Nemotron Cascade 2 30B A3B — Hardware Requirements & GPU Compatibility
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Nemotron Cascade 2 30B A3B is a 31.6B-parameter open language model from NVIDIA in the Nemotron family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 19.32 GB of VRAM — see which GPUs and Macs can run it below.
Runs great on a 24 GB GPU
Runs from 12 GB (IQ2_M · 128K)Best on 24 GB (Q4_K_M · ~161 tok/s · 256K)Macs from 16 GB (IQ2_M · ~52 tok/s)
Other
Reference GPU · RTX 4090 24 GB
Estimates, ±20% · how we estimate
Run Nemotron Cascade 2 30B A3B
One recommended way. Every other method, OS and quant is in the step-by-step guide.
Recommended
ollama run nemotron-cascade-2:30bQ4_K_M fits fully on the RTX 4090 · 4.7 GB free · 16K context
Download: nvidia_Nemotron-Cascade-2-30B-A3B-Q4_K_M.gguf24.73 GBbartowski
Other ways
each opens step-by-stepRun in cloud
Fits on Tesla P40 (24 GB) (4 GB headroom) · Q4_K_M
- Generation speed
- ~79 tok/s
- generation speed
- Cost per 1M output tokens
- $0.43
- per 1M output tokens
How Much VRAM Does Nemotron Cascade 2 30B A3B Need?
Select a quantization level; the listed hardware adheres to it.
| Quantization | VRAM | +32K | On RTX 4090 (reference) | |
|---|---|---|---|---|
| 13.8 GB | 13.9 GB | 2-bit · K-quant — 2-bit quantization with K-quant improvements | Fits: 256K context · ~191 tok/s | |
| 14.2 GB | 14.3 GB | 3-bit small — 3-bit small quantization | Fits: 256K context · ~188 tok/s | |
| 15.8 GB | 15.9 GB | 3-bit medium — 3-bit medium quantization | Fits: 256K context · ~179 tok/s | |
| 16.2 GB | 16.2 GB | 4-bit · legacy — 4-bit legacy quantization | Fits: 256K context · ~177 tok/s | |
| 19.3 GB | 19.4 GB | 4-bit medium — 4-bit medium quantization — most popular sweet spot | Fits: 256K context · ~161 tok/s | |
| 22.9 GB | 23 GB | 5-bit medium — 5-bit medium quantization — good quality/size tradeoff | Fits: 128K context · ~147 tok/s | |
| 26.4 GB | 26.5 GB | 6-bit, very good — 6-bit quantization, very good quality | Does not fit: needs 26.4 GB | |
| 31.9 GB | 32 GB | 8-bit, near-lossless — 8-bit quantization, near-lossless | Does not fit: needs 32 GB |
Selected Q4_K_M
4-bit medium quantization19.3GBat 16K context
- Weights
- 19.0 GB
- KV cache
- 0.1 GB
- Overhead
- 0.3 GB
Fits RTX 4090 (reference) · 4.7 GB free · ~161 tok/s
≥50 tok/s needs ≥196 GB/s · RTX 5090 ~211, RTX 3090 Ti ~161
Which Hardware Can Run Nemotron Cascade 2 30B A3B?
Q4_K_M · 19.3 GBNemotron Cascade 2 30B A3B (Q4_K_M) requires 19.3 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 26+ GB is recommended. Using the full 262K context window can add up to 1.2 GB, bringing total usage to 20.5 GB. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti. 122 devices with unified memory can run Nemotron Cascade 2 30B A3B, including Mac Studio (M3 Ultra, 256GB), Mac Studio (M3 Ultra, 512GB), Mac Studio M1 Max (32 GB).
