Llama 3.2 11B Vision Instruct — Hardware Requirements & GPU Compatibility
Vision
Llama 3.2 11B Vision Instruct is a 10.7B-parameter open language model from Meta in the Llama 3 family. At BF16 it needs about 23.47 GB of VRAM — see which GPUs and Macs can run it below.
166.6K downloads
1.6K likesSpecifications
- Publisher
- Meta
- Family
- Llama 3
- Parameters
- 10.7B
- License
- llama3.2
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HuggingFace
How Much VRAM Does Llama 3.2 11B Vision Instruct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 23.5 GB | — | 21.34 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Llama 3.2 11B Vision Instruct?
BF16 · 23.5 GBLlama 3.2 11B Vision Instruct (BF16) requires 23.5 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 31+ GB is recommended. 5 GPUs can run it, including NVIDIA GeForce RTX 5090.
All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).
Which Devices Can Run Llama 3.2 11B Vision Instruct?
BF16 · 23.5 GB21 devices with unified memory can run Llama 3.2 11B Vision Instruct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightBenchmarks
View all 1 →Related Models
Frequently Asked Questions
- How much VRAM does Llama 3.2 11B Vision Instruct need?
Llama 3.2 11B Vision Instruct requires 23.5 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 10.7B × 16 bits ÷ 8 = 21.3 GB
KV Cache + Overhead ≈ 2.2 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF1623.5 GB- Can I run Llama 3.2 11B Vision Instruct on a Mac?
Llama 3.2 11B Vision Instruct requires at least 23.5 GB at BF16, which exceeds the unified memory of most consumer Macs. You would need a Mac Studio or Mac Pro with a high-memory configuration.
- Can I run Llama 3.2 11B Vision Instruct locally?
Yes — Llama 3.2 11B Vision Instruct can run locally on consumer hardware. At BF16 quantization it needs 23.5 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama 3.2 11B Vision Instruct?
At BF16, Llama 3.2 11B Vision Instruct can reach ~124 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~28 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 23.5 × 0.55 = ~124 tok/s
Estimated speed at BF16 (23.5 GB)
~124 tok/s~28 tok/s~93 tok/s~77 tok/sReal-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 BF16, the download is about 21.34 GB.
- Which GPUs can run Llama 3.2 11B Vision Instruct?
5 consumer GPUs can run Llama 3.2 11B Vision Instruct at BF16 (23.5 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090.
- Which devices can run Llama 3.2 11B Vision Instruct?
21 devices with unified memory can run Llama 3.2 11B Vision Instruct at BF16 (23.5 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.