Best home rig for Llama 3.1 8B
Older model. For current picks see 2025–2026 models.
Real, measured generation speeds for Llama 3.1 8B on home hardware, fastest first. Different quants/engines aren't directly comparable.
| Build | Quant | Engine | tok/s | Cost | |
|---|---|---|---|---|---|
| HP OmniBook Ultra 16 (RTX Spark N1X, 128GB) laptop: local AI benchmarks | MLPerf Client v2.0 (geomean) | llama.cpp CUDA | 56.5 | $6,230 (≈€5,559) | source |
| RTX 4090 desktop (speed comparison vs Macs) | Q8 | llama.cpp-based | 52.99 | source | |
| Mac Studio M1 Ultra | Q8 | llama.cpp-based | 37.92 | source | |
| MacBook Pro M2 Max | Q8 | llama.cpp-based | 28.92 | source |
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Rent before you buy: try this model on a cloud GPU for a few dollars first: RunPod
FAQ
How much VRAM does Llama 3.1 8B need?
The weights are about 4.9 GB at Q4_K_M (8B params × 4.89 bits ÷ 8, before context/KV cache). Add memory for context (KV cache) and the runtime. The smallest-memory setup with a measured result here, fully in memory, is RTX 4090 (24 GB) at 52.99 tok/s.
What is the fastest measured hardware for Llama 3.1 8B?
RTX Spark N1X: 56.5 tok/s generation (llama.cpp CUDA, MLPerf Client v2.0 (geomean)), from https://www.storagereview.com/review/hp-omnibook-ultra-16-review-rtx-spark-n1x.
Related: what GPU do you need for 2026 models · all builds