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NVIDIA Quadro RTX 4000

8 GB VRAM160 W TDP0 providers0 offerings0 regions

The NVIDIA Quadro RTX 4000 is an enterprise-class GPU built on the Turing architecture, featuring 8 GB of memory with 416 GB/s of memory bandwidth and a 160 W TDP. It delivers 7.1 TFLOPS of FP32 performance.

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Hardware specifications

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Memory
8 GB
GDDR6X
What LLMs fit? ↓
TDP
160 W
Memory bandwidth
416 GB/s
Architecture
Turing
FP32 performance
7.1 TFLOPS
Brand
NVIDIA
Series
Quadro RTX
Market price history

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LLMs that fit on the NVIDIA Quadro RTX 4000

Estimated VRAM for popular open-weight LLMs at 16-bit (FP16/BF16), 8-bit (FP8/INT8) and 4-bit (INT4/MXFP4) precision, against 8 GB per GPU.

ModelParameters16-bit8-bit4-bit
Gemma 3 12B
Google
12.2B
29 GB
Too large
16 GB
Too large
9.4 GB
Tight on 1× · 2% spare
Granite 4.1 8B
IBM
8.8B
21 GB
Too large
12 GB
Too large
6.8 GB
1× · 1.2 GB free
Qwen3 8B
Alibaba
8.2B
20 GB
Too large
11 GB
Too large
6.3 GB
1× · 1.7 GB free
Llama 3.1 8B Instruct
Meta
8B
19 GB
Too large
11 GB
Too large
6.2 GB
1× · 1.8 GB free
Mistral 7B Instruct v0.3
Mistral AI
7.2B
17 GB
Too large
9.6 GB
Tight on 1× · 0% spare
5.6 GB
1× · 2.4 GB free
Qwen3 4B
Alibaba
4B
9.7 GB
Too large
5.3 GB
1× · 2.7 GB free
3.1 GB
1× · 4.9 GB free

Estimates: model weights plus 20% for KV cache and runtime overhead, at short context lengths. Long contexts and large batches need more. Rows marked “tight” hold the weights but not that full margin. A ×N figure is the total VRAM across N of these GPUs and makes no claim about interconnect throughput. How we estimate this

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Frequently asked questions

How much VRAM does the Quadro RTX 4000 have?
The Quadro RTX 4000 comes with 8 GB of VRAM, which determines the largest models and batch sizes it can hold in memory for training and inference.
Which LLMs can I run on the Quadro RTX 4000?
With 8 GB of VRAM, a single Quadro RTX 4000 can serve models such as Granite 4.1 8B, Qwen3 8B, Llama 3.1 8B Instruct, Mistral 7B Instruct v0.3. The table above lists the estimated VRAM for each model at 16-bit, 8-bit and 4-bit precision, including the multi-GPU configurations needed for larger models.