Can the RTX A6000 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.

Fits comfortably

At Q4_K_M quantization with 4,096 context, the Llama 3.1 8B requires approximately 5.84GB of VRAM. The RTX A6000 has 48GB available (42.16GB headroom).

4,096 tokens 16,384 tokens 32,768 tokens 131,072 tokens

VRAM Breakdown

Component	Size (GB)	Source
Model weights (Q4_K_M)	4.82	calculated
Loading overhead (10%)	0.48	calculated
KV cache @ 4,096 context	0.54	calculated
Total required	5.84
GPU memory available	48	vendor_spec
Headroom / shortfall	+42.16

Quantization Options

Quant	Weights (GB)	Total @ 4K (GB)	Total @ 32K (GB)	Fits RTX A6000?
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

102.22 tok/s

llama.cpp (CUDA, LLAMA_CUBLAS build) · Q4_K_M · Token generation: average speed generating 1024 tokens, batch 1, -ngl 10000 full offload, RunPod; model is Meta-Llama-3-8B (not 3.1); 2024-era build — cross-check: same repo's 4090=127.74 vs myaihardware b3500 125 (+2%); tested_at = repo last push containing Llama-3 results (exact run date unstated)
Tested: 2024-05-13 · Source (Reproducible community benchmark, confidence 0.70)

Get the RTX A6000

Check current price on Amazon

Affiliate link — we earn a commission on qualifying purchases at no cost to you.

If It Doesn't Fit: Alternatives

GeForce RTX 5060 8GB VRAM

GeForce RTX 5050 8GB VRAM

GeForce RTX 4060 8GB VRAM

GeForce RTX 3070 8GB VRAM

Radeon RX 7600 8GB VRAM

Arc A750 8GB VRAM

Frequently Asked Questions

Can the Llama 3.1 8B run on 48GB VRAM?

Yes. At Q4_K_M quantization with 4K context, the Llama 3.1 8B needs approximately 5.84GB. Your 48GB of usable memory has 42.16GB headroom.

What is the maximum context length I can use on the RTX A6000?

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 ~5.84GB; at 32K it needs more.

Which quantization should I use with the RTX A6000?

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.

Full Llama 3.1 8B fit page · All AI models · RTX A6000 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.

Methodology · Editorial policy · Affiliate disclosure