Elbaz NVIDIA Nemotron 3 Nano 30B A3B PRISM — Hardware Requirements & GPU Compatibility
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Elbaz NVIDIA Nemotron 3 Nano 30B A3B PRISM is a 30B-parameter open language model from Ex0bit in the Nemotron family. At Q4_K_M it needs about 19.80 GB of VRAM — see which GPUs and Macs can run it below.
2.5K downloads
26 likesSpecifications
- Publisher
- Ex0bit
- Family
- Nemotron
- Parameters
- 30B
- Release Date
- 2025-12-18
- License
- Other
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How Much VRAM Does Elbaz NVIDIA Nemotron 3 Nano 30B A3B PRISM Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 14.0 GB | — | 12.75 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 16.1 GB | — | 14.63 GB | 3-bit medium quantization |
| IQ4_XS | 4.30 | 17.7 GB | — | 16.13 GB | Importance-weighted 4-bit, compact |
| Q4_K_Mest. | 4.80 | 19.8 GB | — | 18.00 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 23.5 GB | — | 21.38 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 27.2 GB | — | 24.75 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 33 GB | — | 30.00 GB | 8-bit quantization, near-lossless |
| BF16 | 16.00 | 66 GB | — | 60.00 GB | Brain floating point 16 — preferred for training |
est.= calculated VRAM estimate; no published GGUF file found for that quantization yet. Other rows are verified against real community uploads.
Which GPUs Can Run Elbaz NVIDIA Nemotron 3 Nano 30B A3B PRISM?
Q4_K_M · 19.8 GBElbaz NVIDIA Nemotron 3 Nano 30B A3B PRISM (Q4_K_M) requires 19.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 26+ GB is recommended. 8 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Elbaz NVIDIA Nemotron 3 Nano 30B A3B PRISM?
Q4_K_M · 19.8 GB41 devices with unified memory can run Elbaz NVIDIA Nemotron 3 Nano 30B A3B PRISM, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Elbaz NVIDIA Nemotron 3 Nano 30B A3B PRISM need?
Elbaz NVIDIA Nemotron 3 Nano 30B A3B PRISM requires 19.8 GB of VRAM at Q4_K_M, or 66 GB at BF16.
VRAM = Weights + KV Cache + Overhead
Weights = 30B × 4.8 bits ÷ 8 = 18 GB
KV Cache + Overhead ≈ 1.8 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M19.8 GB- Can NVIDIA GeForce RTX 4090 run Elbaz NVIDIA Nemotron 3 Nano 30B A3B PRISM?
Yes, at Q5_K_M (23.5 GB) or lower. Higher quantizations like Q6_K (27.2 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Elbaz NVIDIA Nemotron 3 Nano 30B A3B PRISM?
For Elbaz NVIDIA Nemotron 3 Nano 30B A3B PRISM, Q4_K_M (19.8 GB) offers the best balance of quality and VRAM usage. Q5_K_M (23.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 14.0 GB.
VRAM requirement by quantization
Q2_K14.0 GBIQ4_XS17.7 GBQ4_K_M ★19.8 GBQ5_K_M23.5 GBQ6_K27.2 GBBF1666.0 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Elbaz NVIDIA Nemotron 3 Nano 30B A3B PRISM on a Mac?
Elbaz NVIDIA Nemotron 3 Nano 30B A3B PRISM requires at least 14.0 GB at Q2_K, 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 Elbaz NVIDIA Nemotron 3 Nano 30B A3B PRISM locally?
Yes — Elbaz NVIDIA Nemotron 3 Nano 30B A3B PRISM can run locally on consumer hardware. At Q4_K_M quantization it needs 19.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Elbaz NVIDIA Nemotron 3 Nano 30B A3B PRISM?
At Q4_K_M, Elbaz NVIDIA Nemotron 3 Nano 30B A3B PRISM can reach ~222 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~33 tok/s. 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 ÷ 19.8 × 0.65 = ~263 tok/s
Estimated speed at Q4_K_M (19.8 GB)
~263 tok/s~33 tok/s~263 tok/s~222 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Elbaz NVIDIA Nemotron 3 Nano 30B A3B PRISM?
At Q4_K_M, the download is about 18.00 GB. The full-precision BF16 version is 60.00 GB. The smallest option (Q2_K) is 12.75 GB.
- Which GPUs can run Elbaz NVIDIA Nemotron 3 Nano 30B A3B PRISM?
8 consumer GPUs can run Elbaz NVIDIA Nemotron 3 Nano 30B A3B PRISM at Q4_K_M (19.8 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Elbaz NVIDIA Nemotron 3 Nano 30B A3B PRISM?
41 devices with unified memory can run Elbaz NVIDIA Nemotron 3 Nano 30B A3B PRISM at Q4_K_M (19.8 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.