Llama 3.2 11B Vision Instruct on NVIDIA RTX A1000 Laptop GPU

Can the NVIDIA RTX A1000 Laptop GPU run Llama 3.2 11B Vision Instruct?

Yes, but slowly (heavy RAM offload)

Slow (heavy RAM offload). At Q4_K_M it needs about 8.7 GB of 4.0 GB usable, with an estimated speed of 9.6 tok/s.

Best quant: Q4_K_M
Generation speed: 9.6 tok/s
Prompt processing: 253 tok/s
First token (1K prompt): 4.0 s
Largest safe context: —

Best app to run it on the NVIDIA RTX A1000 Laptop GPU

On Windows

Ollama

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

Splits the model between GPU memory and system RAM, which this setup needs.
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-instruct-q4_K_M

Docker Model Runner Splits the model between GPU memory and system RAM, which this setup needs.
Text Generation WebUI Splits the model between GPU memory and system RAM, which this setup needs.

On Linux

Ollama

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

Splits the model between GPU memory and system RAM, which this setup needs.
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-instruct-q4_K_M

Docker Model Runner Splits the model between GPU memory and system RAM, which this setup needs.
Text Generation WebUI Splits the model between GPU memory and system RAM, which this setup needs.

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 Llama 3.2 11B Vision Instruct

Recommended for the NVIDIA RTX A1000 Laptop GPU: Q4_K_M. 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 llama3.2-vision:11b-instruct-q4_K_M

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 39Basic quality (D) · 39 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking.: How capable the model itself is at Q4_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 Slowheavy RAM offload: How well it runs on the NVIDIA RTX A1000 Laptop GPU: 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.	18.0 / 45
Memory fitFull marks when everything sits in GPU memory; fewer when part spills to system RAM.	4.0 / 25
SpeedGeneration speed against a 40 tokens/s target for LLMs, or render time against a target for image and video.	7.3 / 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 score	44.3 / 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 NVIDIA RTX A1000 Laptop GPU

Quant	Memory needed	Available	Speed	Runs	Quality	Download
`Q2_K`	6.4 GB	4.0 GB	18.1 tok/s	Slowheavy RAM offload	29Basic quality (D) · 29 of 100: how capable the model is at Q2_K, whatever it runs on. Match score 42/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`Q3_K_M`	7.4 GB	4.0 GB	13.2 tok/s	Slowheavy RAM offload	35Basic quality (D) · 35 of 100: how capable the model is at Q3_K_M, whatever it runs on. Match score 43/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`Q4_K_M`	8.7 GB	4.0 GB	9.6 tok/s	Slowheavy RAM offload	39Basic quality (D) · 39 of 100: how capable the model is at Q4_K_M, whatever it runs on. Match score 44/100 (quality, fit, speed and memory use) sets the ranking.
`Q5_K_M`	9.7 GB	4.0 GB	—	Won't run	40Fair quality (C) · 40 of 100: how capable the model is at Q5_K_M, whatever it runs on. Match score 5/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`Q6_K`	10.9 GB	4.0 GB	—	Won't run	41Fair quality (C) · 41 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 5/100 (quality, fit, speed and memory use) sets the ranking.	All files (opens in a new tab)
`Q8_0`	13.5 GB	4.0 GB	—	Won't run	42Fair quality (C) · 42 of 100: how capable the model is at Q8_0, whatever it runs on. Match score 5/100 (quality, fit, speed and memory use) sets the ranking.
`F16`	23.5 GB	4.0 GB	—	Won't run	42Fair quality (C) · 42 of 100: how capable the model is at F16, whatever it runs on. Match score 5/100 (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

42Fair quality (C) · 42 of 100: how capable the model is at F16, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5090 F16 · 48.4 tok/s · Runs great
41Fair quality (C) · 41 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. AMD Radeon RX 9070 XT Q6_K · 44.0 tok/s · Runs great
41Fair quality (C) · 41 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4070 Ti SUPER Q6_K · 53.6 tok/s · Runs great
41Fair quality (C) · 41 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 4080 SUPER Q6_K · 58.7 tok/s · Runs great
41Fair quality (C) · 41 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5070 Ti Q6_K · 71.5 tok/s · Runs great
41Fair quality (C) · 41 of 100: how capable the model is at Q6_K, whatever it runs on. Match score 73/100 (quality, fit, speed and memory use) sets the ranking. NVIDIA GeForce RTX 5080 Q6_K · 76.6 tok/s · Runs great

Quick answers

How much memory does Llama 3.2 11B Vision Instruct need on the NVIDIA RTX A1000 Laptop GPU 4GB?

At Q4_K_M it needs about 8.7 GB. The NVIDIA RTX A1000 Laptop GPU 4GB has 4.0 GB usable.

How fast is Llama 3.2 11B Vision Instruct on the NVIDIA RTX A1000 Laptop GPU 4GB?

About 9.6 tok/s at Q4_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 NVIDIA RTX A1000 Laptop GPU 4GB?

Q4_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.