Can the GeForce RTX 4090 run Gemma 3 27B?
Gemma 3 27B runs locally on a 24 GB GPU at Q4_K_M, which needs about 20.2 GB of VRAM with a 4k-token context. A 32 GB or larger GPU is needed for comfortable 32k-token use, because the upper-bound KV cache grows to 16.6 GB at 32k tokens.
At Q4_K_M quantization with 131,072 context, the Gemma 3 27B requires approximately 84.65GB of VRAM. The GeForce RTX 4090 has 24GB available (60.65GB shortfall).
VRAM Breakdown
| Component | Size (GB) | Source |
|---|---|---|
| Model weights (Q4_K_M) | 16.44 | calculated |
| Loading overhead (10%) | 1.64 | calculated |
| KV cache @ 131,072 context | 66.57 | calculated |
| Total required | 84.65 | |
| GPU memory available | 24 | vendor_spec |
| Headroom / shortfall | -60.65 |
Quantization Options
| Quant | Weights (GB) | Total @ 4K (GB) | Total @ 32K (GB) | Fits GeForce RTX 4090? |
|---|---|---|---|---|
| Q4_K_M | 16.44 | 20.16 | 34.72 | Yes |
| Q5_K_M | 19.52 | 23.55 | 38.11 | Yes |
| Q6_K | 22.61 | 26.95 | 41.51 | No |
| Q8_0 | 29.11 | 34.1 | 48.66 | No |
| FP16 | 54.8 | 62.36 | 76.92 | No |
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Frequently Asked Questions
Can the Gemma 3 27B run on 24GB VRAM?
No. The Gemma 3 27B requires approximately 84.65GB at Q4_K_M quantization with 4K context. 24GB is not enough.
What is the maximum context length I can use on the GeForce RTX 4090?
The Gemma 3 27B supports up to 131,072 tokens of context. Larger context increases KV-cache memory requirements. At 4K context the model needs ~84.65GB; at 32K it needs more.
Which quantization should I use with the GeForce RTX 4090?
Q4_K_M is the recommended starting point — it offers the best speed-to-quality tradeoff. If you have VRAM headroom, Q6_K or Q8_0 improves quality at the cost of speed. FP16 is only for inference servers with ample memory.
How large is the Gemma 3 27B Q4_K_M file on disk?
The gguf Q4_K_M artifact is 16.55 GB on disk (ai_model_artifacts.size_bytes, bartowski/google_gemma-3-27b-it-GGUF). That is a download size, not runtime VRAM. Calculated runtime VRAM at Q4_K_M with 4K context is 20.16 GB (ModelFitService weights + 10% overhead + KV cache).
How much calculated VRAM does Gemma 3 27B need on a 24 GB GPU?
Calculated VRAM is 20.16 GB at Q4_K_M with 4K context versus 24 GB vendor_spec VRAM on GeForce RTX 4090 (products.specifications.vram_gb). Headroom is 3.84 GB. No tokens-per-second figure is shown: no exact product x model benchmark row.
Related Pages
Full Gemma 3 27B fit page · All AI models · GeForce RTX 4090 specs · VRAM calculator
VRAM figures are calculated from model architecture parameters using established formulas. Benchmark data is measured and cited per row. GPU memory is vendor_spec from manufacturer datasheets.