Llama 3.2 3B.
3B Llama — laptop-class chat + RAG.
1× Nvidia Titan V.
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 | |
|---|---|---|---|---|
| OpenRouter | api aggregator | $0.0509 | $0.335 | Launch ↗ |
Speed across providers.
Tokens/sec and time-to-first-token measured against the same prompt template on each provider's API.
| Provider | Tokens/sec | TTFT | Total |
|---|---|---|---|
| OpenRouter | 107.9 | 439 ms | 695 ms |
Variants in the Llama family.
Llama 3.3 70B
Meta's best-in-class open-weight LLM — 70B class.
Llama 3.1 70B
Llama 3.1 70B — production workhorse, superseded by 3.3 but still widely depl...
Llama 3.1 8B
Meta's most popular open-weight small LLM — fits anywhere.
Llama 3.1 405B
Meta's largest open-weight LLM — dense 405B, frontier-class at launch.
Llama 3.2 90B Vision
Meta's largest vision-capable Llama.
Llama 3.2 11B Vision
Meta's open-weight multimodal LLM — vision + text in 11B.
Llama 3.2 1B
Meta's smallest Llama — mobile + on-device target.
Frequently asked.
How do I run Llama 3.2 3B?
Where can I access Llama 3.2 3B?
How much does it cost to run Llama 3.2 3B?
Is Llama 3.2 3B 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)
|
10 GB | — | — | Compare → | |
|
FP8
FP8 — 8-bit float (Hopper / Blackwell)
|
5 GB | — | — | Compare → | |
|
INT4
INT4 — 4-bit integer (~4× VRAM saving)
|
3 GB | — | — | Compare → |
What it costs per month across providers.
Estimate your monthly bill for Llama 3.2 3B across every host that publishes per-token pricing. Slide your token volumes; the chart + table re-rank cheapest-first.
Cheapest provider on the left.
Total monthly cost — input + output tokens combined.
Bill breakdown.
| Provider | Monthly total | |
|---|---|---|
| $1.18 | Sign up ↗ |
Rent the GPU instead of paying per token.
For an open-weights model like Llama 3.2 3B, 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.
About Llama 3.2 3B.
Llama 3.2 3B targets laptop GPUs (RTX 4060, M2 Pro) and edge servers. Strong for RAG pipelines where a small reasoning model handles routing/synthesis. ~6 GB VRAM at FP16, ~2 GB at INT4.