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
DGX Spark (GB10, 128 GB): 23 models on vLLM at 128K context, Qwen3.6 35B-A3B up to 102 tok/s		NVIDIA NIM 2.0.9, 128K ctx, 1 user	26.4		source

Rent before you buy: try this model on a cloud GPU for a few dollars first: Runpod (referral link; sign up with Google to get the new-user bonus)

Memory math: how much VRAM / RAM does Llama 3.1 8B need?

Llama 3.1 8B has 8B parameters (source: Ollama library tag 8b). Weight size = parameters × bits-per-weight ÷ 8, before context (KV cache) and runtime overhead.

Format	Bits/weight	Weights	Smallest common memory size that holds the weights alone
Q8_0	8.50	8.5 GB	12 GB
Q6_K	6.56	6.6 GB	8 GB
Q5_K_M	5.70	5.7 GB	8 GB
Q4_K_M	4.89	4.9 GB	8 GB
Q3_K_M	4.00	4.0 GB	8 GB
Q2_K	3.16	3.2 GB	8 GB

Bits-per-weight for GGUF quants are llama.cpp's own figures (llama.cpp quantize README). "As measured" rows apply the source's stated bits-per-weight to the parameter count; real files differ slightly because some layers stay at higher precision. Try other combinations in the VRAM calculator.

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.