Code Llama 7B Instruct on Apple M4 Max (64 GB)

Can the Apple M4 Max (64 GB) run Code Llama 7B Instruct?

Yes, it runs great

Runs great. At Q8_0 it needs about 15.9 GB of 48.0 GB usable, with an estimated speed of 43.6 tok/s.

Best quant: Q8_0
Generation speed: 43.6 tok/s
Prompt processing: 1,693 tok/s
First token (1K prompt): 0.6 s
Largest safe context: 16K

Best app to run it on the Apple M4 Max (64 GB)

On macOS

LM Studio

Desktop app · runs on llama.cpp, MLX · use the MLX build

Runs the MLX build, typically 20–35% faster than GGUF for a model with 6.7B active parameters on Apple silicon.
Point-and-click: search, download and chat in one window.

Open Code Llama 7B Instruct in LM Studio

Ollama One command with Ollama’s own build of this model.
MLX LM Runs the MLX build, typically 20–35% faster than GGUF for a model with 6.7B active parameters on Apple silicon.

Want an agent? Code Llama 7B Instruct supports tool use, so you can pair a llama.cpp or MLX LM server with one of these. Apps that run on this computer come first.

Coding agents

Personal assistants

How we choose: the files published for this model, your operating system and GPU, whether it fits fully in memory, and published speed tests (MLX vs llama.cpp on Apple silicon, vLLM vs llama.cpp vs Ollama on NVIDIA). Details.

Download Code Llama 7B Instruct

Recommended for the Apple M4 Max (64 GB): Q8_0. 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.

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

ollama run codellama:7b-instruct-q8_0

Quantized files · GGUF

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

Download the Q8_0 file (opens in a new tab)

huggingface.co/TheBloke/CodeLlama-7B-Instruct-GGUF (opens in a new tab)

ollama run hf.co/TheBloke/CodeLlama-7B-Instruct-GGUF:Q8_0

Original weights · Hugging Face

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

huggingface.co/meta-llama/CodeLlama-7b-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)

Meta’s official Code Llama repo.

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

Links checked 2 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 24Basic quality (D) · 24 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q8_0, from its editorial quality tier scaled by the quantization or precision. It’s the same on any hardware that runs this variant. Scale: A+ 85+ · A 70+ · B 55+ · C 40+ · D under 40.
Runs Runs great: How well it runs on the Apple M4 Max (64 GB): speed, capped by how well it fits in memory. Runs great: 30+ tokens/s (or near the image, video or decision speed target) · Runs well: 10+ tokens/s · Slow: 3+ tokens/s · Very slow: under 3 · Won’t run: doesn’t fit even with offload. A tight fit or RAM offload caps it at Runs well; heavy offload caps it at Slow.

Match score

To order lists, each result also gets a match score out of 100 from four parts. It favors the most capable model that still runs well.

Model qualityEditorial quality tier of the model, scaled by the quantization or precision used.	10.8 / 45
Memory fitFull marks when the model fits in the share of unified memory the GPU can use; fewer when it goes past that limit.	25.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	15.0 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.	7.6 / 15
Match score	58.4 / 100

The match score orders results; it is not shown as a grade. Quality and Runs are the two ratings in the tables.

Every quantization on the Apple M4 Max (64 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	11.4 GB	48.0 GB	141 tok/s	Runs great	16Basic quality (D) · 16 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q3_K_M`	12.0 GB	48.0 GB	111 tok/s	Runs great	20Basic quality (D) · 20 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q4_K_M`	12.8 GB	48.0 GB	85.6 tok/s	Runs great	22Basic quality (D) · 22 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 56/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q5_K_M`	13.5 GB	48.0 GB	70.7 tok/s	Runs great	23Basic quality (D) · 23 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 56/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q6_K`	14.3 GB	48.0 GB	59.4 tok/s	Runs great	23Basic quality (D) · 23 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`Q8_0`	15.9 GB	48.0 GB	43.6 tok/s	Runs great	24Basic quality (D) · 24 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 58/100 (quality, fit, speed and memory use) sets the ranking.	GGUF (opens in a new tab)
`F16`	22.2 GB	48.0 GB	20.1 tok/s	Runs well	24Basic quality (D) · 24 of 100: how capable the model is at F16, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking.	Original weights (opens in a new tab)

Memory figures assume 8K of context. Longer conversations or documents grow the KV cache.

Other hardware that runs it

24Basic quality (D) · 24 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT Q8_0 · 51.1 tok/s · Runs great
24Basic quality (D) · 24 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 Ti SUPER Q8_0 · 62.3 tok/s · Runs great
24Basic quality (D) · 24 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4080 SUPER Q8_0 · 68.2 tok/s · Runs great
24Basic quality (D) · 24 of 100: how capable the model is at F16, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4090 F16 · 43.1 tok/s · Runs great
24Basic quality (D) · 24 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5060 Ti 16GB Q8_0 · 41.5 tok/s · Runs great
24Basic quality (D) · 24 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 65/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5070 Ti Q8_0 · 83.1 tok/s · Runs great

Quick answers

How much memory does Code Llama 7B Instruct need on the M4 Max 64GB?

At Q8_0 it needs about 15.9 GB. The M4 Max 64GB has 48.0 GB usable.

How fast is Code Llama 7B Instruct on the M4 Max 64GB?

About 43.6 tok/s at Q8_0, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Code Llama 7B Instruct should I use on the M4 Max 64GB?

Q8_0. Of the versions that fit, it is the one that runs best; when two run equally well, the check prefers the higher-rated one.

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.