Question 1

What AI models can run on RTX Pro 6000 Blackwell?

Accepted Answer

The RTX Pro 6000 Blackwell (96 GB VRAM) can run models including gemma-4-E4B-it, gemma-4-26B-A4B-it, gemma-4-26B-A4B, gemma-3-12b-it, gemma-4-31B and more — 10 compatible models indexed so far. Compatible models are determined by whether the model's memory footprint fits within the GPU's 96 GB VRAM, potentially using quantization (Q8, Q6, or Q4) to reduce memory usage.

Question 2

What is the cheapest cloud option for RTX Pro 6000 Blackwell?

Accepted Answer

The cheapest on-demand option for the RTX Pro 6000 Blackwell is RunPod at $1.89/hr (1x_RTXPRO6000_SECURE). Spot/preemptible pricing is available at $1.19/hr. We track pricing across 26 providers to help you find the best deal.

Question 3

Is RTX Pro 6000 Blackwell good for fine-tuning?

Accepted Answer

Yes, the RTX Pro 6000 Blackwell is excellent for fine-tuning. With 96 GB of VRAM, it can handle full fine-tuning of models up to ~30B parameters and LoRA/QLoRA fine-tuning of much larger models. It's a top-tier choice for training workloads.

Question 4

How much VRAM does RTX Pro 6000 Blackwell have?

Accepted Answer

The RTX Pro 6000 Blackwell has 96 GB of VRAM (GDDR7). It also offers 1792 GB/s memory bandwidth, 251.9 TFLOPS of FP16 compute, FP8 support for efficient inference.

Question 5

What quantization formats work on RTX Pro 6000 Blackwell?

Accepted Answer

The RTX Pro 6000 Blackwell supports the following quantization formats: FP16 (full precision), FP8, Q8 (8-bit), Q6 (6-bit), Q4 (4-bit). FP16 works for models up to ~40B parameters. FP8 halves memory vs FP16 with minimal quality loss. Q4 quantization lets models up to ~326B parameters (by VRAM estimate) fit on the RTX Pro 6000 Blackwell.

Provider	On-demand	Spot	Actions
RunPod	$1.890/hr	$1.190/hr	Rent Get $5–$500 free
Amazon Web Services	$3.363/hr	$0.459/hr	—

RTX Pro 6000 Blackwell

Specifications

Cloud pricing

Compatible models

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Frequently asked questions

VRAM	96 GB
Memory type	GDDR7
Bandwidth	1792 GB/s
FP16 TFLOPS	251.9
FP8 support	Yes