Llama 3.3 70B Instruct — VRAM, Speed & Local Setup

Written by Jakub Rusinowski · Last updated December 8, 2024

Model library → Llama 3.3 → Llama 3.3 70B Instruct

The current king of open weights. Exceptionally capable at following complex instructions, coding, and creative writing.

Llama 3.3 70B Instruct needs about 43 GB of VRAM at Q2_K_XS (Tight) — quantized weights plus framework overhead, before any KV cache. On Apple Silicon that figure comes out of unified memory.

Specifications

Parameters70 Billion
Context window128,000
ArchitectureDense
ProviderMeta
LicenceLlama Community
Specified atQ2_K_XS (Tight)
System RAM64 GB
Record updated2024-12-08

Licence

Llama Community — commercial use permitted. Weights are downloadable and commercial use is permitted, subject to the licence’s acceptable-use terms.

VRAM and Speed by Quantization

Modelled on a reference NVIDIA RTX 4090 (24 GB), at 8K context. Speed figures are ESTIMATES from the memory-bandwidth roofline described on the methodology page, not benchmarks we ran — rows marked measured come from published or reader-submitted runs. VRAM here includes the KV cache, so it reads higher than the headline figure above, which does not.

QuantWeightsVRAM neededEst. speedFit on 24 GB
Q2_K23.0 GB26.5 GB~4 tok/s (est.)Offloads to system RAM (slow)
Q3_K_M29.8 GB33.3 GB~3 tok/s (est.)Offloads to system RAM (slow)
Q4_K_M42.3 GB45.7 GB~3 tok/s (est.)Offloads to system RAM (slow)
Q5_K_M49.6 GB53.1 GB~2 tok/s (est.)Offloads to system RAM (slow)
Q6_K57.4 GB60.9 GB—Won't fit
Q8_074.4 GB77.9 GB—Won't fit
F16140.0 GB143.5 GB—Won't fit

Want the memory numbers alone, at every quantization level and your own context length? Use the Llama 3.3 70B Instruct VRAM calculator.

Buy This HardwareApple MacBook Pro M5 Pro — 64 GB VRAM · 30 W board powerDeploy in the Cloud NowNVIDIA A40 on RunPod — from $0.44/hr · rate checked 2026-08

or compare on Vast.ai

As an Amazon Associate we earn from qualifying purchases. Cloud GPU links are referral links — we may earn a commission at no extra cost to you.

Recommended GPU

The cheapest catalogued GPU that runs Llama 3.3 70B Instruct is the Apple M5 Pro (64 GB).

Affiliate disclosure: Some links on this page are affiliate links — if you buy through them, LLM Configurator may earn a commission at no extra cost to you. As an Amazon Associate, LLM Configurator earns from qualifying purchases.
Apple MacBook Pro M5 Pro
64 GB VRAM · 30 W board power
2026 prices are volatile — check the current listing.
Check price on Amazon

How to Run Llama 3.3 70B Instruct

Install Ollama, then run:

ollama run llama3.3

Weights on Hugging Face: meta-llama/Llama-3.3-70B-Instruct.

Best for: coding, creative, reasoning.

Can I Run Llama 3.3 70B Instruct on My GPU?

Llama 3.3 70B Instruct — Frequently Asked Questions

How much VRAM does Llama 3.3 70B Instruct need?
About 43 GB at Q2_K_XS (Tight) — quantized weights plus framework overhead, before any KV cache. The cache grows with context length and is added on top; the table above folds it in. Apple Silicon counts unified memory toward the same figure.
Does Llama 3.3 70B Instruct run on an RTX 4090 (24 GB)?
No. Llama 3.3 70B Instruct needs about 43 GB at Q2_K_XS (Tight), more than a single RTX 4090's 24 GB. It needs a larger card, several GPUs, or Apple Silicon with enough unified memory — or it runs with part of the weights offloaded to system RAM, which is much slower.
How do I run Llama 3.3 70B Instruct locally?
Install Ollama and run `ollama run llama3.3`. That pulls the weights and starts a local OpenAI-compatible endpoint; after the download nothing leaves the machine.

← All Llama 3.3 models | VRAM calculator | Check your own hardware