Llama 3.3 70B — Specifiche
Compiled by Mustafa Ihsan from the vendor’s published documentation · Last updated
| Sviluppatore | Meta |
|---|---|
| Tipo | LLM (densa) |
| Modalità | Testo → Testo |
| Parametri | 70B |
| Finestra contestuale | 128K |
| Licenza | Llama 3.3 Community (open) |
| Pesi aperti | Sì |
| Pubblicato | 2024 |
| Prezzo dell’input | $0.10 /1M |
| Prezzo dell’output | $0.32 /1M |
| Provider API | Together, DeepInfra, OpenRouter, Ollama |
Esegui localmente
| VRAM (4-bit) | ~40 GB |
|---|---|
| GPU minima | 2× RTX 4090 / 1× GPU da 48 GB |
What is Llama 3.3 70B?
Llama 3.3 70B is Meta’s efficient dense 70B — close to 405B-class quality at a fraction of
the size, with a 128K context. It needs about 40 GB of VRAM at 4-bit, so two RTX 4090s or a
single 48 GB card, and it is among the most-deployed open models for self-hosting.
Dense is the operative word. Nearly every large open model released since is a
mixture-of-experts, which lowers inference compute but keeps the full parameter count
resident in memory — DeepSeek V4-Pro activates 49B parameters but still needs roughly 800 GB
of VRAM to hold. A dense 70B has no such gap: what you load is what you use, so 40 GB is the
whole story. For teams sizing hardware, that predictability is worth a lot, and it is why
3.3 70B remains the default for on-premises deployments even as newer architectures post
better benchmark numbers. At $0.10 in / $0.32 out per million tokens the API is cheap enough
that self-hosting only makes sense for data-residency reasons or at sustained high
volume.
Llama 3.3 70B pricing: API cost per 1M tokens
| Input (per ogni milione di token) | $0.100 |
|---|---|
| Output (per ogni milione di token) | $0.320 |
| Rapporto output/input | 3.2× |
| Combinato (4:1 in:out) | $0.144 per 1 milione di token |
What Llama 3.3 70B costs per month
Spesa mensile reale con un mix input-to-output 4:1 — il rapporto effettivamente prodotto da un tipico carico di lavoro basato su chat o RAG.
| Carico di lavoro | Token/mese | Costo/mese |
|---|---|---|
| Progetto secondario | 1 milione in ingresso / 0,25 milioni in uscita | $0.18 |
| Piccolo team | 20 milioni in ingresso / 5 milioni in uscita | $3.60 |
| Produzione | 200 milioni in ingresso / 50 milioni in uscita | $36 |
Calcola i tuoi numeri personalizzati nel Calcolatore dei costi delle API per l'IA.
Cheaper alternatives to Llama 3.3 70B
| Modello | Costo combinato ($/1 milione) | Risparmi |
|---|---|---|
| Mistral 7B aperta | $0.0220 | 85% cheaper |
| Llama 3.1 8B aperta | $0.0220 | 85% cheaper |
| Mistral NeMo 12B aperta | $0.0240 | 83% cheaper |
Eseguirlo in autonomia o pagare l’API?
Llama 3.3 70B is open-weight, so you can run it yourself. It needs ~40 GB of VRAM at 4-bit (2× RTX 4090 / 1× 48GB). Self-hosting only beats the API once your volume is high enough to keep that hardware busy — the calcolatore self-hosting vs API calcola il punto di pareggio in base al tuo volume di token.
Domande frequenti
How much does Llama 3.3 70B cost per 1M tokens?
Llama 3.3 70B costs $0.100 per 1M input tokens and $0.320 per 1M output tokens. At a typical 4:1 input-to-output mix that blends to about $0.144 per 1M tokens.
How much does Llama 3.3 70B cost per month?
A small-team workload of 20M input and 5M output tokens a month costs about $3.60 on Llama 3.3 70B. A side project (1M in / 0.25M out) costs roughly $0.18.
What is a cheaper alternative to Llama 3.3 70B?
Mistral 7B is the strongest cheaper option in our database at $0.0220 per 1M blended — about 85% less than Llama 3.3 70B. It is also open-weight, so self-hosting is an option.
Can I run Llama 3.3 70B locally?
Yes. Llama 3.3 70B is open-weight and needs about ~40 GB of VRAM at 4-bit quantisation (2× RTX 4090 / 1× 48GB).
Why does Llama 3.3 70B charge more for output than input?
Output tokens are generated one at a time and cannot be batched the way a prompt can, so they cost the provider more to serve. Llama 3.3 70B charges 3.2× more for output, which is why prompt-heavy workloads are far cheaper to run than generation-heavy ones.
I prezzi indicati sono le tariffe ufficiali pubblicate per l'API principale del modello e vengono aggiornati man mano che i fornitori li modificano. Sconti per volumi elevati, elaborazione batch e input memorizzati nella cache non sono inclusi. Confronta tutti i modelli fianco a fianco nel Database di modelli IA o il Classifica LLM.