Llama 3.2 11B Vision Instruct — Hardware Requirements & GPU Compatibility
VisionFunctions
Llama 3.2 11B Vision Instruct is a smaller vision-language model in Meta's Llama 3 family, with roughly 11 billion parameters, built to process images together with text and follow chat-style instructions. It brings multimodal capability, such as visual question answering and image description, to a size that is far more approachable than the 90-billion-parameter variant from the same release. Released in September 2024 under Meta's Llama 3.2 community license, it does not carry a fully permissive open license but does allow broad use under Meta's terms. At around 11 billion parameters, it fits comfortably on a single mid-range to high-end consumer GPU once quantized to 4-bit, putting it within reach of enthusiast local setups.
Runs great on a 24 GB GPU
Runs from 8 GB (Q4_K_M · 4K)Best on 24 GB (Q4_K_M · ~93 tok/s · 32K)Macs from 16 GB (Q4_K_M · ~20 tok/s)
llama3.2
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Llama 3.2 11B Vision Instruct
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run llama3.2-vision:11bQ4_K_M fits fully on the RTX 4090 · 14.2 GB free · 16K context
Download: Llama-3.2-11B-Vision-Instruct.Q4_K_M.gguf5.96 GBleafspark
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 24 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX 3080 Ti (12 GB) (2 GB headroom) · Q4_K_M
- Generation speed
- ~84 tok/s
- generation speed
- Cost per 1M output tokens
- $0.40
- per 1M output tokens
How Much VRAM Does Llama 3.2 11B Vision Instruct Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 5.0 GB | 11 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 32K context · ~131 tok/s | |
| 5.7 GB | 11.7 GB | 3-bit medium — 3-bit medium quantization | Fits: 32K context · ~115 tok/s | |
| 7.0 GB | 13 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 32K context · ~93 tok/s | |
| 8.4 GB | 14.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 32K context · ~78 tok/s | |
| 9.7 GB | 15.6 GB | 6-bit, very good — 6-bit quantization, very good quality | Fits: 32K context · ~68 tok/s | |
| 11.7 GB | 17.7 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Fits: 32K context · ~56 tok/s | |
| 23.5 GB | 29.4 GB | 16-bit · bfloat — Brain floating point 16 — preferred for training | Fits: 4K context · ~28 tok/s |
Selected Q4_K_M
4-bit medium quantization9.8GBat 16K context
- Weights
- 6.3 GB
- KV cache
- 3.2 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 14.2 GB free · ~93 tok/s
≥50 tok/s needs ≥541 GB/s · RTX 5090 ~166, RTX 3090 Ti ~93
Which Hardware Can Run Llama 3.2 11B Vision Instruct?
Q4_K_M · 7.0 GBLlama 3.2 11B Vision Instruct (Q4_K_M) requires 7.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 10+ GB is recommended. 52 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3080. 151 devices with unified memory can run Llama 3.2 11B Vision Instruct, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), NVIDIA Jetson Orin Nano 8GB (Super).
12 GB
12 GB tier →fits · 2.2 GB free · up to 16K ctx
10 GPUs
RTX 3080 Ti~84Simulate output on RTX 3080 TiRTX 5070~62Simulate output on RTX 5070RTX 4070~47Simulate output on RTX 407020 GB
fits · 10.2 GB free · up to 32K ctx
1 GPU
RX 7900 XT~68Simulate output on RX 7900 XT32–40 GB
fits · 22.2 GB free · up to 32K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~166Simulate output on RTX 5090M5 Max36 GB~46Simulate output on Mac Studio M5 Max (36 GB)M532 GB~15Simulate output on MacBook Air 13" M5 (32 GB)Orin~19Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~13Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Llama 3.2 11B Vision Instruct?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
Here are all 3 boards that have results for this model, sorted by their weight in the llmrun Score; badges show who ran the test.
Alternatives to Llama 3.2 11B Vision Instruct
Llama 3 family · 65 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| GLM 4.1V 9B ThinkingZ.ai | — | 7.2 GB | ~100 | same size |
| Qwen2.5 Omni 7BAlibaba | — | 9.9 GB | ~93 | same size |
| INTELLECT 1 InstructPrimeIntellect | — | 7.8 GB | ~97 | same size |
| Falcon 11BTII UAE | — | 8.5 GB | ~89 | same size |
| GigaChat3.1 Audio 10B A1.8Bai-sage | — | 22.9 GB | ~198 | same size |
| Bielik 11B V3.0 Instructspeakleash | — | 27.4 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- Meta
- Family
- Llama 3
- Parameters
- 10.7B
- Release Date
- 2024-09-18
- License
- llama3.2
Frequently Asked Questions
How much VRAM does Llama 3.2 11B Vision Instruct need?
Llama 3.2 11B Vision Instruct requires 7.0 GB of VRAM at Q4_K_M, or 23.5 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 10.7B × 4.8 bits ÷ 8 = 6.4 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
What's the best quantization for Llama 3.2 11B Vision Instruct?
For Llama 3.2 11B Vision Instruct, Q4_K_M (7.0 GB) offers the best balance of quality and VRAM usage. Q5_K_M (8.4 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 5.0 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Llama 3.2 11B Vision Instruct on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Llama 3.2 11B Vision Instruct. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 7.0 GB of usable unified memory (RAM minus macOS overhead).
Can I run Llama 3.2 11B Vision Instruct locally?
Yes — Llama 3.2 11B Vision Instruct can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Llama 3.2 11B Vision Instruct?
At Q4_K_M, Llama 3.2 11B Vision Instruct can reach ~682 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~93 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: NVIDIA B200 → 8000 ÷ 7.04 × 0.65 = ~739 tok/s
Estimated speed at Q4_K_M (7.0 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Llama 3.2 11B Vision Instruct?
At Q4_K_M, the download is about 6.40 GB. The full-precision BF16 version is 21.34 GB. The smallest option (Q2_K) is 4.53 GB.
Which GPUs can run Llama 3.2 11B Vision Instruct?
52 consumer GPUs can run Llama 3.2 11B Vision Instruct at Q4_K_M. Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 38 GPUs have plenty of headroom for comfortable inference.
Which devices can run Llama 3.2 11B Vision Instruct?
151 devices with unified memory can run Llama 3.2 11B Vision Instruct at Q4_K_M (7.0 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.