About Llama 3.1 8B
Llama 3.1 8B is arguably the default small open model of its era — Meta's compact dense release that a huge share of local-AI tooling, tutorials and fine-tunes standardised on. It brought the extended context window of the Llama 3.1 generation down to a size anyone could run, under Meta's community licence: weights free to use commercially for all but the very largest platforms, subject to an acceptable-use policy.
Years of ecosystem investment are its real moat: quantisations, adapters and deployment recipes exist for practically every runtime and device class.
Llama 3.1 8B is Meta's 8.03B-parameter model released in July 2024, with a 128K-token context window. It uses a classic dense-attention design whose KV cache grows linearly with context: its KV cache is about 1.1 GB at an 8K context, 17.2 GB at 128K, and 17.2 GB at the full 128K window (FP16 cache).
This is the model VRAM anxiety was cured by: quantised builds fit comfortably on entry-level discrete GPUs and ordinary laptops, with full-precision weights within reach of a single mid-range card. Its conventional grouped-query attention means cache does grow linearly with context, and at the generation's long window that growth can eventually rival the weights themselves — the one scenario where a small model surprises people, and worth checking in the calculator above. As a first local model, or the reliable small end of a multi-model setup, it remains the path of least resistance.
For most people Q4_K_M is the sweet spot — the most popular quality/size trade-off — while Q8 is near-lossless if you have the memory. Totals above include the KV cache and a realistic framework overhead, so they are what you should expect to see in practice rather than just the download size. Weight sizes are calibrated against real GGUF files — see the methodology.
Frequently asked questions
How much VRAM does Llama 3.1 8B need?
At Q4_K_M with an 8K context, Llama 3.1 8B needs about 8 GB (weights 5 GB + KV cache + overhead). The smallest common hardware that fits is a RTX 3060 12GB.
Can an RTX 4090 (24GB) run Llama 3.1 8B?
Yes. An RTX 4090's 24 GB runs Llama 3.1 8B at FP16 / BF16 (about 20 GB at 8K context) — at full FP16 precision.
Can a Mac run Llama 3.1 8B?
Yes — Apple Silicon with 16 GB of unified memory or more (macOS lets the GPU use ~75% of it, ~12 GB) runs Llama 3.1 8B at Q4_K_M.
How much VRAM does Llama 3.1 8B need at its full 128K context?
The KV cache grows with context: about 1.1 GB at 8K tokens versus 17.2 GB at the full 128K window (FP16 cache). Add that to the weights (5 GB at Q4_K_M) plus overhead — long contexts can cost more than a whole quantisation step.
Related
VRAM calculator for any model ·Token counter
Similar-size local models: Gemma 4 E4B (8B) · DeepSeek R1 Distill Qwen3 8B (8.2B) · Gemma 4 12B (12B) · Phi-4 (14.7B) · Qwen3.5 4B (4B)
Last updated 2026-09-03. Architecture figures from the model's published config.json; see the methodology.