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Can the Apple M6 (16 GB) run Gemma 7B IT?

18Basic quality (D) · 18 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs great

Runs great. At Q2_K it needs about 8.6 GB of 10.7 GB usable, with an estimated speed of 31.1 tok/s.

Best quant
Q2_K
Generation speed
31.1 tok/s
Prompt processing
414 tok/s
First token (1K prompt)
2.5 s
Largest safe context
8K

Best app to run it on the Apple M6 (16 GB)

On macOS

Ollama

Command line + local API · runs on llama.cpp (ggml), MLX (Apple silicon, preview)

  • One command with Ollama’s own build of this model.
  • Serves an OpenAI-compatible API that other apps and coding tools can use.
ollama run gemma:7b-instruct-q2_K
  • LM Studio Point-and-click: search, download and chat in one window.
  • llama.cpp Usually the first to support new model architectures.

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 7B IT

Recommended for the Apple M6 (16 GB): 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.

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

ollama run gemma:7b-instruct-q2_K

Quantized files · GGUF

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

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

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

Original weights · Hugging Face

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

huggingface.co/google/gemma-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 18Basic quality (D) · 18 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking.
How capable the model itself is at Q2_K, 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 M6 (16 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.8.2 / 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.13.2 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.15.0 / 15
Match score61.4 / 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 M6 (16 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 8.6 GB 10.7 GB 31.1 tok/s Runs great 18Basic quality (D) · 18 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 61/100 (quality, fit, speed and memory use) sets the ranking.
Q3_K_M 9.4 GB 10.7 GB 24.5 tok/s Runs welltight fit 21Basic quality (D) · 21 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking.
Q4_K_M 10.4 GB 10.7 GB 18.9 tok/s Runs welltight fit 24Basic quality (D) · 24 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 60/100 (quality, fit, speed and memory use) sets the ranking.
Q5_K_M 11.3 GB 10.7 GB 13.3 tok/s Runs wellover GPU memory limit 25Basic quality (D) · 25 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking.
Q6_K 12.2 GB 10.7 GB 11.2 tok/s Slowfar over GPU memory limit 25Basic quality (D) · 25 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 38/100 (quality, fit, speed and memory use) sets the ranking.
Q8_0 14.3 GB 10.7 GB — Won't run 26Basic quality (D) · 26 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 3/100 (quality, fit, speed and memory use) sets the ranking.
F16 22.3 GB 10.7 GB — Won't run 26Basic quality (D) · 26 of 100: how capable the model is at F16, whatever it runs on. Match score 3/100 (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

Quick answers

How much memory does Gemma 7B IT need on the M6 16GB?

At Q2_K it needs about 8.6 GB. The M6 16GB has 10.7 GB usable.

How fast is Gemma 7B IT on the M6 16GB?

About 31.1 tok/s at Q2_K, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Gemma 7B IT should I use on the M6 16GB?

Q2_K. 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.

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