Nanbeige4.1 3B — Hardware Requirements & GPU Compatibility
Chat
Nanbeige4.1 3B is a compact chat model from Nanbeige, a Chinese AI startup focused on building efficient small-scale language models. At just under 4 billion parameters, it is designed to run on virtually any modern GPU or even on CPU, making it one of the more accessible options for users with limited hardware. Despite its small size, it handles basic conversation, simple reasoning, and Chinese-English bilingual tasks, serving as a practical entry point for local LLM experimentation.
689.2K downloads
1.0K likesSpecifications
- Publisher
- Nanbeige
- Parameters
- 3.9B
- Architecture
- LlamaForCausalLM
- Context Length
- 262,144 tokens
- Vocabulary Size
- 166,144
- Release Date
- 2026-02-26
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Nanbeige4.1 3B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 2.1 GB | 19.1 GB | 1.67 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 2.2 GB | 19.2 GB | 1.72 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 2.4 GB | 19.4 GB | 1.92 GB | 3-bit medium quantization |
| Q3_K_L | 4.10 | 2.5 GB | 19.5 GB | 2.02 GB | 3-bit large quantization |
| IQ4_XS | 4.30 | 2.5 GB | 19.6 GB | 2.11 GB | Importance-weighted 4-bit, compact |
| Q4_K_S | 4.50 | 2.6 GB | 19.7 GB | 2.21 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 2.8 GB | 19.8 GB | 2.36 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 3.1 GB | 20.2 GB | 2.70 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 3.2 GB | 20.3 GB | 2.80 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 3.7 GB | 20.7 GB | 3.25 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 4.4 GB | 21.4 GB | 3.93 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Nanbeige4.1 3B?
Q4_K_M · 2.8 GBNanbeige4.1 3B (Q4_K_M) requires 2.8 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 4+ GB is recommended. Using the full 262K context window can add up to 17.1 GB, bringing total usage to 19.8 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Nanbeige4.1 3B?
Q4_K_M · 2.8 GB33 devices with unified memory can run Nanbeige4.1 3B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Derivatives (4)
Frequently Asked Questions
- How much VRAM does Nanbeige4.1 3B need?
Nanbeige4.1 3B requires 2.8 GB of VRAM at Q4_K_M, or 4.4 GB at Q8_0. Full 262K context adds up to 17.1 GB (19.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 3.9B × 4.8 bits ÷ 8 = 2.4 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 17.4 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M2.8 GBQ4_K_M + full context19.8 GB- What's the best quantization for Nanbeige4.1 3B?
For Nanbeige4.1 3B, Q4_K_M (2.8 GB) offers the best balance of quality and VRAM usage. Q5_K_S (3.1 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 2.1 GB.
VRAM requirement by quantization
Q2_K2.1 GB~75%Q3_K_L2.5 GB~86%Q4_K_S2.6 GB~88%Q4_K_M ★2.8 GB~89%Q5_K_M3.2 GB~92%Q8_04.4 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Nanbeige4.1 3B on a Mac?
Nanbeige4.1 3B requires at least 2.1 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 Nanbeige4.1 3B locally?
Yes — Nanbeige4.1 3B can run locally on consumer hardware. At Q4_K_M quantization it needs 2.8 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Nanbeige4.1 3B?
At Q4_K_M, Nanbeige4.1 3B can reach ~1045 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~235 tok/s. Speed depends mainly on GPU memory bandwidth. Real-world results typically within ±20%.
tok/s = (bandwidth GB/s ÷ model GB) × efficiency
Example: AMD Instinct MI300X → 5300 ÷ 2.8 × 0.55 = ~1045 tok/s
Estimated speed at Q4_K_M (2.8 GB)
AMD Instinct MI300X~1045 tok/sNVIDIA GeForce RTX 4090~235 tok/sNVIDIA H100 SXM~781 tok/sAMD Instinct MI250X~646 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Nanbeige4.1 3B?
At Q4_K_M, the download is about 2.36 GB. The full-precision Q8_0 version is 3.93 GB. The smallest option (Q2_K) is 1.67 GB.