✅Runs easilyfp16 · 4K ctx · batch 1

Run Gemma 2 9B on RTX A6000

At fp16 precision, Gemma 2 9B (9.0 B parameters) needs roughly 20.9 GB of VRAM. RTX A6000 has 48 GB, leaving 27.1 GB of headroom — plenty of room for longer contexts and larger batch sizes. Expected throughput ≈ 77 tokens/sec on a single card.

44%
VRAM
Runs easily
Estimated VRAM (fp16)
20.9 GB
of 48 GB on RTX A6000· 27.1 GB free
Tok/sec
77
TFTT
868ms
Power
116W
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RTX A6000
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Memory breakdown (fp16)

Model Weights
18.00 GB
KV Cache
0.37 GB
Activations
1.48 GB
Framework Overhead
1.04 GB

Quantization comparison

Lower precision = less VRAM with a small quality trade-off. Quality order: FP16 > INT8 > INT4.

PrecisionVRAMUtilisationFits on RTX A6000?
FP16 (full precision)20.9 GB44%✅Runs easily
INT8 (8-bit)11.1 GB23%✅Runs easily
INT4 / Q4 (4-bit)6.2 GB13%✅Runs easily

Frequently asked

Can RTX A6000 run Gemma 2 9B?
Yes. Gemma 2 9B needs ≈ 20.9 GB VRAM at fp16 and RTX A6000 provides 48 GB. Expected throughput is 77 tokens/sec per GPU.
How much VRAM does Gemma 2 9B use?
About 20.9 GB at fp16, 6.2 GB at INT4 (for a 4K context, batch size 1). Longer contexts add to KV cache size; larger batches increase activations.
What's the fastest way to run Gemma 2 9B on RTX A6000?
Use a production inference engine like vLLM, SGLang, or TensorRT-LLM with paged attention — they cut KV cache 2–3× vs the naive estimate and batch multiple requests efficiently. For RTX A6000, enable Flash Attention 2.
Where can I rent a RTX A6000?
GPUniq aggregates live RTX A6000 offers from verified providers. You can deploy an instance in about a minute and pay hourly with no commitment.

Other GPUs for Gemma 2 9B

Other models on RTX A6000

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