Llama 3 3 Nemotron Super 49B V1 5 — Hardware Requirements & GPU Compatibility
ChatFunctions
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.
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 5
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/unsloth/Llama-3_3-Nemotron-Super-49B-v1_5-GGUF:Q4_K_MQ4_K_M · needs ~37.2 GB · 16K context
Download: Llama-3_3-Nemotron-Super-49B-v1_5-Q4_K_M.gguf30.22 GBunsloth
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 5 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 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 consumer GPU has enough memory. 67 devices with unified memory can run Llama 3 3 Nemotron Super 49B V1 5, 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 5?
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.
- LMArena Text
LMArena · Oct 2026Arena
Arena1343.1
#83/188 open
Alternatives to Llama 3 3 Nemotron Super 49B V1 5
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
- 2025-07-25
- License
- Other
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)
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 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
★ Recommended — best balance of quality and VRAM usage.
Can I run Llama 3 3 Nemotron Super 49B V1 5 on a Mac?
Yes — Mac Mini M5 Pro (24 GB) and 106 other Macs can run Llama 3 3 Nemotron Super 49B V1 5, 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 5 locally?
Yes — Llama 3 3 Nemotron Super 49B V1 5 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 5?
At Q4_K_M, Llama 3 3 Nemotron Super 49B V1 5 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 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?
67 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, 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.