OpenReasoning Nemotron 32B — Hardware Requirements & GPU Compatibility
ChatCodeReasoning
OpenReasoning Nemotron 32B is a 32.8B-parameter open language model from NVIDIA. It supports a context window of up to 131,072 tokens. At BF16 it needs about 66.36 GB of VRAM — see which GPUs and Macs can run it below.
702 downloads
126 likesSpecifications
- Publisher
- NVIDIA
- Parameters
- 32.8B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2025-09-16
- License
- CC BY 4.0
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HuggingFace
How Much VRAM Does OpenReasoning Nemotron 32B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 66.4 GB | 100.2 GB | 65.53 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run OpenReasoning Nemotron 32B?
BF16 · 66.4 GBOpenReasoning Nemotron 32B (BF16) requires 66.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 87+ GB is recommended. Using the full 131K context window can add up to 33.8 GB, bringing total usage to 100.2 GB. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run OpenReasoning Nemotron 32B?
BF16 · 66.4 GB5 devices with unified memory can run OpenReasoning Nemotron 32B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
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Frequently Asked Questions
- How much VRAM does OpenReasoning Nemotron 32B need?
OpenReasoning Nemotron 32B requires 66.4 GB of VRAM at BF16. Full 131K context adds up to 33.8 GB (100.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 32.8B × 16 bits ÷ 8 = 65.5 GB
KV Cache + Overhead ≈ 0.9 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 34.7 GB (at full 131K context)
VRAM usage by quantization
BF1666.4 GBBF16 + full context100.2 GB- Can NVIDIA GeForce RTX 5090 run OpenReasoning Nemotron 32B?
No — OpenReasoning Nemotron 32B requires at least 66.4 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run OpenReasoning Nemotron 32B on a Mac?
OpenReasoning Nemotron 32B requires at least 66.4 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 OpenReasoning Nemotron 32B locally?
Yes — OpenReasoning Nemotron 32B can run locally on consumer hardware. At BF16 quantization it needs 66.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is OpenReasoning Nemotron 32B?
At BF16, OpenReasoning Nemotron 32B can reach ~44 tok/s on AMD Instinct MI300X. 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 ÷ 66.4 × 0.55 = ~44 tok/s
Estimated speed at BF16 (66.4 GB)
~44 tok/s~33 tok/s~27 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of OpenReasoning Nemotron 32B?
At BF16, the download is about 65.53 GB.
- Which GPUs can run OpenReasoning Nemotron 32B?
No single consumer GPU has enough VRAM to run OpenReasoning Nemotron 32B at BF16 (66.4 GB). Multi-GPU or professional hardware is required.
- Which devices can run OpenReasoning Nemotron 32B?
5 devices with unified memory can run OpenReasoning Nemotron 32B at BF16 (66.4 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio M2 Ultra (192 GB), Mac Studio M4 Max (128 GB), NVIDIA DGX A100 640GB. Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.