Llama 3.1 8B Instruct
Llama 3.1 8B Instruct loads in 6.5 GB at Q4_K_M, comfortably from 16 GB of memory — Macs included, starting at the Mac Mini M6 16GB. Below is the memory math we ran for it, the cheapest card that fits, and the fastest machine we track.
How much memory does Llama 3.1 8B Instruct need?
Llama 3.1 8B Instruct is a 8B dense model built for chat, coding. The weights alone take 6.5 GB at Q4_K_M; add context and the OS, and it belongs in the 16 GB memory class. Every number below comes from the ModelFit engine — speeds are estimates, labeled est., because we do not benchmark hardware ourselves.
KV = fp16 estimate (q8_0 cache roughly halves it). "Comfortable" = weights + KV within the engine's tiered budget (~70-85% of memory).
Which machines run Llama 3.1 8B Instruct?
Verdicts come from the same engine that powers our fit checker — memory capacity first, bandwidth for speed. How we compute these numbers · Hugging Face model card
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Frequently asked questions
How much memory does Llama 3.1 8B Instruct need?
6.5 GB for the Q4_K_M weights, plus ~2.0 GB of KV-cache at 16k context — about 8.5 GB total. Comfortable from 16 GB of VRAM or unified memory.
Does Llama 3.1 8B Instruct run on a Mac?
Yes — from the Mac Mini M6 16GB (~28 tok/s est.). Unified memory means the RAM budget is the only limit.
What is the cheapest GPU for Llama 3.1 8B Instruct?
The AMD Radeon RX 7900 XT is the cheapest tracked card that runs Llama 3.1 8B Instruct comfortably — ~74 tok/s est. at ~$550 used.
Which Llama 3.1 8B Instruct quant should I pick?
The tracked Q4_K_M build at 6.5 GB is the one we recommend for most machines. Every published build, ranked by real GGUF file size rather than a formula, is on the quant comparison page linked below.
Cite this page
ModelFit: Llama 3.1 8B Instruct — specs, memory math and hardware verdicts. https://modelfit.io/models/llama3.1-8b/ (dataset updated 2026-09-03, CC BY 4.0).