Llama 3.3 70B
MetaLarge Language ModelOpen SourceMeta's high-performance dense model offering near-405B quality at a fraction of the cost. Popular for self-hosting and fine-tuning.
Abilities
Use Cases
Available in Tools
Availability
How to Use
Pros
- Open weights — permissive license allows commercial use for most companies
- Delivers near-405B quality at fraction of compute — excellent quality per FLOP
- Largest fine-tuning community — thousands of specialized variants on HuggingFace
- Can run on 2x A100 or 4x A10G GPUs — manageable for many organizations
- Extremely well-supported across inference frameworks (vLLM, TGI, llama.cpp)
Cons
- Still requires enterprise-grade GPUs — not feasible on consumer hardware
- Cannot compete with latest frontier closed models (GPT-4.1, Opus 4) on hardest tasks
- 128K context window is shorter than Gemini (1M) or GPT-4.1 (1M)
- No native vision/multimodal capabilities in the text-only variant
What to Use It For
Perfect For
Sweet spot of quality and compute cost — runs on 2x A100 with near-frontier quality
Largest fine-tuning ecosystem — thousands of community adapters and proven training recipes
Free weights + manageable hardware requirements = lowest cost per token for quality inference
Good For
Strong general-purpose performance with well-tested instruction following
Good multilingual training enables solid translation quality across major languages
Not Recommended
128K max context is insufficient for book-length inputs that Gemini handles in one pass
Try instead: Gemini 2.5 Pro
Noticeable gap vs Claude Opus 4 or GPT-4.1 on the hardest reasoning and analysis tasks
Try instead: Claude Opus 4
Do Not Use For
Needs 2x A100 (80GB each) — far beyond any gaming GPU or laptop
Try instead: Phi-4
Text-only model with no vision or audio capabilities whatsoever
Try instead: Whisper, FLUX.1