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NVIDIA A100 80GB

80 GB VRAM312 FP16 TFLOPS400 W TDP15 providers83 offerings13 regions

The NVIDIA A100 80GB is an enterprise-class GPU built on the Ampere architecture, featuring 80 GB of memory with 2,000 GB/s of memory bandwidth and a 400 W TDP. It delivers 19.5 TFLOPS of FP32 performance and 312 TFLOPS of FP16 tensor performance. Currently available from 15 providers starting at $0.27/GPU/hour, with a market median of $1.49/GPU/hour across 83 configurations.

A100 80GB · PCIe reference design

A100 80GB · PCIe reference design

PCIe illustration. Listed configurations may use SXM or other variants.

Cheapest
$0.270/hr
Vast.ai
Median
$1.490/hr
across 83 offerings
Most expensive
$3.608/hr
Sesterce Cloud
90-day trend
+4.2%
median rate

Hardware specifications

Same across all providers

Memory
80 GB
HBM2e
What LLMs fit? ↓
FP16 performance
312 TFLOPS
peak
TDP
400 W
Memory bandwidth
2,000 GB/s
Architecture
Ampere
FP32 performance
19.5 TFLOPS
Brand
NVIDIA
Series
A100
Market price history

Median price across all providers

Prices updated

15 offerings from 15 providers

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

ProviderCountvCPURAMRegionPer GPU hourTotal/hrAction
RunpodPromoted
Up to$500creditAffiliate promotion — we may earn a commission.
×116117 GB
IcelandUnited StatesRomania
$1.790$1.79Launch
Vast.aiReferral linkTrendingCheapest
×132188 GB
United StatesUnited KingdomJapan+5
From$0.270
-15.6% 30d
From$0.27
Launch
Up to$100credit
×1——
United StatesRussia
From$1.070
From$1.07
Launch
Thunder ComputeReferral link
Up to$20student credit
×1864 GB--
From$1.090
0.0% 30d
From$1.09
Launch
×82521.88 TB
CanadaUnited States
From$1.400
+3.7% 30d
From$11.20
Launch
×82401.25 TB
United States
From$1.418
+25.0% 30d
From$11.34
Launch
Sesterce CloudReferral link
×82401.25 TB
United StatesCanadaIndia
From$1.485
+1.0% 30d
From$11.88
Launch
×116120 GB
United States
$1.490
0.0% 30d
From$1.49
Launch
OblivusReferral link
×82521.88 TB
United States
From$1.570
0.0% 30d
From$12.56
Launch
×116120 GB
United States
From$1.700
-9.5% 30d
From$1.70
Launch
×116160 GB
United States
$1.760
+18.1% 30d
From$1.76
Launch
RunpodReferral linkTrending
Up to$500credit
×116117 GB
IcelandUnited StatesRomania+1
$1.790
0.0% 30d
$1.79
Launch
×122120 GB--
From$1.927
+11.0% 30d
From$1.93
Launch
Up to$1credit
×864512 GB
India
From$1.985
-1.1% 30d
From$15.88
Launch
×130200 GB
IndiaUnited StatesGermany+1
From$1.990
-16.7% 30d
From$1.99
Launch
×11080 GB--
$1.990
From$1.99
Launch

LLMs that fit on the NVIDIA A100 80GB

Estimated VRAM for popular open-weight LLMs at 16-bit (FP16/BF16), 8-bit (FP8/INT8) and 4-bit (INT4/MXFP4) precision, against 80 GB per GPU. Showing the 12 largest that fit.

ModelParameters16-bit8-bit4-bit
Kimi K2.5
Moonshot AI
1T
32B active
—
Not released
—
Not released
714 GB
Tight on 8× · 7% spare
GLM-5.2
Z.ai
753B
—
Not released
994 GB
Too large
579 GB
8× · 61 GB free
DeepSeek R1
DeepSeek
671B
37B active
—
Not released
826 GB
Too large
481 GB
8× · 159 GB free
DeepSeek V4 Flash
DeepSeek
284B
—
Not released
—
Not released
192 GB
4× · 128 GB free
or 2× · 0% spare
Solar Open2 250B
Upstage
250B
15B active
601 GB
8× · 39 GB free
330 GB
8× · 310 GB free
or 4× · 16% spare
192 GB
4× · 128 GB free
Qwen3 235B-A22B
Alibaba
235B
22B active
564 GB
8× · 76 GB free
310 GB
4× · 9.7 GB free
181 GB
4× · 139 GB free
or 2× · 6% spare
Laguna-S 2.1
Poolside
118B
8B active
282 GB
4× · 38 GB free
155 GB
2× · 4.8 GB free
90 GB
2× · 70 GB free
or 1× · 6% spare
gpt-oss-120b
OpenAI
117B
5.1B active
—
Not released
—
Not released
78 GB
1× · 1.7 GB free
Qwen3.6 35B-A3B
Alibaba
35B
3B active
86 GB
2× · 74 GB free
or 1× · 11% spare
47 GB
1× · 33 GB free
28 GB
1× · 52 GB free
Qwen3 32B
Alibaba
32.8B
79 GB
1× · 1.4 GB free
43 GB
1× · 37 GB free
25 GB
1× · 55 GB free
Gemma 4 31B
Google
31.3B
75 GB
1× · 4.9 GB free
41 GB
1× · 39 GB free
24 GB
1× · 56 GB free
GLM-4.7 Flash
Z.ai
31.2B
75 GB
1× · 5.1 GB free
41 GB
1× · 39 GB free
24 GB
1× · 56 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 · Start from a model instead

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Frequently asked questions

How much does the A100 80GB cost per hour?
The A100 80GB starts at $0.27 per GPU-hour, with a median of $1.49 across 83 available configurations. Actual pricing depends on the provider, region, and whether you rent on-demand, spot or reserved.
Which cloud providers offer the A100 80GB?
16 providers currently list the A100 80GB: Runpod, Sesterce Cloud, Hyperstack, Theta EdgeCloud, Oblivus, Verda and 10 more. Compare their hourly pricing, regions and machine specs in the table above.
How much VRAM does the A100 80GB have?
The A100 80GB comes with 80 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 A100 80GB?
With 80 GB of VRAM, a single A100 80GB can serve models such as gpt-oss-120b, Qwen3.6 35B-A3B, Qwen3 32B, Gemma 4 31B. 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.
What's the biggest LLM I can run on multiple A100 80GB GPUs?
The largest models we track that fit across several A100 80GB cards are GLM-5.2 on 8 GPUs at 4-bit, DeepSeek R1 on 8 GPUs at 4-bit, DeepSeek V4 Flash on 4 GPUs at 4-bit. A multi-GPU configuration means you rent and pay for every card for the whole run, and splitting a model across cards costs throughput as well: the GPUs exchange activations on every token, so performance depends on how fast they are wired together. We do not track interconnect, so check the provider's node specification. The table above gives the smallest configuration for each model at 16-bit, 8-bit and 4-bit precision.
Where is the A100 80GB cheapest?
The lowest on-demand price we currently track for the A100 80GB is $0.27 per GPU-hour at Vast.ai. Because providers update pricing and availability frequently, check the live table above before you launch.