v2.2 · 2026-06-12

Meta Llama 3.1 70B Instruct

llama-3-1-70b-instruct

TEXT-CHAT

Meta Llama 3.1 70B Instruct is a 70B text chat model profile for local AI planning. This page records provider, license, context, quantization, hardware-fit estimates, setup hints, source links, and caveats so readers do not choose a model by name alone.

PROVIDER

Meta

FAMILY

llama

MODEL TYPE

text-chat

PARAMETERS

70B

MODALITIES

text

ARCHITECTURE

decoder-only Transformer (LlamaForCausalLM) with grouped-query attention

CONTEXT WINDOW

128,000 tokens

TRAINING TOKENS

15.0T

RELEASE DATE

2024-07-23

LICENSE & LINKS

Llama 3.1 Community Licenselicense text

MODEL CARD

huggingface.co/meta-llama/Llama-3.1-70B-Instruct

HUGGING FACE

meta-llama/Llama-3.1-70B-Instruct

GGUF REPOSITORIES

bartowski/Meta-Llama-3.1-70B-Instruct-GGUF community conversion

Community GGUF conversion with Q4_K_M, Q5_K_M and Q8_0 file-size table; larger quantizations are split.

SETUP HINTS

OLLAMA

ollama run llama3.1:70b

LM STUDIO

Use bartowski/Meta-Llama-3.1-70B-Instruct-GGUF and select Q4_K_M only on systems with enough RAM/VRAM.

LLAMA.CPP

llama-server -hf bartowski/Meta-Llama-3.1-70B-Instruct-GGUF:Q4_K_M

QUANTIZATION FILE SIZES (GGUF)

Q4_K_M

42.52 GB

Q5_K_M

49.95 GB

Q8_0

74.98 GB

Quantization note: Q4_K_M

RAM / VRAM ESTIMATES

MIN RAM

48 GB

COMFORTABLE RAM

64 GB

MIN VRAM

48 GB

COMFORTABLE VRAM

80 GB

These are conservative local-inference estimates, not official hardware requirements.

HARDWARE FIT

CPU only (no GPU)

limited

8 GB RAM

16 GB RAM

32 GB RAM

8 GB VRAM

12 GB VRAM

24 GB VRAM

partial_offload_only

Apple Silicon (unified memory)

requires_64gb_or_more_unified_memory

Hardware fit values are conservative local-inference estimates based on GGUF size plus runtime and KV-cache overhead. Actual requirements depend on context length, quantization, runtime, GPU offload, and other running apps.

BEST FOR

·high-quality local general assistant

·complex drafting and summarization

·multilingual dialogue

·local RAG where quality matters more than latency

AVOID IF

·you have less than about 48 GB available memory for the model

·you need laptop-class latency

·you need an Apache-2.0 license

CAVEATS

·RAM and VRAM estimates are conservative local-inference estimates based on GGUF file size plus runtime and KV-cache overhead, not official Meta requirements.

·Full-GPU Q4_K_M inference generally exceeds 24 GB VRAM; 24 GB cards require CPU offload, smaller quantization, shorter context, or a different model.

SOURCE URLS