Can the GeForce RTX 4090 run Llama 3.1 8B?
You can run Llama 3.1 8B locally on a GPU with 8 GB of VRAM at Q4_K_M quantization using a 4k-token context, which needs about 5.8 GB of VRAM. A 12 GB GPU such as the GeForce RTX 3060 12GB or RTX 4070 runs the model comfortably, and 32k-token contexts need about 9.6 GB of VRAM at Q4_K_M.
At Q4_K_M quantization with 16,384 context, the Llama 3.1 8B requires approximately 7.45GB of VRAM. The GeForce RTX 4090 has 24GB available (16.55GB headroom).
VRAM Breakdown
| Component | Size (GB) | Source |
|---|---|---|
| Model weights (Q4_K_M) | 4.82 | calculated |
| Loading overhead (10%) | 0.48 | calculated |
| KV cache @ 16,384 context | 2.15 | calculated |
| Total required | 7.45 | |
| GPU memory available | 24 | vendor_spec |
| Headroom / shortfall | +16.55 |
Quantization Options
| Quant | Weights (GB) | Total @ 4K (GB) | Total @ 32K (GB) | Fits GeForce RTX 4090? |
|---|---|---|---|---|
| Q4_K_M | 4.82 | 5.84 | 9.59 | Yes |
| Q5_K_M | 5.72 | 6.83 | 10.58 | Yes |
| Q6_K | 6.62 | 7.82 | 11.57 | Yes |
| Q8_0 | 8.53 | 9.92 | 13.67 | Yes |
| FP16 | 16.06 | 18.21 | 21.96 | Yes |
Measured Speed
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Frequently Asked Questions
Can the Llama 3.1 8B run on 24GB VRAM?
Yes. At Q4_K_M quantization with 4K context, the Llama 3.1 8B needs approximately 7.45GB. Your 24GB of usable memory has 16.55GB headroom.
What is the maximum context length I can use on the GeForce RTX 4090?
The Llama 3.1 8B supports up to 131,072 tokens of context. Larger context increases KV-cache memory requirements. At 4K context the model needs ~7.45GB; 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.
Can a 6 GB GPU run Llama 3.1 8B?
No, 6 GB is not enough at Q4_K_M with a 4k context, which needs about 5.8 GB plus room for the desktop and compute buffers. A 6 GB GPU must offload some layers to system RAM, which slows generation to a few tokens per second.
How much VRAM does Llama 3.1 8B need at Q4_K_M?
Llama 3.1 8B needs about 5.8 GB of VRAM at Q4_K_M with a 4k-token context and about 9.6 GB with a 32k-token context, calculated as 4.82 GB of weights plus 10% loading overhead plus the KV cache.
Related Pages
Full Llama 3.1 8B 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.