NVIDIA A10G

24 GB VRAM70 FP16 TFLOPS300 W TDP0 providers0 offerings0 regions

The NVIDIA A10G is an enterprise-class GPU built on the Ampere architecture, featuring 24 GB of memory with 600 GB/s of memory bandwidth and a 300 W TDP. It delivers 35 TFLOPS of FP32 performance and 70 TFLOPS of FP16 tensor performance.

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90-day trend

+33.3%

median rate

Hardware specifications

Same across all providers

Memory

24 GB

What LLMs fit? ↓

FP16 performance

70 TFLOPS

peak

TDP

300 W

Memory bandwidth

600 GB/s

Architecture

Ampere

FP32 performance

35 TFLOPS

Brand

NVIDIA

Series

A10

Market price history

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Provider	Count	vCPU	RAM	Region	Per GPU hour	Total/hr	Action
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LLMs that fit on the NVIDIA A10G

Estimated VRAM for popular open-weight LLMs at 16-bit (FP16/BF16), 8-bit (FP8/INT8) and 4-bit (INT4/MXFP4) precision, against 24 GB per GPU. Showing the 12 largest that fit.

Model	Parameters	16-bit	8-bit	4-bit
Qwen3.6 35B-A3B Alibaba	35B 3B active	86 GB Too large	47 GB Too large	28 GB Tight on 1× · 4% spare
Qwen3 32B Alibaba	32.8B	79 GB Too large	43 GB Too large	25 GB Tight on 1× · 14% spare
Gemma 4 31B Google	31.3B	75 GB Too large	41 GB Too large	24 GB Tight on 1× · 19% spare
GLM-4.7 Flash Z.ai	31.2B	75 GB Too large	41 GB Too large	24 GB Barely fits on 1×
Qwen3.6 27B Alibaba	27.8B	67 GB Too large	37 GB Too large	21 GB 1× · 2.7 GB free
Mistral Small 3.2 24B Mistral AI	24B	58 GB Too large	32 GB Too large	18 GB 1× · 5.6 GB free
gpt-oss-20b OpenAI	21B 3.6B active	— Not released	— Not released	17 GB 1× · 7.5 GB free
Gemma 3 12B Google	12.2B	29 GB Too large	16 GB 1× · 7.9 GB free	9.4 GB 1× · 15 GB free
Granite 4.1 8B IBM	8.8B	21 GB 1× · 2.9 GB free	12 GB 1× · 12 GB free	6.8 GB 1× · 17 GB free
Qwen3 8B Alibaba	8.2B	20 GB 1× · 4.3 GB free	11 GB 1× · 13 GB free	6.3 GB 1× · 18 GB free
Llama 3.1 8B Instruct Meta	8B	19 GB 1× · 4.7 GB free	11 GB 1× · 13 GB free	6.2 GB 1× · 18 GB free
Mistral 7B Instruct v0.3 Mistral AI	7.2B	17 GB 1× · 6.6 GB free	9.6 GB 1× · 14 GB free	5.6 GB 1× · 18 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 VRAM does the A10G have?

The A10G comes with 24 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 A10G?

With 24 GB of VRAM, a single A10G can serve models such as GLM-4.7 Flash, Qwen3.6 27B, Mistral Small 3.2 24B, gpt-oss-20b. 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.