⚠️Tight fitfp16 · 4K ctx · batch 1

Run Gemma 2 9B on RTX 4090

At fp16 precision, Gemma 2 9B (9.0 B parameters) needs roughly 20.9 GB of VRAM. RTX 4090 has 24 GB — fits, but only 3.1 GB spare. You'll want shorter sequences, batch 1, or INT8 quantization (6.2 GB at INT4).

87%
VRAM
Tight fit
Estimated VRAM (fp16)
20.9 GB
of 24 GB on RTX 4090· 3.1 GB free
Tok/sec
88
TFTT
760ms
Power
174W
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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 4090?
FP16 (full precision)20.9 GB87%⚠️Tight fit
INT8 (8-bit)11.1 GB46%Runs easily
INT4 / Q4 (4-bit)6.2 GB26%Runs easily

Frequently asked

Can RTX 4090 run Gemma 2 9B?
Yes. Gemma 2 9B needs ≈ 20.9 GB VRAM at fp16 and RTX 4090 provides 24 GB. Expected throughput is 88 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 4090?
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 4090, enable Flash Attention 2 and INT8/INT4 quantization to leave headroom for batching.
Where can I rent a RTX 4090?
GPUniq aggregates live RTX 4090 offers from verified providers. You can deploy an instance in about a minute and pay hourly with no commitment.
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Chat with Gemma 2 9B — no setup

Send a prompt and see how Gemma 2 9B responds directly in our chat. No installation, no GPU required to test.

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Want to tweak sequence length, batch size, or fine-tuning? Open the full calculator →