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Can the Apple M4 (16 GB) run LLaVA 1.5 7B?

11Basic quality (D) · 11 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 56/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well

Runs well. At Q2_K it needs about 6.4 GB of 10.7 GB usable, with an estimated speed of 29.5 tok/s.

Best quant
Q2_K
Generation speed
29.5 tok/s
Prompt processing
378 tok/s
First token (1K prompt)
2.7 s
Largest safe context
4K

Best app to run it on the Apple M4 (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 llava:7b-v1.5-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 LLaVA 1.5 7B

Recommended for the Apple M4 (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 llava:7b-v1.5-q2_K

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/second-state/Llava-v1.5-7B-GGUF (opens in a new tab)

ollama run hf.co/second-state/Llava-v1.5-7B-GGUF:Q2_K

Original weights · Hugging Face

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

huggingface.co/liuhaotian/llava-v1.5-7b (opens in a new tab)

No sign-in needed.

Links checked 3 days ago against Hugging Face, Ollama and GitHub.

How it’s rated

Quality 11Basic quality (D) · 11 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 56/100 (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 well
How well it runs on the Apple M4 (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.5.0 / 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.12.9 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.13.8 / 15
Match score56.8 / 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 (16 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 6.4 GB 10.7 GB 29.5 tok/s Runs well 11Basic quality (D) · 11 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 56/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 7.1 GB 10.7 GB 23.2 tok/s Runs well 13Basic quality (D) · 13 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q4_K_M 7.9 GB 10.7 GB 18.0 tok/s Runs well 15Basic quality (D) · 15 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 8.6 GB 10.7 GB 14.8 tok/s Runs well 15Basic quality (D) · 15 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 9.4 GB 10.7 GB 12.5 tok/s Runs welltight fit 15Basic quality (D) · 15 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 54/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q8_0 11.1 GB 10.7 GB 7.8 tok/s Slowover GPU memory limit 16Basic quality (D) · 16 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 38/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 17.7 GB 10.7 GB — Won't run 16Basic quality (D) · 16 of 100: how capable the model is at F16, whatever it runs on. Match score 2/100 (quality, fit, speed and memory use) sets the ranking. Original weights (opens in a new tab)

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

Other hardware that runs it

Quick answers

How much memory does LLaVA 1.5 7B need on the M4 16GB?

At Q2_K it needs about 6.4 GB. The M4 16GB has 10.7 GB usable.

How fast is LLaVA 1.5 7B on the M4 16GB?

About 29.5 tok/s at Q2_K, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of LLaVA 1.5 7B should I use on the M4 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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