text model · Llama 3.2 Vision · Linux
Can I run Llama 3.2 Vision 11B on Nvidia DGX Spark (128GB)?
Yes. Llama 3.2 Vision 11B runs on Nvidia DGX Spark (128GB) at Q4_K_M (~9 GB of ~112 GB usable).
Runs at Q4_K_M using ~9 GB of ~112 GB usable. You have room for FP16 for higher quality.
That figure is at a 4k context and moves about ±15% as context length changes. Nvidia DGX Spark (128GB) leaves ~103 GB of headroom, room to step up to FP16 for higher quality.
- Q4_K_M needed
- ~9 GB
- Usable on device
- ~112 GB
- Device memory
- 128 GB
- Best quant
- Q4_K_M
Which quant fits
Running cost · estimate
- Power draw
- ~140 W
- Electricity / 1M tokens
- ~$0.24
- Pays for itself after
- ~15,381M tok
At ~$0.15/kWh and the estimated ~24 tok/s, a million generated tokens costs about $0.24 in electricity. Versus a hosted API at ~$0.5 per million tokens, the ~$3,999 Nvidia DGX Spark (128GB) pays for itself after roughly 15,381 million tokens, so local hardware is mostly a fixed cost, not a per-token one. TDP is the peak draw, so this is an upper bound. Assumptions.
Run it
Pick your tool. All 3 load the same Q4_K_M weights.
ollama run llama3.2-vision:11b llama-cli -hf leafspark/Llama-3.2-11B-Vision-Instruct-GGUF:Q4_K_M lms get leafspark/Llama-3.2-11B-Vision-Instruct-GGUF - Parameters
- 10.7B
- Q4_K_M size
- 7.36 GB
- Q8_0 size
- 11.49 GB
- Context
- 128k
- Ollama tag
- llama3.2-vision:11b
- Memory
- 128 GB unified
- Usable for weights
- ~112 GB
- Power draw
- ~140 W
- Best runtime
- llama.cpp (CUDA) / Ollama (CUDA)
You could also run
Run Llama 3.2 Vision 11B on other hardware
FAQ
Can Nvidia DGX Spark (128GB) run Llama 3.2 Vision 11B?
Yes. Llama 3.2 Vision 11B runs on Nvidia DGX Spark (128GB) at Q4_K_M (~9 GB of ~112 GB usable).
How much memory does Llama 3.2 Vision 11B need?
Nvidia DGX Spark (128GB) has room to spare. At Q4_K_M the weights are ~7.36 GB; with KV cache and runtime overhead, budget ~9 GB at a 4k context.
What is the best tool to run Llama 3.2 Vision 11B on Linux?
Ollama for a simple setup; vLLM for the most speed. vLLM shines for multi-user serving/throughput. For a single local chat, Ollama or llama.cpp is simpler and lighter.
Embed this
A live badge for your model card or README, updated as the data is. [](https://localmodel.run/can-i-run/llama-3.2-vision-11b/nvidia-dgx-spark-128gb) Sources
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docs.nvidia.com · 2 sources
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github.com · 1 source
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huggingface.co · 2 sources
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nvidia.com · 1 source
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nvidianews.nvidia.com · 1 source
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ollama.com · 3 sources
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tomshardware.com · 1 source
Weights are measured from GGUF files; KV cache and overhead are computed, so totals can vary ~15% with context and runtime. Any tok/s is a bandwidth estimate. See methodology.