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Can the Intel Core i7-14700 NAS (128 GB) run Llama 3.2 11B Vision Instruct?

35Basic quality (D) · 35 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.

Yes, it runs well

Runs well. At Q3_K_M it needs about 16.2 GB of 113 GB usable, with an estimated speed of 11.1 tok/s.

Best quant
Q3_K_M
Generation speed
11.1 tok/s
Prompt processing
50.6 tok/s
First token (1K prompt)
20.2 s
Largest safe context
128K

Best app to run it on the Intel Core i7-14700 NAS (128 GB)

On Linux

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 llama3.2-vision:11b

How we choose: the files published for this model, which apps run in a container on a NAS without a graphics card (models run on its processor), and whether the model fits fully in the memory models can use. Details.

Download Llama 3.2 11B Vision Instruct

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 llama3.2-vision:11b

Original weights · Hugging Face

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

huggingface.co/meta-llama/Llama-3.2-11B-Vision-Instruct (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).

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

How it’s rated

Quality 35Basic quality (D) · 35 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.
How capable the model itself is at Q3_K_M, 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 Intel Core i7-14700 NAS (128 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.15.9 / 45
Memory fitFull marks when the model fits in the share of the NAS’s memory that models can use (the rest stays with the NAS system and its storage services); 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.7.9 / 15
Memory useRewards using the memory you have, so a bigger, better variant beats a tiny one that leaves most memory idle.3.3 / 15
Match score52.1 / 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 Intel Core i7-14700 NAS (128 GB)

Quant Memory needed Available Speed Runs Quality Download
Q2_K 15.2 GB 113 GB 14.1 tok/s Runs well 29Basic quality (D) · 29 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 50/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q3_K_M 16.2 GB 113 GB 11.1 tok/s Runs well 35Basic quality (D) · 35 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. All files (opens in a new tab)
Q4_K_M 17.5 GB 113 GB 8.6 tok/s Slow 39Basic quality (D) · 39 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking.
Q5_K_M 18.5 GB 113 GB 7.1 tok/s Slow 40Fair quality (C) · 40 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q6_K 19.7 GB 113 GB 6.0 tok/s Slow 41Fair quality (C) · 41 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking. All files (opens in a new tab)
Q8_0 22.3 GB 113 GB 4.4 tok/s Slow 42Fair quality (C) · 42 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 53/100 (quality, fit, speed and memory use) sets the ranking.
F16 32.3 GB 113 GB 2.0 tok/s Very slow 42Fair quality (C) · 42 of 100: how capable the model is at F16, whatever it runs on. Match score 53/100 (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 Llama 3.2 11B Vision Instruct need on the Intel Core i7-14700 NAS 128GB?

At Q3_K_M it needs about 16.2 GB. The Intel Core i7-14700 NAS 128GB has 113 GB usable.

How fast is Llama 3.2 11B Vision Instruct on the Intel Core i7-14700 NAS 128GB?

About 11.1 tok/s at Q3_K_M, an estimate from the hardware’s memory bandwidth and the size of the model.

Which quantization of Llama 3.2 11B Vision Instruct should I use on the Intel Core i7-14700 NAS 128GB?

Q3_K_M. 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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