Llama 3 3 Nemotron Super 49B V1 5 — Hardware Requirements & GPU Compatibility
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Llama 3.3 Nemotron Super 49B is a 49.9-billion parameter chat model by NVIDIA, built on a modified Llama 3.3 architecture. It occupies a unique size point between the common 70B and 8B tiers, offering strong reasoning and conversational ability while requiring less VRAM than full 70B models. NVIDIA's Nemotron Super training pipeline applies extensive alignment tuning to optimize helpfulness and factual accuracy. The model typically needs 32GB or more of VRAM for local inference at reduced precision, placing it within reach of high-end consumer GPUs like the RTX 4090 or professional workstation cards.
56.5K downloads
227 likesSpecifications
- Publisher
- NVIDIA
- Family
- Llama 3
- Parameters
- 49.9B
- Architecture
- DeciLMForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-10-15
- License
- Other
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HuggingFace
How Much VRAM Does Llama 3 3 Nemotron Super 49B V1 5 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| BF16 | 16.00 | 109.7 GB | — | 99.73 GB | Brain floating point 16 — preferred for training |
Which GPUs Can Run Llama 3 3 Nemotron Super 49B V1 5?
BF16 · 109.7 GBLlama 3 3 Nemotron Super 49B V1 5 (BF16) requires 109.7 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 143+ GB is recommended. No single GPU has enough memory — multi-GPU or cluster setups are needed.
Which Devices Can Run Llama 3 3 Nemotron Super 49B V1 5?
BF16 · 109.7 GB5 devices with unified memory can run Llama 3 3 Nemotron Super 49B V1 5, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Studio M4 Max (128 GB).
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightRelated Models
Derivatives (4)
Frequently Asked Questions
- How much VRAM does Llama 3 3 Nemotron Super 49B V1 5 need?
Llama 3 3 Nemotron Super 49B V1 5 requires 109.7 GB of VRAM at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 49.9B × 16 bits ÷ 8 = 99.7 GB
KV Cache + Overhead ≈ 10 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
BF16109.7 GB- Can NVIDIA GeForce RTX 5090 run Llama 3 3 Nemotron Super 49B V1 5?
No — Llama 3 3 Nemotron Super 49B V1 5 requires at least 109.7 GB at BF16, which exceeds the NVIDIA GeForce RTX 5090's 32 GB of VRAM.
- Can I run Llama 3 3 Nemotron Super 49B V1 5 on a Mac?
Llama 3 3 Nemotron Super 49B V1 5 requires at least 109.7 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 3 Nemotron Super 49B V1 5 locally?
Yes — Llama 3 3 Nemotron Super 49B V1 5 can run locally on consumer hardware. At BF16 quantization it needs 109.7 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama 3 3 Nemotron Super 49B V1 5?
At BF16, Llama 3 3 Nemotron Super 49B V1 5 can reach ~27 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 ÷ 109.7 × 0.55 = ~27 tok/s
Estimated speed at BF16 (109.7 GB)
AMD Instinct MI300X~27 tok/sAMD Instinct MI250X~16 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 3 Nemotron Super 49B V1 5?
At BF16, the download is about 99.73 GB.