What can NVIDIA RTX PRO 6000 Blackwell run for chat?

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

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

Runs comfortably
148 models

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

#1Qwen 3 0.6B
0.6B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 2.4 GBHeadroom: 93.6 GB
2692
tok/s
Estimated
Weights
0.30 GB
KV cache
0.30 GB
Activations
0.02 GB
Runtime
1.80 GB
#2Llama 3.2 3B Instruct
3B
llama
Commercial OK
Quant: Q8_0Context: 8,192VRAM: 6.9 GBHeadroom: 89.1 GB
ollama run llama3.2:3b
306
tok/s
Estimated
Weights
3.40 GB
KV cache
1.50 GB
Activations
0.18 GB
Runtime
1.80 GB
#3Qwen 3 1.7B
1.7B
qwen
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 3.6 GBHeadroom: 92.4 GB
950
tok/s
Estimated
Weights
0.90 GB
KV cache
0.85 GB
Activations
0.05 GB
Runtime
1.80 GB
#4TinyLlama 1.1B Chat v1.0
1.1B
llama
Commercial OK
Quant: Q4_K_MContext: 2,048VRAM: 2.6 GBHeadroom: 93.4 GB
1468
tok/s
Estimated
Weights
0.60 GB
KV cache
0.14 GB
Activations
0.03 GB
Runtime
1.80 GB
#5Gemma 2 2B Instruct
2B
gemma
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 4.0 GBHeadroom: 92.0 GB
807
tok/s
Estimated
Weights
1.10 GB
KV cache
1.00 GB
Activations
0.06 GB
Runtime
1.80 GB
#6Mistral 7B Instruct v0.3
7B
mistral
Commercial OK
Quant: Q5_K_MContext: 8,192VRAM: 10.7 GBHeadroom: 85.3 GB
ollama run mistral:7b
203
tok/s
Estimated
Weights
5.10 GB
KV cache
3.50 GB
Activations
0.26 GB
Runtime
1.80 GB
#7DeepSeek V2 Lite Chat
15.7B
deepseek
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 18.7 GBHeadroom: 77.3 GB
673
tok/s
Estimated
Weights
8.60 GB
KV cache
7.85 GB
Activations
0.44 GB
Runtime
1.80 GB
Quant: Q4_K_MContext: 4,096VRAM: 7.6 GBHeadroom: 88.4 GB
231
tok/s
Estimated
Weights
3.90 GB
KV cache
1.75 GB
Activations
0.20 GB
Runtime
1.80 GB
#9Kumru 2B
2.4B
mistral
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 4.6 GBHeadroom: 91.4 GB
ollama run alibayram/kumru:latest
673
tok/s
Estimated
Weights
1.50 GB
KV cache
1.20 GB
Activations
0.08 GB
Runtime
1.80 GB
#10Turkcell LLM 7B v1
7.4B
mistral
Commercial OK
Quant: Q4_K_MContext: 8,192VRAM: 10.2 GBHeadroom: 85.8 GB
ollama run RefinedNeuro/Turkcell-LLM-7b-v1:latest
218
tok/s
Estimated
Weights
4.50 GB
KV cache
3.70 GB
Activations
0.23 GB
Runtime
1.80 GB
Quant: Q4_0Context: 8,192VRAM: 9.7 GBHeadroom: 86.3 GB
ollama run brooqs/mistral-turkish-v2:latest
241
tok/s
Estimated
Weights
4.10 GB
KV cache
3.60 GB
Activations
0.21 GB
Runtime
1.80 GB
#12Malhajar Mistral 7B Turkish
7.2B
mistral
Commercial OK
Quant: Q5_K_MContext: 8,192VRAM: 10.8 GBHeadroom: 85.2 GB
ollama run koezgen/malhajar-mistral-tur7b:latest
197
tok/s
Estimated
Weights
5.10 GB
KV cache
3.60 GB
Activations
0.26 GB
Runtime
1.80 GB

Runs with tradeoffs
4 models

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

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
28
tok/s
Estimated
Weights
134.00 GB
KV cache
29.50 GB
Activations
6.70 GB
Runtime
1.80 GB
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
28
tok/s
Estimated
Weights
134.00 GB
KV cache
29.50 GB
Activations
6.70 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
13
tok/s
Estimated
Weights
73.00 GB
KV cache
15.38 GB
Activations
3.65 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
4
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

Check the current price

Adds 32 GB to your CPU-offload working set. Helps when models don't quite fit in VRAM.

Unlocks: 5 new comfortable, 6 new tradeoff

  • • Gemma 3 270M
  • • SmolLM2 135M Instruct
  • • SmolLM2 360M Instruct
  • • VBART Large (Turkish Summarization)

Upgrade to NVIDIA H200

Launch MSRP $31,000 (2024). Check the current price.

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

Unlocks: 6 new comfortable

  • • Gemma 3 270M
  • • SmolLM2 135M Instruct
  • • Mixtral 8x22B Instruct
  • • SmolLM2 360M Instruct

Add a second NVIDIA RTX PRO 6000 Blackwell

Launch MSRP $8,499 (2025). Check the current price.

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: 11 new comfortable

  • • Qwen 3 235B-A22B
  • • Gemma 3 270M
  • • SmolLM2 135M Instruct
  • • Llama 3.1 Nemotron Ultra 253B

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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 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.

—
DeepSeek V3 (671B MoE)
671B
deepseek
Commercial OK

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

—
Cloud GPU rental

Not enough VRAM for these models? Rent a GPU by the hour

The models above need more memory than 96 GB VRAM + 128 GB system RAM. You can rent a GPU with enough VRAM by the hour to run one of them, or to test it before you buy hardware. For daily use, owning a card usually costs less over time.

RunPod

billed per GPU-hour

Hourly GPU pods, in community-cloud and secure-cloud tiers.

The two tiers run on different hosts and terms. Check which tier a pod is on before a long job.

Rent on RunPod

Vast.ai

billed per GPU-hour

Marketplace of host-listed GPUs; price and reliability vary by host.

Host quality varies. Filter for verified hosts and check the host's reliability before a long run.

Rent on Vast.ai

Referral links: RunLocalAI may earn a commission or account credits if you sign up and rent, at no extra cost to you. Hourly rates and which GPUs are listed change all the time, so check the provider's page before you start. Editorial opinions are independent of the referral relationship; see how we make money.

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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