20 GB
fits · 0.7 GB free · up to 128K ctx
1 GPU
RX 7900 XT~67Simulate output on RX 7900 XT32–40 GB
fits · 12.7 GB free · up to 256K ctx
1 GPU · 3 Mac classes · 4 device families
RTX 5090~211Simulate output on RTX 5090M5 Max36 GB~58Simulate output on Mac Studio M5 Max (36 GB)M532 GB~33Simulate output on MacBook Air 13" M5 (32 GB)Orin~52Simulate output on NVIDIA Jetson AGX Orin 32GBSnapdragon X Elite (X1E-80-100)~33Simulate output on Snapdragon X Elite Copilot+ PC
By VRAM tier · typical GPUs and Macs at Q4_K_M
Alternatives to Nemotron Cascade 2 30B A3B
Nemotron family · 42 models →Similar models
6| Model | Score | VRAM | tok/s | Why it is similar |
|---|---|---|---|---|
| Hy Memory 30B A3BTencent | — | 19.1 GB | ~184 | same size |
| Llm Jp 4.1 32B A3b Thinkingllm-jp | — | 20.1 GB | ~188 | same size |
| Llm Jp 4.1 33B Thinkingllm-jp | — | 22.5 GB | ~35 | same size |
| Agnes 3.0 FlashAgnes-AI | — | 22.1 GB | ~35 | same size |
| Muse Glimmer 30BMeta | — | 19.6 GB | ~36 | same size |
| Hy MT2 30B A3BTencent | — | 19.1 GB | ~170 | same size |
Fine-tunes & variants(2)
Specifications
- Publisher
- NVIDIA
- Family
- Nemotron
- Parameters
- 31.6B
- Active parameters
- 3.6B per token
- Architecture
- NemotronHForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 131,072
- Release Date
- 2026-03-18
- License
- Other
Frequently Asked Questions
How much VRAM does Nemotron Cascade 2 30B A3B need?
Nemotron Cascade 2 30B A3B requires 19.3 GB of VRAM at Q4_K_M, or 63.5 GB at BF16. Full 262K context adds up to 1.2 GB (20.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 31.6B × 4.8 bits ÷ 8 = 18.9 GB
KV Cache + Overhead ≈ 0.4 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.
KV Cache + Overhead ≈ 1.6 GB (at full 262K context)
VRAM usage by quantization
Can NVIDIA GeForce RTX 4090 run Nemotron Cascade 2 30B A3B?
Yes, at Q5_K_L (23.3 GB) or lower. Higher quantizations like Q6_K (26.4 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
What's the best quantization for Nemotron Cascade 2 30B A3B?
For Nemotron Cascade 2 30B A3B, Q4_K_M (19.3 GB) offers the best balance of quality and VRAM usage. Q4_K_L (19.7 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 9.1 GB.
VRAM requirement by quantization
★ Recommended — best balance of quality and VRAM usage.
Can I run Nemotron Cascade 2 30B A3B on a Mac?
Yes — Mac Mini M2 Pro (16 GB) and 133 other Macs can run Nemotron Cascade 2 30B A3B, the smallest at IQ2_M. Apple Silicon uses unified memory, so the model shares RAM with the system. At Q4_K_M you need at least 19.3 GB of usable unified memory (RAM minus macOS overhead), from Mac Mini M5 Pro (24 GB).
Can I run Nemotron Cascade 2 30B A3B locally?
Yes — Nemotron Cascade 2 30B A3B can run locally on consumer hardware. Popular tools include Ollama, LM Studio, and llama.cpp.
How fast is Nemotron Cascade 2 30B A3B?
At Q4_K_M, Nemotron Cascade 2 30B A3B can reach ~92 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~161 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = 1000 ÷ (active GB ÷ (bandwidth GB/s × efficiency) × 1000 + layers × routing ms)
Mixture-of-Experts: just the active experts get read for each token, along with a fixed routing cost per layer.
Example: NVIDIA B200 → 2.2 GB active ÷ (8000 × 0.65) = 0.42 ms, plus 52 layers × 0.055 ms = 2.86 ms, so 1000 ÷ 3.28 ms = ~305 tok/s
Estimated speed at Q4_K_M (19.3 GB)
Real-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
What's the download size of Nemotron Cascade 2 30B A3B?
At Q4_K_M, the download is about 18.95 GB. The full-precision BF16 version is 63.16 GB. The smallest option (IQ2_XXS) is 8.68 GB.
Which GPUs can run Nemotron Cascade 2 30B A3B?
8 consumer GPUs can run Nemotron Cascade 2 30B A3B at Q4_K_M. Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU has plenty of headroom for comfortable inference.
Which devices can run Nemotron Cascade 2 30B A3B?
122 devices with unified memory can run Nemotron Cascade 2 30B A3B at Q4_K_M (19.3 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, 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.