Qwen3.5 9B Gemini 3.1 Pro Reasoning Distill — Hardware Requirements & GPU Compatibility
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Qwen3.5 9B Gemini 3.1 Pro Reasoning Distill is a 9.7B-parameter open language model from Jackrong in the Qwen 3.5 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 6.36 GB of VRAM — see which GPUs and Macs can run it below.
499 downloads
3 likesSpecifications
- Publisher
- Jackrong
- Family
- Qwen 3.5
- Parameters
- 9.7B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-03-10
- License
- Apache 2.0
Get Started
How Much VRAM Does Qwen3.5 9B Gemini 3.1 Pro Reasoning Distill Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 4.7 GB | 38.8 GB | 4.10 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 5.3 GB | 39.4 GB | 4.71 GB | 3-bit medium quantization |
| Q4_K_Mest. | 4.80 | 6.4 GB | 40.5 GB | 5.79 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 7.5 GB | 41.5 GB | 6.88 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 8.5 GB | 42.6 GB | 7.96 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 10.2 GB | 44.3 GB | 9.65 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 19.9 GB | 54.0 GB | 19.31 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 Qwen3.5 9B Gemini 3.1 Pro Reasoning Distill?
Q4_K_M · 6.4 GBQwen3.5 9B Gemini 3.1 Pro Reasoning Distill (Q4_K_M) requires 6.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 9+ GB is recommended. Using the full 262K context window can add up to 34.1 GB, bringing total usage to 40.5 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 3070 Ti.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Qwen3.5 9B Gemini 3.1 Pro Reasoning Distill?
Q4_K_M · 6.4 GB58 devices with unified memory can run Qwen3.5 9B Gemini 3.1 Pro Reasoning Distill, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, MacBook Air 13" M3 (8 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3.5 9B Gemini 3.1 Pro Reasoning Distill need?
Qwen3.5 9B Gemini 3.1 Pro Reasoning Distill requires 6.4 GB of VRAM at Q4_K_M, or 19.9 GB at BF16. Full 262K context adds up to 34.1 GB (40.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 9.7B × 4.8 bits ÷ 8 = 5.8 GB
KV Cache + Overhead ≈ 0.6 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 34.7 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M6.4 GBQ4_K_M + full context40.5 GB- What's the best quantization for Qwen3.5 9B Gemini 3.1 Pro Reasoning Distill?
For Qwen3.5 9B Gemini 3.1 Pro Reasoning Distill, Q4_K_M (6.4 GB) offers the best balance of quality and VRAM usage. Q5_K_M (7.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.7 GB.
VRAM requirement by quantization
Q2_K4.7 GBQ4_K_M ★6.4 GBQ5_K_M7.5 GBQ6_K8.5 GBQ8_010.2 GBBF1619.9 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3.5 9B Gemini 3.1 Pro Reasoning Distill on a Mac?
Qwen3.5 9B Gemini 3.1 Pro Reasoning Distill requires at least 4.7 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 Qwen3.5 9B Gemini 3.1 Pro Reasoning Distill locally?
Yes — Qwen3.5 9B Gemini 3.1 Pro Reasoning Distill can run locally on consumer hardware. At Q4_K_M quantization it needs 6.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3.5 9B Gemini 3.1 Pro Reasoning Distill?
At Q4_K_M, Qwen3.5 9B Gemini 3.1 Pro Reasoning Distill can reach ~692 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~103 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 ÷ 6.4 × 0.65 = ~818 tok/s
Estimated speed at Q4_K_M (6.4 GB)
~818 tok/s~103 tok/s~818 tok/s~692 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 Gemini 3.1 Pro Reasoning Distill?
At Q4_K_M, the download is about 5.79 GB. The full-precision BF16 version is 19.31 GB. The smallest option (Q2_K) is 4.10 GB.
- Which GPUs can run Qwen3.5 9B Gemini 3.1 Pro Reasoning Distill?
50 consumer GPUs can run Qwen3.5 9B Gemini 3.1 Pro Reasoning Distill at Q4_K_M (6.4 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT, AMD Radeon RX 7600. 39 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen3.5 9B Gemini 3.1 Pro Reasoning Distill?
59 devices with unified memory can run Qwen3.5 9B Gemini 3.1 Pro Reasoning Distill at Q4_K_M (6.4 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.