What hardware do you need to run Llama 3.3 70B Instruct?
Llama 3.3 70B Instruct at Q4_K_M is 43 GB of weights, with a context ceiling of 128k tokens. A solid generalist. Slower than the 30B MoE models for similar everyday quality.
How good is it, really?
On the Artificial Analysis Intelligence Index v4.3 it scores 8, 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 — good
- Everyday coding — good
- Reasoning & maths — usable
- Agentic work — usable
What it costs either way
Renting the same model costs $0.1 per million input tokens and $0.32 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 | |
|---|---|---|---|---|
| Strix Halo Framework Desktop, 128GB | $3,449 | 4 tok/s measured | Pays back in 1,274 years | Run the numbers |
| DGX Spark GB10 Grace Blackwell, 128GB | $4,699 | 3.8 tok/s estimated | Never pays back | Run the numbers |
| Mac Studio M5 Max, 128GB | $5,099 | 8.6 tok/s estimated | Pays back in 435 years | Run the numbers |
| MacBook Pro M5 Max (16-inch), 128GB | $6,999 | 8.6 tok/s estimated | Pays back in 596 years | 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
- 70.6B
- Quantisation
- Q4_K_M
- Weights on disk
- 43 GB
- KV cache
- 11 GB at 32k context
- Maximum context
- 128k tokens (128k)
- Licence
- Llama 3.3 Community License
- Sources
- source 1, source 2