Llama 3 3 Nemotron Super 49B V1 5 — Hardware Requirements & GPU Compatibility
Chat
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
- 2025-07-25
- 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 |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 23.3 GB | — | 21.19 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 24 GB | — | 21.82 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 26.7 GB | — | 24.31 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 27.4 GB | — | 24.93 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 32.9 GB | — | 29.92 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 39.1 GB | — | 35.53 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 45.3 GB | — | 41.14 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 54.9 GB | — | 49.87 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Llama 3 3 Nemotron Super 49B V1 5?
Q4_K_M · 32.9 GBLlama 3 3 Nemotron Super 49B V1 5 (Q4_K_M) requires 32.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 43+ 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?
Q4_K_M · 32.9 GB29 devices with unified memory can run Llama 3 3 Nemotron Super 49B V1 5, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Pro 16" M4 Max (48 GB).
Runs great
— Plenty of headroom
Where to Download Llama 3 3 Nemotron Super 49B V1 5
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
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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 32.9 GB of VRAM at Q4_K_M, or 109.7 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 49.9B × 4.8 bits ÷ 8 = 29.9 GB
KV Cache + Overhead ≈ 3 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M32.9 GB- Can NVIDIA GeForce RTX 4090 run Llama 3 3 Nemotron Super 49B V1 5?
Yes, at Q3_K_S (24 GB) or lower. Higher quantizations like IQ3_M (24.7 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Llama 3 3 Nemotron Super 49B V1 5?
For Llama 3 3 Nemotron Super 49B V1 5, Q4_K_M (32.9 GB) offers the best balance of quality and VRAM usage. Q4_K_L (33.6 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 15.1 GB.
VRAM requirement by quantization
IQ2_XXS15.1 GBQ2_K23.3 GBQ3_K_L28.1 GBQ4_K_M ★32.9 GBQ5_034.3 GBBF16109.7 GB★ Recommended — best balance of quality and VRAM usage.
- 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 15.1 GB at IQ2_XXS, 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 Q4_K_M quantization it needs 32.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Llama 3 3 Nemotron Super 49B V1 5?
At Q4_K_M, Llama 3 3 Nemotron Super 49B V1 5 can reach ~134 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 ÷ 32.9 × 0.65 = ~158 tok/s
Estimated speed at Q4_K_M (32.9 GB)
~158 tok/s~158 tok/s~134 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 Q4_K_M, the download is about 29.92 GB. The full-precision BF16 version is 99.73 GB. The smallest option (IQ2_XXS) is 13.71 GB.
- Which GPUs can run Llama 3 3 Nemotron Super 49B V1 5?
No single consumer GPU has enough VRAM to run Llama 3 3 Nemotron Super 49B V1 5 at Q4_K_M (32.9 GB). Multi-GPU or professional hardware is required.
- Which devices can run Llama 3 3 Nemotron Super 49B V1 5?
29 devices with unified memory can run Llama 3 3 Nemotron Super 49B V1 5 at Q4_K_M (32.9 GB), including ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.