What hardware do you need to run Llama 3.1 8B Instruct?
Llama 3.1 8B Instruct at Q8_0 is 8.5 GB of weights, with a context ceiling of 128k tokens. Same model at higher precision. Marginal quality gain; twice the memory.
How good is it, really?
On the Artificial Analysis Intelligence Index v4.3 it scores 7, which puts it in the Below every hosted tier band. Fine for simple, well-specified tasks. Noticeably less capable than anything the big labs sell today. Score source. See the whole table.
- Summarising — good
- Translation — usable
- Everyday coding — usable
- Reasoning & maths — don’t
- Agentic work — don’t
What it costs either way
Renting the same model costs $0.02 per million input tokens and $0.04 per million output (OpenRouter, cheapest active endpoint, checked 2026-09-03). Buying a machine only beats that if you use it hard enough, for long enough, that the hardware price divides down below the rental bill.
Machines that run it
| Machine | Price | Speed at 32k | Pay-back | |
|---|---|---|---|---|
| Mac mini M6, 24GB | $1,099 | 9.9 tok/s estimated | Pays back in 3,105 years | Run the numbers |
| Strix Halo Framework Desktop, 32GB | $1,269 | 15 tok/s estimated | Never pays back | Run the numbers |
| MacBook Pro M5 (14-inch), 32GB | $2,399 | 8.9 tok/s estimated | Never pays back | Run the numbers |
| Mac Studio M5 Max, 36GB | $2,499 | 27 tok/s estimated | Pays back in 2,568 years | Run the numbers |
| DGX Spark GB10 Grace Blackwell, 128GB | $4,699 | 16 tok/s estimated | Never pays back | Run the numbers |
One machine per family, cheapest first. Speeds are measured where a public benchmark exists and estimated from memory bandwidth otherwise; the calculator says which for any configuration.
The specifics
- Parameters
- 8B
- Quantisation
- Q8_0
- Weights on disk
- 8.5 GB
- KV cache
- 4.3 GB at 32k context
- Maximum context
- 128k tokens (128k)
- Licence
- Llama 3.1 Community License
- Sources
- source 1, source 2