Gemma 1.1 7B IT on Apple M3 Ultra (512 GB)

Can the Apple M3 Ultra (512 GB) run Gemma 1.1 7B IT?

Yes, it runs great

Runs great. At Q8_0 it needs about 44.1 GB of 384 GB usable, with an estimated speed of 51.6 tok/s.

Best quant: Q8_0
Generation speed: 51.6 tok/s
Prompt processing: 2,062 tok/s
First token (1K prompt): 0.5 s
Largest safe context: 8K

Best app to run it on the Apple M3 Ultra (512 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 8.5B active parameters on Apple silicon.
Point-and-click: search, download and chat in one window.

Open Gemma 1.1 7B IT 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 8.5B active parameters on Apple silicon.

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 Gemma 1.1 7B IT

Recommended for the Apple M3 Ultra (512 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 gemma:7b-instruct-v1.1-q8_0

Quantized files · GGUF

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

huggingface.co/google/gemma-1.1-7b-it-GGUF (opens in a new tab)

ollama run hf.co/google/gemma-1.1-7b-it-GGUF

Original weights · Hugging Face

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

huggingface.co/google/gemma-1.1-7b-it (opens in a new tab)

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

Links checked 1 day ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 28Basic quality (D) · 28 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 55/100 (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 M3 Ultra (512 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.	12.6 / 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.	2.7 / 15
Match score	55.3 / 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 M3 Ultra (512 GB)

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	38.5 GB	384 GB	166 tok/s	Runs great	19Basic quality (D) · 19 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 51/100 (quality, fit, speed and memory use) sets the ranking.
`Q3_K_M`	39.3 GB	384 GB	131 tok/s	Runs great	23Basic quality (D) · 23 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 52/100 (quality, fit, speed and memory use) sets the ranking.
`Q4_K_M`	40.3 GB	384 GB	101 tok/s	Runs great	26Basic quality (D) · 26 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking.
`Q5_K_M`	41.1 GB	384 GB	83.7 tok/s	Runs great	27Basic quality (D) · 27 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking.
`Q6_K`	42.1 GB	384 GB	70.3 tok/s	Runs great	27Basic quality (D) · 27 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking.
`Q8_0`	44.1 GB	384 GB	51.6 tok/s	Runs great	28Basic quality (D) · 28 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking.
`F16`	52.2 GB	384 GB	23.8 tok/s	Runs well	28Basic quality (D) · 28 of 100: how capable the model is at F16, whatever it runs on. Match score 52/100 (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

28Basic quality (D) · 28 of 100: how capable the model is at F16, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 F16 · 60.4 tok/s · Runs great
27Basic quality (D) · 27 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT Q6_K · 55.0 tok/s · Runs great
27Basic quality (D) · 27 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 Ti SUPER Q6_K · 67.0 tok/s · Runs great
27Basic quality (D) · 27 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4080 SUPER Q6_K · 73.4 tok/s · Runs great
27Basic quality (D) · 27 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5060 Ti 16GB Q6_K · 44.7 tok/s · Runs great
27Basic quality (D) · 27 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 67/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5070 Ti Q6_K · 89.4 tok/s · Runs great

Quick answers

How much memory does Gemma 1.1 7B IT need on the M3 Ultra 512GB?

At Q8_0 it needs about 44.1 GB. The M3 Ultra 512GB has 384 GB usable.

How fast is Gemma 1.1 7B IT on the M3 Ultra 512GB?

About 51.6 tok/s at Q8_0, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Gemma 1.1 7B IT should I use on the M3 Ultra 512GB?

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.