WhiteRabbitNeo v3 7B — Hardware Requirements & GPU Compatibility
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WhiteRabbitNeo v3 7B is a 7.6B-parameter open language model from WhiteRabbitNeo. It supports a context window of up to 32,768 tokens. At Q4_K_M it needs about 4.99 GB of VRAM — see which GPUs and Macs can run it below.
4.8K downloads
150 likesSpecifications
- Publisher
- WhiteRabbitNeo
- Parameters
- 7.6B
- Architecture
- Qwen2ForCausalLM
- Context Length
- 32,768 tokens
- Vocabulary Size
- 152,064
- Release Date
- 2025-04-25
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does WhiteRabbitNeo v3 7B Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 3.6 GB | 5.4 GB | 3.24 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 3.8 GB | 5.5 GB | 3.33 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 4.1 GB | 5.9 GB | 3.71 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 4.2 GB | 6.0 GB | 3.81 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 5.0 GB | 6.8 GB | 4.57 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 5.8 GB | 7.6 GB | 5.43 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 6.7 GB | 8.5 GB | 6.28 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 8.0 GB | 9.8 GB | 7.62 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run WhiteRabbitNeo v3 7B?
Q4_K_M · 5.0 GBWhiteRabbitNeo v3 7B (Q4_K_M) requires 5.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 7+ GB is recommended. Using the full 33K context window can add up to 1.8 GB, bringing total usage to 6.8 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run WhiteRabbitNeo v3 7B?
Q4_K_M · 5.0 GB59 devices with unified memory can run WhiteRabbitNeo v3 7B, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Apple iPhone 17 Pro.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightWhere to Download WhiteRabbitNeo v3 7B
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does WhiteRabbitNeo v3 7B need?
WhiteRabbitNeo v3 7B requires 5.0 GB of VRAM at Q4_K_M, or 15.7 GB at BF16. Full 33K context adds up to 1.8 GB (6.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 7.6B × 4.8 bits ÷ 8 = 4.6 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 2.2 GB (at full 33K context)
VRAM usage by quantization
Q4_K_M5.0 GBQ4_K_M + full context6.8 GB- What's the best quantization for WhiteRabbitNeo v3 7B?
For WhiteRabbitNeo v3 7B, Q4_K_M (5.0 GB) offers the best balance of quality and VRAM usage. Q4_K_L (5.1 GB) provides better quality if you have the VRAM. The smallest option is IQ2_M at 3.0 GB.
VRAM requirement by quantization
IQ2_M3.0 GBIQ3_M3.8 GBQ4_K_S4.7 GBQ4_K_M ★5.0 GBQ5_K_S5.7 GBBF1615.7 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run WhiteRabbitNeo v3 7B on a Mac?
WhiteRabbitNeo v3 7B requires at least 3.0 GB at IQ2_M, 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 WhiteRabbitNeo v3 7B locally?
Yes — WhiteRabbitNeo v3 7B can run locally on consumer hardware. At Q4_K_M quantization it needs 5.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is WhiteRabbitNeo v3 7B?
At Q4_K_M, WhiteRabbitNeo v3 7B can reach ~882 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~131 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 ÷ 5.0 × 0.65 = ~1042 tok/s
Estimated speed at Q4_K_M (5.0 GB)
~1042 tok/s~131 tok/s~1042 tok/s~882 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of WhiteRabbitNeo v3 7B?
At Q4_K_M, the download is about 4.57 GB. The full-precision BF16 version is 15.23 GB. The smallest option (IQ2_M) is 2.57 GB.
- Which GPUs can run WhiteRabbitNeo v3 7B?
50 consumer GPUs can run WhiteRabbitNeo v3 7B at Q4_K_M (5.0 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run WhiteRabbitNeo v3 7B?
59 devices with unified memory can run WhiteRabbitNeo v3 7B at Q4_K_M (5.0 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.