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Can the Apple M6 (24 GB) run Vicuna 13B v1.5?

14Basic quality (D) · 14 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking.

Yes, it runs well

Runs well. At Q2_K it needs about 10.4 GB of 16.1 GB usable, with an estimated speed of 22.7 tok/s.

Best quant
Q2_K
Generation speed
22.7 tok/s
Prompt processing
271 tok/s
First token (1K prompt)
3.8 s
Largest safe context
4K

Best app to run it on the Apple M6 (24 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 vicuna:13b-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 Vicuna 13B v1.5

Recommended for the Apple M6 (24 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 vicuna:13b-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/QuantFactory/vicuna-13b-v1.5-GGUF (opens in a new tab)

ollama run hf.co/QuantFactory/vicuna-13b-v1.5-GGUF:Q2_K

Original weights · Hugging Face

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

huggingface.co/lmsys/vicuna-13b-v1.5 (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 14Basic quality (D) · 14 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 57/100 (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 M6 (24 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.6.3 / 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.11.3 / 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 score57.6 / 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 (24 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 10.4 GB 16.1 GB 22.7 tok/s Runs well 14Basic quality (D) · 14 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 57/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q3_K_M 11.6 GB 16.1 GB 17.8 tok/s Runs well 16Basic quality (D) · 16 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 13.2 GB 16.1 GB 13.8 tok/s Runs well 19Basic quality (D) · 19 of 100: how capable the model is at Q4_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)
Q5_K_M 14.5 GB 16.1 GB 11.4 tok/s Runs welltight fit 19Basic quality (D) · 19 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 55/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
Q6_K 15.9 GB 16.1 GB 9.6 tok/s Slowtight fit 19Basic quality (D) · 19 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. GGUF (opens in a new tab)
Q8_0 19.0 GB 16.1 GB 6.0 tok/s Slowfar over GPU memory limit 20Basic quality (D) · 20 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 33/100 (quality, fit, speed and memory use) sets the ranking. GGUF (opens in a new tab)
F16 31.3 GB 16.1 GB — Won't run 20Basic quality (D) · 20 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 Vicuna 13B v1.5 need on the M6 24GB?

At Q2_K it needs about 10.4 GB. The M6 24GB has 16.1 GB usable.

How fast is Vicuna 13B v1.5 on the M6 24GB?

About 22.7 tok/s at Q2_K, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Vicuna 13B v1.5 should I use on the M6 24GB?

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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