NVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16 — Hardware Requirements & GPU Compatibility
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NVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16 is a 75.4B-parameter open language model from NVIDIA in the Nemotron family. It supports a context window of up to 262,144 tokens. At BF16 it needs about 165.78 GB of VRAM — see which GPUs and Macs can run it below.
3.3K downloads
55 likesSpecifications
- Publisher
- NVIDIA
- Family
- Nemotron
- Parameters
- 75.4B
- Architecture
- NemotronHPuzzleForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2026-06-24
- License
- Other
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How Much VRAM Does NVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16est. | 16.00 | 165.8 GB | — | 150.71 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run NVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16?
BF16 · 165.8 GBNVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16 (BF16) requires 165.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 216+ GB is recommended. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run NVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16?
BF16 · 165.8 GB6 devices with unified memory can run NVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio (M3 Ultra, 256GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightWhere to Download NVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does NVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16 need?
NVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16 requires 165.8 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 75.4B × 16 bits ÷ 8 = 150.7 GB
KV Cache + Overhead ≈ 15.1 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF16165.8 GB- Can NVIDIA GeForce RTX 5090 run NVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16?
No — NVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16 requires at least 165.8 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run NVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16 on a Mac?
NVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16 requires at least 165.8 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 NVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16 locally?
Yes — NVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16 can run locally on consumer hardware. At BF16 quantization it needs 165.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is NVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16?
At BF16, NVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16 can reach ~27 tok/s on AMD Instinct MI350X. 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 ÷ 165.8 × 0.65 = ~31 tok/s
Estimated speed at BF16 (165.8 GB)
~31 tok/s~31 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 NVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16?
At BF16, the download is about 150.71 GB.
- Which GPUs can run NVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16?
No single consumer GPU has enough VRAM to run NVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16 at BF16 (165.8 GB). Multi-GPU or professional hardware is required.
- Which devices can run NVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16?
6 devices with unified memory can run NVIDIA Nemotron Labs 3 Puzzle 75B A9B BF16 at BF16 (165.8 GB), including Mac Pro M2 Ultra (192 GB), Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.