PRO 6000 Blackwell

~$8999

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: 55 new comfortable

  • Qwen 3 235B-A22B
  • Qwen 3 0.6B
  • GLM-5
  • Qwen 3 1.7B

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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 (96 GB) + 60% of system RAM (77 GB) combined.

Qwen 3.5 235B-A17B (MoE)
397B
qwen
Commercial OK

Even with CPU offload, needs more memory than your VRAM (96 GB) + 60% of system RAM (77 GB) combined.

Qwen 3 235B-A22B
235B
qwen
Commercial OK

Even with CPU offload, needs more memory than your VRAM (96 GB) + 60% of system RAM (77 GB) combined.

DeepSeek R1 (671B reasoning)
671B
deepseek
Commercial OK

Even with CPU offload, needs more memory than your VRAM (96 GB) + 60% of system RAM (77 GB) combined.

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

Even with CPU offload, needs more memory than your VRAM (96 GB) + 60% of system RAM (77 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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