Qwen3.5 9B Humanize DPO Round2 — Hardware Requirements & GPU Compatibility
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Qwen3.5 9B Humanize DPO Round2 is a 9B-parameter open language model from XiangJinYu in the Qwen family. At Q4_K_M it needs about 5.94 GB of VRAM — see which GPUs and Macs can run it below.
121 downloads
4 likesSpecifications
- Publisher
- XiangJinYu
- Family
- Qwen
- Parameters
- 9B
- Release Date
- 2026-05-14
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3.5 9B Humanize DPO Round2 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q3_K_L | 4.10 | 5.1 GB | — | 4.61 GB | 3-bit large quantization |
| Q4_K_M | 4.80 | 5.9 GB | — | 5.40 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 7.0 GB | — | 6.41 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 8.2 GB | — | 7.42 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 9.9 GB | — | 9.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3.5 9B Humanize DPO Round2?
Q4_K_M · 5.9 GBQwen3.5 9B Humanize DPO Round2 (Q4_K_M) requires 5.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Qwen3.5 9B Humanize DPO Round2?
Q4_K_M · 5.9 GB33 devices with unified memory can run Qwen3.5 9B Humanize DPO Round2, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3.5 9B Humanize DPO Round2 need?
Qwen3.5 9B Humanize DPO Round2 requires 5.9 GB of VRAM at Q4_K_M, or 9.9 GB at Q8_0.
VRAM = Weights + KV Cache + Overhead
Weights = 9B × 4.8 bits ÷ 8 = 5.4 GB
KV Cache + Overhead ≈ 0.5 GB (at 2K context + ~0.3 GB framework)
VRAM usage by quantization
Q4_K_M5.9 GB- What's the best quantization for Qwen3.5 9B Humanize DPO Round2?
For Qwen3.5 9B Humanize DPO Round2, Q4_K_M (5.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (7.0 GB) provides better quality if you have the VRAM. The smallest option is Q3_K_L at 5.1 GB.
VRAM requirement by quantization
Q3_K_L5.1 GBQ4_K_M ★5.9 GBQ5_K_M7.0 GBQ6_K8.2 GBQ8_09.9 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3.5 9B Humanize DPO Round2 on a Mac?
Qwen3.5 9B Humanize DPO Round2 requires at least 5.1 GB at Q3_K_L, 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 Qwen3.5 9B Humanize DPO Round2 locally?
Yes — Qwen3.5 9B Humanize DPO Round2 can run locally on consumer hardware. At Q4_K_M quantization it needs 5.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3.5 9B Humanize DPO Round2?
At Q4_K_M, Qwen3.5 9B Humanize DPO Round2 can reach ~491 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~110 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 ÷ 5.9 × 0.55 = ~491 tok/s
Estimated speed at Q4_K_M (5.9 GB)
~491 tok/s~110 tok/s~367 tok/s~303 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3.5 9B Humanize DPO Round2?
At Q4_K_M, the download is about 5.40 GB. The full-precision Q8_0 version is 9.00 GB. The smallest option (Q3_K_L) is 4.61 GB.
- Which GPUs can run Qwen3.5 9B Humanize DPO Round2?
35 consumer GPUs can run Qwen3.5 9B Humanize DPO Round2 at Q4_K_M (5.9 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 28 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen3.5 9B Humanize DPO Round2?
33 devices with unified memory can run Qwen3.5 9B Humanize DPO Round2 at Q4_K_M (5.9 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.