Code Llama 70B Instruct on Apple M2 (8 GB)

Can the Apple M2 (8 GB) run Code Llama 70B Instruct?

4 pts

No, it doesn’t fit

No. Code Llama 70B Instruct needs about 30.0 GB even at Q2_K; the Apple M2 (8 GB) has 5.4 GB usable.

Download Code Llama 70B Instruct

Recommended: Q2_K. Every other quantization has its own download link in the table below.

Quickest · Ollama

One command downloads a ready-to-run version and starts a chat.

ollama run codellama:70b-instruct-q2_K

Get Ollama at ollama.com/download (opens in a new tab) · model page (opens in a new tab)

Quantized files · GGUF

Works in LM Studio, llama.cpp, Jan and Ollama. No sign-in needed.

Download the Q2_K file (opens in a new tab)

huggingface.co/TheBloke/CodeLlama-70B-Instruct-GGUF (opens in a new tab)

ollama run hf.co/TheBloke/CodeLlama-70B-Instruct-GGUF:Q2_K

Original weights · Hugging Face

Full-precision files from Meta, for fine-tuning, vLLM, Transformers.

huggingface.co/meta-llama/CodeLlama-70b-Instruct-hf (opens in a new tab)

Free Hugging Face account needed: request access on the model page, and Meta approves it (usually within minutes to a few hours).

Official repo · GitHub

Reference code, inference scripts and docs from Meta. The weights themselves download from Hugging Face.

github.com/meta-llama/codellama (opens in a new tab)

git clone https://github.com/meta-llama/codellama

Meta’s official Code Llama repo.

Links checked 5 days ago against Hugging Face, Ollama and GitHub.

How the F grade is scored

Every result gets up to 100 points from four parts. The points decide the letter grade.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.	4.2 / 45
Memory fitFull marks when everything sits in GPU or unified memory; fewer when part spills to system RAM.	0.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	0.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	0.0 / 15
Total	4.2 / 100	4 ptsWon't run (F) · 4 of 100 points: Model quality 4.2/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15

It doesn’t fit, so only a reduced share of the quality points counts and fit, speed and memory use score zero.

Grade scale: S 85+ · A 72+ · B 58+ · C 42+ · D 25+ · F under 25 points.

Every quantization on the Apple M2 (8 GB)

Quant	Memory needed	Available	Speed	Fit	Grade · points	Download
`Q2_K`	30.0 GB	5.4 GB	—	Won't run	4 ptsWon't run (F) · 4 of 100 points: Model quality 4.2/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF (opens in a new tab)
`Q3_K_M`	36.2 GB	5.4 GB	—	Won't run	4 ptsWon't run (F) · 4 of 100 points: Model quality 5.0/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF (opens in a new tab)
`Q4_K_M`	44.4 GB	5.4 GB	—	Won't run	5 ptsWon't run (F) · 5 of 100 points: Model quality 5.6/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF (opens in a new tab)
`Q5_K_M`	51.3 GB	5.4 GB	—	Won't run	5 ptsWon't run (F) · 5 of 100 points: Model quality 5.8/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	GGUF (opens in a new tab)
`Q6_K`	58.9 GB	5.4 GB	—	Won't run	5 ptsWon't run (F) · 5 of 100 points: Model quality 5.9/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15
`Q8_0`	75.5 GB	5.4 GB	—	Won't run	5 ptsWon't run (F) · 5 of 100 points: Model quality 5.9/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15
`F16`	140 GB	5.4 GB	—	Won't run	5 ptsWon't run (F) · 5 of 100 points: Model quality 5.9/45, Memory fit 0.0/25, Speed 0.0/15, Memory use 0.0/15	Original weights (opens in a new tab)

Memory figures assume 4K of context. Longer conversations or documents grow the KV cache.

Hardware that can run it

68 ptsGood (B) · 68 of 100 points: Model quality 18.8/45, Memory fit 25.0/25, Speed 13.4/15, Memory use 11.7/15 NVIDIA RTX PRO 6000 Blackwell Q4_K_M · 31.9 tok/s
67 ptsGood (B) · 67 of 100 points: Model quality 13.9/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 NVIDIA GeForce RTX 5090 Q2_K · 52.4 tok/s
64 ptsGood (B) · 64 of 100 points: Model quality 18.8/45, Memory fit 24.0/25, Speed 6.9/15, Memory use 15.0/15 Apple M4 Max (64 GB) Q4_K_M · 8.4 tok/s
63 ptsGood (B) · 63 of 100 points: Model quality 18.8/45, Memory fit 24.0/25, Speed 5.9/15, Memory use 15.0/15 Apple M1 Max (64 GB) Q4_K_M · 6.1 tok/s
63 ptsGood (B) · 63 of 100 points: Model quality 19.6/45, Memory fit 25.0/25, Speed 3.9/15, Memory use 15.0/15 AMD Ryzen AI Max+ 395 (128 GB) Q6_K · 2.7 tok/s
63 ptsGood (B) · 63 of 100 points: Model quality 18.8/45, Memory fit 25.0/25, Speed 6.9/15, Memory use 12.4/15 Apple M4 Max (128 GB) Q4_K_M · 8.4 tok/s

Models that do run on the Apple M2 (8 GB)

77 ptsGreat (A) · 77 of 100 points: Model quality 26.1/45, Memory fit 25.0/25, Speed 11.9/15, Memory use 15.0/15 TMax 4B Q4_K_M · 25.1 tok/s
77 ptsGreat (A) · 77 of 100 points: Model quality 26.5/45, Memory fit 25.0/25, Speed 11.3/15, Memory use 15.0/15 Qwen3.5 4B Q4_K_M · 22.7 tok/s
77 ptsGreat (A) · 77 of 100 points: Model quality 26.5/45, Memory fit 25.0/25, Speed 10.8/15, Memory use 15.0/15 Gemma 4 E2B Q4_K_M · 20.6 tok/s
77 ptsGreat (A) · 77 of 100 points: Model quality 25.7/45, Memory fit 25.0/25, Speed 11.4/15, Memory use 15.0/15 Agents-A1 4B Q4_K_M · 23.3 tok/s
76 ptsGreat (A) · 76 of 100 points: Model quality 22.7/45, Memory fit 24.0/25, Speed 15.0/15, Memory use 15.0/15 Ling 3.0 Tiny (8B-A1.3B) Q3_K_M · 52.6 tok/s

Estimates come from published hardware specs and our open formulas, not from running the model on your machine. Real speed varies with runtime, drivers and settings. Numbers marked “Measured” come from a linked benchmark.