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Last updated: 2026-10-28

[Models](https://getrigbase.com/models/) / Nanbeige4.3 33B

# Best home rig for Nanbeige4.3 33B

Quick answer

**How much VRAM does Nanbeige4.3 33B need?**
The weights are about 20.2 GB at Q4_K_M (33B params × 4.89 bits ÷ 8, before context/KV cache). Add memory for context (KV cache) and the runtime. The smallest-memory setup with a measured result here, fully in memory, is 2x RTX 3090 (48 GB) at 66.2 tok/s.

**What is the fastest measured hardware for Nanbeige4.3 33B?**
2x RTX 3090: 66.2 tok/s generation (vLLM TP2, 1 stream, W4A16 AutoRound), from https://github.com/tonyd2wild/Nanbeige4.3-33B-W4A16-2x-RTX-3090.

Last updated: 28 Oct 2026

Current model 33B dense 2 measured results fastest plain decode: 66.2 tok/s  [model info ↗](https://huggingface.co/Nanbeige/Nanbeige4.3-33B)

[Will it fit my GPU?](https://getrigbase.com/can-it-run/) [Best value GPU per euro →](https://getrigbase.com/guides/best-value-gpu-local-llm-europe/)

Real, measured generation speeds for Nanbeige4.3 33B on home hardware, fastest first. Different quants/engines aren't directly comparable.

| Build | Quant | Engine | tok/s | Prompt processing (tok/s) | Cost | Source |
|---|---|---|---|---|---|---|
| [2x RTX 3090: Nanbeige4.3 33B W4A16 AutoRound on vLLM TP2, 62.2 tok/s prose (Tech2wild)](https://getrigbase.com/builds/tony-2x-rtx-3090-nanbeige4-3-33b-w4a16-2x-rtx-3090/) | W4A16 AutoRound | vLLM TP2, 1 stream | **66.2** | – |  | [github.com ↗](https://github.com/tonyd2wild/Nanbeige4.3-33B-W4A16-2x-RTX-3090) |
| [2x RTX 3090: Nanbeige4.3 33B W4A16 AutoRound on vLLM TP2, 62.2 tok/s prose (Tech2wild)](https://getrigbase.com/builds/tony-2x-rtx-3090-nanbeige4-3-33b-w4a16-2x-rtx-3090/) | W4A16 AutoRound | vLLM TP2, 1 stream | **62.2** | – |  | [github.com ↗](https://github.com/tonyd2wild/Nanbeige4.3-33B-W4A16-2x-RTX-3090) |

Prompt processing = prefill speed reported by the source, @ prompt length. * est. TTFT @8k is calculated from measured prefill, not measured. [Why it matters](https://getrigbase.com/can-it-run/#prefill).
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**Rent before you buy:** try this model on a cloud GPU for a few dollars first: [Runpod](https://runpod.io?ref=77qcpl2c) (referral link; sign up with Google to get the new-user bonus)

## Memory math: how much VRAM / RAM does Nanbeige4.3 33B need?

Nanbeige4.3 33B has **33B parameters** ([source](https://huggingface.co/Nanbeige/Nanbeige4.3-33B): Nanbeige model card: 33B dense Llama-architecture, Apache 2.0). Weight size = parameters × bits-per-weight ÷ 8, before context (KV cache) and runtime overhead.

| Format | Bits/weight | Weights | Smallest common memory size that holds the weights alone |
|---|---|---|---|
| Q8_0 | 8.50 | 35.1 GB | 48 GB |
| Q6_K | 6.56 | 27.1 GB | 32 GB |
| Q5_K_M | 5.70 | 23.5 GB | 24 GB |
| Q4_K_M | 4.89 | **20.2 GB** | 24 GB |
| Q3_K_M | 4.00 | 16.5 GB | 24 GB |
| Q2_K | 3.16 | 13.0 GB | 16 GB |

Bits-per-weight for GGUF quants are llama.cpp's own figures ([llama.cpp quantize README](https://github.com/ggml-org/llama.cpp/blob/master/tools/quantize/README.md)). "As measured" rows apply the source's stated bits-per-weight to the parameter count; real files differ slightly because some layers stay at higher precision. Try other combinations in the [VRAM calculator](https://getrigbase.com/can-it-run/).

## FAQ

### How much VRAM does Nanbeige4.3 33B need?

The weights are about 20.2 GB at Q4_K_M (33B params × 4.89 bits ÷ 8, before context/KV cache). Add memory for context (KV cache) and the runtime. The smallest-memory setup with a measured result here, fully in memory, is 2x RTX 3090 (48 GB) at 66.2 tok/s.

### What is the fastest measured hardware for Nanbeige4.3 33B?

2x RTX 3090: 66.2 tok/s generation (vLLM TP2, 1 stream, W4A16 AutoRound), from https://github.com/tonyd2wild/Nanbeige4.3-33B-W4A16-2x-RTX-3090.

### Which GPUs have measured results for Nanbeige4.3 33B?

2x RTX 3090.

**Can it run on…** [2x RTX 3090](https://getrigbase.com/can-i-run/nanbeige4-3-33b-on-2x-rtx-3090/)

Related: [what GPU do you need for 2026 models](https://getrigbase.com/guides/what-gpu-for-qwen3-6-gemma-4-gpt-oss/) · [all builds](https://getrigbase.com/builds/)