Llama 3 3 Nemotron Super 49B V1 — Hardware Requirements & GPU Compatibility
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Llama 3 3 Nemotron Super 49B V1 is a 49.9B-parameter open language model from NVIDIA in the Llama 3 family. It supports a context window of up to 131,072 tokens. At Q4_K_M it needs about 32.91 GB of VRAM — see which GPUs and Macs can run it below.
Decent on a 24 GB GPU
Runs on 24 GB (IQ2_M · ~35 tok/s · 16K)Macs from 24 GB (IQ2_XXS · ~14 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Llama 3 3 Nemotron Super 49B V1
One recommended way. Every other method, OS and quant is in the step-by-step guide.Each way opens a step-by-step guide for your OS and quant.
Recommended
ollama run hf.co/bartowski/nvidia_Llama-3_3-Nemotron-Super-49B-v1-GGUF:Q4_K_MQ4_K_M · needs ~37.2 GB · 16K context
Download: nvidia_Llama-3_3-Nemotron-Super-49B-v1-Q4_K_M.gguf30.22 GBbartowski
Ways to runOther ways
each opens step-by-stepRun in cloud
Your own private copy · Q4_K_M · 48 GB GPU
- The easiest way to try this model
- Running in a few minutes, easy setup
- Chat right here or connect any app
$5+ bonus for new users
Runs on RTX A6000 (48 GB) (10 GB headroom) · Q4_K_M
- Generation speed
- ~15 tok/s
- generation speed
- Cost per 1M output tokens
- $7.39
- per 1M output tokens
How Much VRAM Does Llama 3 3 Nemotron Super 49B V1 Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 23.3 GB | 32.5 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 4K context · ~28 tok/s | |
| 24 GB | 33.2 GB | 3-bit small — 3-bit small quantization | Fits: 2K context · ~27 tok/s | |
| 26.7 GB | 35.9 GB | 3-bit medium — 3-bit medium quantization | Does not fit: needs 31 GB | |
| 27.4 GB | 36.6 GB | 4-bit · legacy — 4-bit legacy quantization | Does not fit: needs 31.7 GB | |
| 32.9 GB | 42.1 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Does not fit: needs 37.2 GB | |
| 39.1 GB | 48.3 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Does not fit: needs 43.4 GB | |
| 45.3 GB | 54.4 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 49.5 GB | |
| 54.9 GB | 64 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 59.1 GB |
Selected Q4_K_M
4-bit medium quantization37.2GBat 16K context
- Weights
- 32.0 GB
- KV cache
- 4.9 GB
- Overhead
- 0.3 GB
Doesn't fit RTX 4090 (reference) · needs 13.2 GB more, IQ2_M fits
Or .
Which Hardware Can Run Llama 3 3 Nemotron Super 49B V1?
Q4_K_M · 32.9 GBLlama 3 3 Nemotron Super 49B V1 (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 consumer GPU has enough memory. 67 devices with unified memory can run Llama 3 3 Nemotron Super 49B V1, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M5 Max (48 GB).
20 GB
Q4_K_M · best fit IQ2_XXS (19.4 GB)
1 GPU
RX 7900 XT~32Simulate output on RX 7900 XT32–40 GB
Q4_K_M · best fit Q4_0 (31.7 GB)
1 GPU · 3 Mac classes · 1 device family
RTX 5090~43Simulate output on RTX 5090M5 Max36 GB~12Simulate output on Mac Studio M5 Max (36 GB)M532 GB~4Simulate output on MacBook Air 13" M5 (32 GB)Orin~5Simulate output on NVIDIA Jetson AGX Orin 32GB48 GB
48 GB tier →fits · 10.8 GB free · up to 32K ctx
2 Mac classes · 1 device family
M5 Max48 GB~13Simulate output on Mac Studio M5 Max (48 GB)M5 Pro48 GB~7Simulate output on Mac Mini M5 Pro (48 GB)Snapdragon X2 Elite Extreme (X2E-96-100)~5Simulate output on Snapdragon X2 Elite Extreme Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
How good is Llama 3 3 Nemotron Super 49B V1?
llmrun Score blends public benchmark boards into one number; 100 = the average open model.
Not enough data yet for an llmrun Score.
A Score needs results on enough independent boards. How the Composite Score is built
Benchmarks
This is the single board showing results for this model; badges show who ran the test.
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Alternatives to Llama 3 3 Nemotron Super 49B V1
Llama 3 family · 65 models →Similar models
4| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Mixtral 8x7B Instruct v0.1Mistral AI | 65.8 | 23.5 GB | ~92 | same size |
| Kimi Linear 48B A3B BaseMoonshot AI | — | 23.4 GB | ~275 | same size |
| AI21 Jamba Mini 1.5AI21 Labs | — | 117.8 GB | —needs > 24 GB | same size |
| Nemotron H 47B Reasoning 128KNVIDIA | — | 107.2 GB | —needs > 24 GB | same size |
Specifications
- Publisher
- NVIDIA
- Family
- Llama 3
- Parameters
- 49.9B
- Architecture
- DeciLMForCausalLM
- Context Length
- 131,072 tokens
- Vocabulary Size
- 128,256
- Release Date
- 2025-03-16
- License
- Other
Frequently Asked Questions
How much VRAM does Llama 3 3 Nemotron Super 49B V1 need?
Llama 3 3 Nemotron Super 49B V1 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)
Fit ratings and hardware model lists check this model with room for a 16K-token context, which needs a little more memory.
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Llama 3 3 Nemotron Super 49B V1?
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?
For Llama 3 3 Nemotron Super 49B V1, 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
★ Recommended — best balance of quality and VRAM usage.
Can I run Llama 3 3 Nemotron Super 49B V1 on a Mac?
Yes — Mac Mini M5 Pro (24 GB) and 106 other Macs can run Llama 3 3 Nemotron Super 49B V1, the smallest at IQ2_XXS. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 32.9 GB of usable unified memory (RAM minus macOS overhead), from Mac Studio M5 Max (48 GB).
Can I run Llama 3 3 Nemotron Super 49B V1 locally?
Yes — Llama 3 3 Nemotron Super 49B V1 can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Llama 3 3 Nemotron Super 49B V1?
At Q4_K_M, Llama 3 3 Nemotron Super 49B V1 can reach ~146 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)
Real-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?
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?
No single consumer GPU has enough VRAM to run Llama 3 3 Nemotron Super 49B V1 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?
67 devices with unified memory can run Llama 3 3 Nemotron Super 49B V1 at Q4_K_M (32.9 GB), including ASUS Ascent GX10, Beelink GTR9 Pro (Ryzen AI Max+ 395, 128 GB), Framework Desktop (Ryzen AI Max+ 395, 128 GB), GMKtec EVO-X2 (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.