Family Qwen · 9.7B parameters

IOL-AI Qwen3.5 9B Reasoning v2

LoRA fine-tune of Qwen 3.5 9B (Apache 2.0) focused on reasoning, with 262K context. ~6 GB VRAM in Q4, fits on an 8 GB GPU.

🇨🇳 MikCil·License Apache 2.0·Context 256k tokens·Output 2026-07-24·Fits within the 128 GB of the GIGABYTE AI TOP ATOM← Catalog

01What it can do

Strengths
  • Designed for step-by-step reasoning
  • Massive context up to 262K tokens
  • Lightweight: ~6 GB VRAM in Q4, fits on an 8 GB GPU
  • Permissive Apache 2.0 license
Limitations to know
  • —Community fine-tune: limited track record and few public benchmarks
  • —No Ollama tag — install via HuggingFace
Architecture
Dense Transformer · reasoning-oriented LoRA fine-tune of Qwen 3.5 9B (~9.7B) · 262K-token context window
Training
Instruct LoRA fine-tune of Qwen 3.5 9B focused on reasoning, publicly submitted to IOL-AI 2026. Training details not published.
Ideal for
ReasoningVersatile chatLong context

04Install

Install Ollama for your OS. Check the model and its quantization before downloading. Start with 4096 tokens of context, then check placement with ollama ps. A command below is not proof that a test was run on your machine.

$# HuggingFace : MikCil/IOL-AI-Qwen35-9B-IT-LoRA-Reasoning-v2
⚠
First download: between 2 and 40 GB depending on the selected quantization. Plan for sufficient disk space; a stable connection is recommended. Subsequent launches are instant.

02Required memory

Approximate GPU VRAM required to run this model, including 4k tokens of context overhead. For a longer context, add ~1 GB per 8k-token increment.

Q4_K_M
The lightest, ~5% loss
6 GB
Q5_K_M
Good quality/size compromise
7 GB
Q8_0
Nearly indistinguishable from FP16
10 GB
FP16
Full precision — server use
19 GB
Fallback CPU · If you don't have a GPU, allow 13 GB of RAM minimum to run this model at reduced speed.

What hardware do you need for IOL-AI Qwen3.5 9B Reasoning v2?

To run IOL-AI Qwen3.5 9B Reasoning v2 locally with Q4 quantization, you need about 6 GB of VRAM. An option to compare: RTX 5060 Ti 16GB (ASUS Prime) — leave some headroom for the system and context; check engine compatibility with the GPU.

Current offer: RTX 5060 Ti 16GB (ASUS Prime)
AmazonSee price →

Affiliate links — commission possible at no extra cost to you; independent recommendation. As an Amazon Associate, BestLLMfor earns from qualifying purchases.

On the go: IOL-AI Qwen3.5 9B Reasoning v2 also runs on a RTX laptop PC (16 GB of VRAM) →

This model in your private ChatGPT, without the cloud

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03Expected speed

Tokens generated per second in Q4_K_M, 4k context. Beyond 20 t/s, reading is comfortable. Below 10 t/s, that's just for testing.

Entry-level
~18t/s
GTX 1650, RX 6600, MBA M2 8GB
Mid-range
~28t/s
RTX 4060, 4070, MBP M3 Pro
High-end
~45t/s
RTX 4090, M4 Max, Radeon 7900