Can the Arc B580 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 Arc B580 has 12GB available (6.16GB 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	12	vendor_spec
Headroom / shortfall	+6.16

Quantization Options

Quant	Weights (GB)	Total @ 4K (GB)	Total @ 32K (GB)	Fits Arc B580?
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	No

Measured Speed

41 tok/s

llama.cpp (Vulkan) · Q4_K_M · Token generation: single user, batch 1; source reports ~40–42 tok/s range (midpoint stored); Vulkan backend — SYCL/IPEX-LLM paths vary 15–38 tok/s on 14B models
Tested: 2026-05-25 · Source (Unverified community result, confidence 0.55)

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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

Same GPU, Other Models

Qwen 3 8B Stable Diffusion XL 1.0

Frequently Asked Questions

Can the Llama 3.1 8B run on 12GB VRAM?

Yes. At Q4_K_M quantization with 4K context, the Llama 3.1 8B needs approximately 5.84GB. Your 12GB of usable memory has 6.16GB headroom.

What is the maximum context length I can use on the Arc B580?

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 Arc B580?

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 · Arc B580 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.

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