Compatibility check
Can you run Gemma 2 9B Instruct on the NVIDIA L4?
Yes, with quantization. Gemma 2 9B Instruct does not fit the NVIDIA L4 (24 GB) in FP16, but it runs at INT8 (8-bit) using about 11.9 GB (50% of VRAM). Use a GPTQ, AWQ, or GGUF build to get there, and keep prompts moderate to leave room for the KV cache.
Memory breakdown
Weights plus a 1.3 GB KV cache at 4,096tokens, against the card's 24 GB. Verdicts leave ~10% headroom for activations and fragmentation.
| Precision | Weights | KV cache | Total | % of 24 GB | Fit |
|---|---|---|---|---|---|
| FP16 / BF16full quality | 21.3 GB | 1.3 GB | 22.6 GB | 94% | Tight |
| INT8 (8-bit)near-full quality | 10.6 GB | 1.3 GB | 11.9 GB | 50% | Fits |
| INT4 (4-bit)GPTQ / AWQ / GGUF Q4 | 5.3 GB | 1.3 GB | 6.6 GB | 27% | Fits |
Planning estimates, not a substitute for profiling. Real usage varies with the inference runtime, batch size, and how much context you actually use — the KV cache grows linearly with prompt length.
GPUs that run Gemma 2 9B Instruct
Cards where this model fits (at its best precision):
Models that fit the NVIDIA L4
Other popular models that run on this card:
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Frequently asked questions
Can the NVIDIA L4 run Gemma 2 9B Instruct?
Not in FP16, but yes at INT8 (8-bit), where it uses about 11.9 GB versus the card's 24 GB.
How much VRAM does Gemma 2 9B Instruct need?
Approximately 21.3 GB in FP16, 10.6 GB in INT8, and 5.3 GB in 4-bit for the weights, plus a KV cache of about 1.3 GB at 4,096 tokens.
Does quantization let Gemma 2 9B Instruct fit on the NVIDIA L4?
Yes. Dropping to INT8 (8-bit) brings total usage to about 11.9 GB, which fits the 24 GB card with headroom for the KV cache.
What happens to memory with longer context?
The KV cache grows linearly with prompt length. At 4,096 tokens it is about 1.3 GB here; doubling the context roughly doubles that term, so long-context use can push a tight fit over the edge.