Question 1

What AI models can run on RTX 6000 Ada?

Accepted Answer

The RTX 6000 Ada (48 GB VRAM) can run models including Llama-2-7b, Llama-2-7b-chat, NV-Llama2-13B-RLHF-RM, MediPhi-MedWiki, intent-aware-lfqa-qwen3-4b-baseline and more — 10 compatible models indexed so far. Compatible models are determined by whether the model's memory footprint fits within the GPU's 48 GB VRAM, potentially using quantization (Q8, Q6, or Q4) to reduce memory usage.

Question 2

What is the cheapest cloud option for RTX 6000 Ada?

Accepted Answer

We don't currently have cloud pricing data for the RTX 6000 Ada. This GPU may not be widely available from major cloud providers, or pricing data hasn't been synced yet.

Question 3

Is RTX 6000 Ada good for fine-tuning?

Accepted Answer

Yes, the RTX 6000 Ada is well-suited for fine-tuning. With 48 GB of VRAM, it can handle LoRA/QLoRA fine-tuning of models up to ~30B parameters and full fine-tuning of smaller models (up to ~7B parameters). For larger models, consider multi-GPU setups.

Question 4

How much VRAM does RTX 6000 Ada have?

Accepted Answer

The RTX 6000 Ada has 48 GB of VRAM (GDDR6). It also offers 960 GB/s memory bandwidth, 182.6 TFLOPS of FP16 compute, FP8 support for efficient inference.

Question 5

What quantization formats work on RTX 6000 Ada?

Accepted Answer

The RTX 6000 Ada supports the following quantization formats: FP16 (full precision), FP8, Q8 (8-bit), Q6 (6-bit), Q4 (4-bit). FP16 works for models up to ~7B parameters. FP8 halves memory vs FP16 with minimal quality loss. Q4 quantization lets models up to ~163B parameters (by VRAM estimate) fit on the RTX 6000 Ada.

RTX 6000 Ada

Specifications

Cloud pricing

Compatible models

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Frequently asked questions

VRAM	48 GB
Memory type	GDDR6
Bandwidth	960 GB/s
FP16 TFLOPS	182.6
FP8 support	Yes