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NVIDIA RTX 4000 SFF Ada Generation

20 GB VRAM70 W TDP1 providers1 offerings0 regions

The NVIDIA RTX 4000 SFF Ada Generation is an enterprise-class GPU built on the Ada Lovelace architecture, featuring 20 GB of memory with 360 GB/s of memory bandwidth and a 70 W TDP. It delivers 19.8 TFLOPS of FP32 performance. Currently available from 1 provider starting at $0.44/GPU/hour, with a market median of $0.44/GPU/hour across 1 configurations.

Cheapest
$0.440/hr
Runpod
Median
$0.440/hr
across 1 offerings
Most expensive
$0.440/hr
Runpod
90-day trend
0.0%
median rate

Hardware specifications

Same across all providers

Memory
20 GB
GDDR6X
What LLMs fit? ↓
TDP
70 W
Memory bandwidth
360 GB/s
Architecture
Ada Lovelace
FP32 performance
19.8 TFLOPS
Brand
NVIDIA
Series
RTX Ada Generation
Market price history

Median price across all providers

Prices updated 2 hours ago

1 offering from 1 providers

Sorted by price ascending. Compare configurations side-by-side.

ProviderCountvCPURAMRegionPer GPU hourTotal/hrAction
Runpod logoRunpodReferral linkTrendingCheapest
Up to$500credit
×1——--
$0.440
0.0% 30d
$0.44
Launch

LLMs that fit on the NVIDIA RTX 4000 SFF Ada Generation

Estimated VRAM for popular open-weight LLMs at 16-bit (FP16/BF16), 8-bit (FP8/INT8) and 4-bit (INT4/MXFP4) precision, against 20 GB per GPU.

ModelParameters16-bit8-bit4-bit
GLM-4.7 Flash
Z.ai
31.2B
75 GB
Too large
41 GB
Too large
24 GB
Tight on 1× · 0% spare
Qwen3.6 27B
Alibaba
27.8B
67 GB
Too large
37 GB
Too large
21 GB
Tight on 1× · 12% spare
Mistral Small 3.2 24B
Mistral AI
24B
58 GB
Too large
32 GB
Too large
18 GB
1× · 1.6 GB free
gpt-oss-20b
OpenAI
21B
3.6B active
—
Not released
—
Not released
17 GB
1× · 3.5 GB free
Gemma 3 12B
Google
12.2B
29 GB
Too large
16 GB
1× · 3.9 GB free
9.4 GB
1× · 11 GB free
Granite 4.1 8B
IBM
8.8B
21 GB
Tight on 1× · 13% spare
12 GB
1× · 8.4 GB free
6.8 GB
1× · 13 GB free
Qwen3 8B
Alibaba
8.2B
20 GB
Barely fits on 1×
11 GB
1× · 9.2 GB free
6.3 GB
1× · 14 GB free
Llama 3.1 8B Instruct
Meta
8B
19 GB
Barely fits on 1×
11 GB
1× · 9.4 GB free
6.2 GB
1× · 14 GB free
Mistral 7B Instruct v0.3
Mistral AI
7.2B
17 GB
1× · 2.6 GB free
9.6 GB
1× · 10 GB free
5.6 GB
1× · 14 GB free
Qwen3 4B
Alibaba
4B
9.7 GB
1× · 10 GB free
5.3 GB
1× · 15 GB free
3.1 GB
1× · 17 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

Similar GPUs

NVIDIA RTX 4000 Ada GenerationNVIDIA RTX 2000 Ada GenerationNVIDIA L4NVIDIA RTX A4500NVIDIA RTX 5000 Ada GenerationNVIDIA A10

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Frequently asked questions

How much does the RTX 4000 SFF Ada Generation cost per hour?
The RTX 4000 SFF Ada Generation starts at $0.44 per GPU-hour, with a median of $0.44 across 1 available configurations. Actual pricing depends on the provider, region, and whether you rent on-demand, spot or reserved.
Which cloud providers offer the RTX 4000 SFF Ada Generation?
1 providers currently list the RTX 4000 SFF Ada Generation: Runpod. Compare their hourly pricing, regions and machine specs in the table above.
How much VRAM does the RTX 4000 SFF Ada Generation have?
The RTX 4000 SFF Ada Generation comes with 20 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 RTX 4000 SFF Ada Generation?
With 20 GB of VRAM, a single RTX 4000 SFF Ada Generation can serve models such as Mistral Small 3.2 24B, gpt-oss-20b, Gemma 3 12B, Granite 4.1 8B. 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.
Where is the RTX 4000 SFF Ada Generation cheapest?
The lowest on-demand price we currently track for the RTX 4000 SFF Ada Generation is $0.44 per GPU-hour at Runpod. Because providers update pricing and availability frequently, check the live table above before you launch.