IOL-AI Qwen3.5 9B Reasoning v2

When to pick this model

• Step-by-step reasoning tasks on a single 8GB GPU
• General-purpose chat with an emphasis on chain-of-thought quality
• Long-context work up to 262K tokens without heavy hardware
• Experimenting with community reasoning fine-tunes of Qwen 3.5

VRAM requirements by quantization

VRAM figures include model weights plus a typical 8k KV cache and ~600 MB runtime overhead (Ollama / llama.cpp baseline). Add headroom for higher context lengths.

In practice, IOL-AI Qwen3.5 9B Reasoning v2 fits an 8 GB consumer card at Q4_K_M (6 GB). Stepping up to Q8_0 raises the footprint to 10 GB, and unquantized FP16 weights take 19 GB — plan your GPU around the Q4 or Q5 figure unless you specifically need the higher fidelity.

Without a GPU, IOL-AI Qwen3.5 9B Reasoning v2 needs roughly 13 GB of system RAM to run on CPU via llama.cpp or Ollama — workable for background jobs, but far slower than GPU inference. Throughput estimates from our compatibility engine: around 18 tokens/sec on entry-level GPUs, on the order of 28 tokens/sec on a mid-range card, and up to 45 tokens/sec on high-end hardware — assuming the chosen quantization fully fits in VRAM.

What hardware do you need

The table below matches IOL-AI Qwen3.5 9B Reasoning v2 to common GPU memory tiers, using the highest-fidelity quantization that fully fits each card class. Spilling layers to system RAM works but costs most of the speed, so size your card to the quantization you actually want to run.

Which hardware should you buy to run IOL-AI Qwen3.5 9B Reasoning v2?

To run IOL-AI Qwen3.5 9B Reasoning v2 locally at Q4, you need ~6 GB for Q4 weights alone. Hardware option to compare: RTX 5060 Ti 16GB (ASUS Prime). Leave memory for the system and context; verify inference-engine support. A mini PC does not provide CUDA or macOS/MLX.

Strengths

• Tuned specifically for step-by-step reasoning
• Massive context window up to 262K tokens
• Lightweight: ~6GB VRAM at Q4, fits an 8GB GPU
• Apache 2.0 license with no usage restrictions

Limitations

• Community fine-tune with limited track record and few public benchmarks
• No Ollama tag — install via Hugging Face

Typical workloads

In our catalog grid, IOL-AI Qwen3.5 9B Reasoning v2 is filed under Reasoning, General Chat, Long Context — the use cases where its size/quality trade-off makes the most sense. Its tags translate to concrete workloads: multi-step reasoning and math-flavoured tasks; multilingual workloads.

The 256k-token context window is large enough to hold entire codebases' worth of files or long reports in a single prompt, which is what makes local RAG and document analysis practical. The Apache 2.0 license is permissive, so shipping it inside a commercial product raises no special legal questions.

Architecture & training

Architecture: Dense transformer · LoRA fine-tune of Qwen 3.5 9B (~9.7B) focused on reasoning · 262K-token context window

Training: An instruct LoRA fine-tune of Qwen 3.5 9B centered on reasoning, a public IOL-AI 2026 submission. Training details not published.

Quick start

Install the runtime for your system: Windows, macOS or Linux. Check the exact model tag or GGUF quantization below; catalog IDs are not necessarily Ollama tags.

Start at 4096 tokens of context, then use ollama ps to check GPU/CPU placement. A default download may use a different quantization from the configurator’s memory estimate. Keep the free setup working before considering a kit.

# HuggingFace : MikCil/IOL-AI-Qwen35-9B-IT-LoRA-Reasoning-v2

Or use the open-source MCP server to query this model from Claude Desktop, Cursor, or any MCP-compatible client.

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Frequently asked questions

How much VRAM does IOL-AI Qwen3.5 9B Reasoning v2 need?

At the recommended Q4_K_M quantization, IOL-AI Qwen3.5 9B Reasoning v2 needs about 6 GB of VRAM. Q8_0 takes 10 GB, and unquantized FP16 weights take 19 GB.

Can IOL-AI Qwen3.5 9B Reasoning v2 run without a GPU?

Yes — with roughly 13 GB of system RAM it runs CPU-only through llama.cpp or Ollama. Expect a fraction of GPU speed, which is fine for background or batch jobs but slow for interactive chat.

What context window does IOL-AI Qwen3.5 9B Reasoning v2 support?

IOL-AI Qwen3.5 9B Reasoning v2 supports a 256k-token context window (262,144 tokens).

Can I use IOL-AI Qwen3.5 9B Reasoning v2 commercially?

Yes. IOL-AI Qwen3.5 9B Reasoning v2 is released under Apache 2.0, a permissive open-source license that allows commercial use, modification and redistribution.

How fast is IOL-AI Qwen3.5 9B Reasoning v2 on consumer hardware?

Our compatibility engine estimates on the order of 28 tokens/sec on a mid-range GPU and up to 45 tokens/sec on high-end cards, assuming the quantization fully fits in VRAM.

Which quantization of IOL-AI Qwen3.5 9B Reasoning v2 should I download first?

Start with Q4_K_M (6 GB) — the standard size/quality sweet spot. Step up to Q5_K_M or Q8_0 only if you have VRAM headroom. It already fits an 8 GB card at Q5_K_M.