About Llama 3.3 70B
Llama 3.3 70B was Meta's efficiency statement at the top of the dense Llama line: the company introduced it as delivering performance comparable to its far larger flagship of the same generation, at a fraction of the deployment size. It is text-only, dense, and carries Meta's community licence with its acceptable-use terms and large-platform carve-out.
It became the serious local model — the size enthusiasts build machines around and small teams deploy on single servers — because it was the most capability per gigabyte the open ecosystem offered in a dense package.
Llama 3.3 70B is Meta's 70.6B-parameter model released in December 2024, with a 128K-token context window. It uses a classic dense-attention design whose KV cache grows linearly with context: its KV cache is about 2.7 GB at an 8K context, 42.9 GB at 128K, and 42.9 GB at the full 128K window (FP16 cache).
The classic seventy-billion problem defines this page: at quality quantisation the total exceeds any single consumer card, which is why the standard homes are dual-GPU rigs, workstation cards and higher-memory unified-memory machines — or partial offload at a real speed cost. Dense GQA attention adds a KV cache that grows steadily with context on top. The quantisation table above is where the decisions live: each step down the list trades measurable quality for fitting one class of hardware lower, and the sweet spot depends on what you own rather than any universal answer.
For most people Q4_K_M is the sweet spot — the most popular quality/size trade-off — while Q8 is near-lossless if you have the memory. Totals above include the KV cache and a realistic framework overhead, so they are what you should expect to see in practice rather than just the download size. Weight sizes are calibrated against real GGUF files — see the methodology.
Frequently asked questions
How much VRAM does Llama 3.3 70B need?
At Q4_K_M with an 8K context, Llama 3.3 70B needs about 51 GB (weights 43 GB + KV cache + overhead). The smallest common hardware that fits is a A100 80GB.
Can an RTX 4090 (24GB) run Llama 3.3 70B?
Not fully in VRAM. Llama 3.3 70B needs about 51 GB even at Q4_K_M, so a 24 GB card would have to offload layers to system RAM at a large speed penalty.
Can a Mac run Llama 3.3 70B?
Yes — Apple Silicon with 96 GB of unified memory or more (macOS lets the GPU use ~75% of it, ~72 GB) runs Llama 3.3 70B at Q4_K_M.
How much VRAM does Llama 3.3 70B need at its full 128K context?
The KV cache grows with context: about 2.7 GB at 8K tokens versus 42.9 GB at the full 128K window (FP16 cache). Add that to the weights (43 GB at Q4_K_M) plus overhead — long contexts can cost more than a whole quantisation step.
Related
VRAM calculator for any model ·Token counter
Similar-size local models: Llama 4 Scout (109B) · GPT-OSS 120B (117B) · Mistral Small 4 (119B) · Qwen3.6 35B-A3B (35B) · Gemma 4 31B (31B)
Last updated 2026-09-03. Architecture figures from the model's published config.json; see the methodology.