Meta Llama 3.1 8B Instruct Awq Int4.
1× Nvidia P102-100.
Most-aggressive quantisation we have a working recommendation for. Lower precision = less VRAM = cheaper hardware, at a small accuracy cost.
Cheapest hosted endpoints.
| Provider | Access | $/M in | $/M out | |
|---|---|---|---|---|
| Together AI | hosted inference | — | — | Launch ↗ |
Frequently asked.
How do I run Meta Llama 3.1 8B Instruct Awq Int4?
Where can I access Meta Llama 3.1 8B Instruct Awq Int4?
How much does it cost to run Meta Llama 3.1 8B Instruct Awq Int4?
Is Meta Llama 3.1 8B Instruct Awq Int4 open-source or proprietary?
Cheapest hardware per quantisation.
Each row is one quantisation tier (the same weights compressed differently). Lower precision → lower VRAM → cheaper hardware, at the cost of small accuracy loss. $/hr refreshed hourly from each provider's API.
| Quantisation | Cheapest GPU config | Total VRAM | Live $/hr | tokens/sec | |
|---|---|---|---|---|---|
|
FP16
FP16 — half precision (default)
|
20 GB | $0.17/hr | — | Compare → | |
|
FP8
FP8 — 8-bit float (Hopper / Blackwell)
|
10 GB | — | — | Compare → | |
|
INT4
INT4 — 4-bit integer (~4× VRAM saving)
|
5 GB | — | — | Compare → |
What it costs per month across providers.
Estimate your monthly bill for Meta Llama 3.1 8B Instruct Awq Int4 across every host that publishes per-token pricing. Slide your token volumes; the chart + table re-rank cheapest-first.
No priced API access rows on file for Meta Llama 3.1 8B Instruct Awq Int4 yet.
Rent the GPU instead of paying per token.
For an open-weights model like Meta Llama 3.1 8B Instruct Awq Int4, you can rent a GPU and serve inference yourself. The math: cheapest GPU rental × 730 hours/month + your electricity rate × power draw.
Assumes the GPU runs 24/7 at ~85% utilisation. If your traffic is bursty, you'll pay less for the API and probably more for the GPU (idle hours still cost rental). The breakeven analysis lives on the Self-host vs API breakeven tool.