Gemma 2 9B VRAM: what it needs at Q4_K_M, Q8_0 and FP16
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KV cache is counted at FP16. Overhead is a fixed 1.5 GiB estimate for the runtime and compute buffers. A custom size borrows the architecture of the nearest preset.
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GPU tiers
- 8 GiBtoo smallRTX 4060, RTX 3070, RX 7600
- 12 GiBfitsRTX 4070, RTX 3060 12GB, RTX 5070
- 16 GiBfitsRTX 4060 Ti 16GB, RTX 4080, RTX 5080
- 24 GiBfitsRTX 4090, RTX 3090, RX 7900 XTX
- 32 GiBfitsRTX 5090, V100 32GB
- 48 GiBfitsRTX A6000, RTX 6000 Ada, L40S
- 80 GiBfitsA100 80GB, H100 80GB
At Q4_K_M with an 8,192-token context, Gemma 2 9B needs about 9.25 GiB of VRAM: 5.12 GiB of weights, 2.63 GiB of KV cache and 1.50 GiB of runtime overhead. The smallest common GPU tier that holds it is 12 GiB (RTX 4070, RTX 3060 12GB, RTX 5070).
How much VRAM Gemma 2 9B needs
Gemma 2 9B, released by Google in June 2024, punches above its size on chat benchmarks; Gemma 3 has since superseded it, but it remains common in local setups as of October 2026.
With 8,192 tokens of context the estimate is 20.9 GiB at FP16, 13.0 GiB at Q8_0 and 9.25 GiB at Q4_K_M. FP16 needs a 24 GiB card, Q8_0 needs a 16 GiB card, and Q4_K_M needs a 12 GiB card. The weights are 9 billion parameters times the bits per weight of each format (16, 8.5 and 4.89), divided by eight; everything is in GiB, the unit GPU memory is sold in.
Context length and the KV cache
The KV cache stores a key and a value vector for every token in every layer. Gemma 2 9B has 42 layers, 8 KV heads and a head dimension of 256, read from its Hugging Face config.json, so at FP16 each token costs 336 KiB. That is 2.63 GiB at 8,192 tokens, which is also the model's maximum context. The cache does not shrink with weight quantization; only a runtime option that quantizes the cache itself does.
Which quantization to pick
Q4_K_M at 9.25 GiB needs a 12 GiB card, slightly more than other 7-9B models; Q8_0 at 13.0 GiB needs a 16 GiB card, and FP16 at 20.9 GiB needs a 24 GiB card.
Pitfalls
Gemma 2 is capped at 8,192 tokens, so the default context on this page is already the maximum. Its head dimension of 256 makes the cache unusually heavy at 336 KiB per token. Half its layers use a 4,096-token sliding window; this estimate allocates a full cache for every layer, so runtimes that exploit the window need less.
Gemma 2 27B is the bigger sibling; Llama 3.1 8B and Qwen2.5 7B fit a smaller card at the same context.
FAQ
Questions, answered.
Tap a question to expand the answer.
Can Gemma 2 9B run on an 8 GB GPU?
At its full 8,192 tokens the Q4_K_M estimate is 9.25 GiB, over 8 GiB, mostly because the cache is 2.63 GiB. At 4,096 tokens it drops to 7.94 GiB and fits.
Why does Gemma 2 9B need more VRAM than Llama 3.1 8B?
Its cache costs 336 KiB per token against 128 KiB for Llama 3.1 8B, because it has 42 layers and a head dimension of 256. At 8,192 tokens that is 2.63 GiB of cache.
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Estimates only; the KV cache is sized at FP16 and the overhead is a fixed allowance, so always confirm against your own runtime’s report before buying hardware. OpsCanopy is free and open.