What hardware do you need to run DeepSeek-R1-Distill-Llama-70B?
DeepSeek-R1-Distill-Llama-70B at Q4_K_M is 43 GB of weights, with a context ceiling of 128k tokens. Strong maths and reasoning for an open model, but slow and verbose. Not for agents.
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 — usable
- Translation — usable
- Everyday coding — usable
- Reasoning & maths — good
- Agentic work — don’t
What it costs either way
Renting the same model costs $0.8 per million input tokens and $0.8 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 27 years | Run the numbers |
| DGX Spark GB10 Grace Blackwell, 128GB | $4,699 | 3.8 tok/s estimated | Pays back in 38 years | Run the numbers |
| Mac Studio M5 Max, 128GB | $5,099 | 8.6 tok/s estimated | Pays back in 37 years | Run the numbers |
| MacBook Pro M5 Max (16-inch), 128GB | $6,999 | 8.6 tok/s estimated | Pays back in 51 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 in config; the card does not state one for the distills)
- Licence
- MIT
- Sources
- source 1, source 2