Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled GGUF — Hardware Requirements & GPU Compatibility
ChatReasoning
A compact 9-billion-parameter GGUF model distilled from Claude 4.6 Opus reasoning using the Qwen3.5 9B base. This is the smallest model in Jackrong's reasoning distillation series and is designed to run comfortably on consumer GPUs with as little as 6 to 8 GB of VRAM at aggressive quantization levels. While the smaller size necessarily limits the depth of reasoning compared to its 27B sibling, this model punches above its weight class on structured thinking tasks thanks to the distillation approach. A practical choice for users with modest hardware who want improved reasoning over a standard 9B model without investing in a bigger GPU.
57.3K downloads
88 likesSpecifications
- Publisher
- Jackrong
- Family
- Qwen
- Parameters
- 9B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-03-15
- License
- Apache 2.0
Get Started
How Much VRAM Does Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled GGUF Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 4.4 GB | 38.5 GB | 3.83 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 4.5 GB | 38.6 GB | 3.94 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 5.0 GB | 39.0 GB | 4.39 GB | 3-bit medium quantization |
| Q3_K_L | 4.10 | 5.2 GB | 39.3 GB | 4.61 GB | 3-bit large quantization |
| Q4_K_S | 4.50 | 5.6 GB | 39.7 GB | 5.06 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 6.0 GB | 40.1 GB | 5.40 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 6.8 GB | 40.9 GB | 6.19 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 7.0 GB | 41.1 GB | 6.41 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 8.0 GB | 42.1 GB | 7.42 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 9.6 GB | 43.7 GB | 9.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled GGUF?
Q4_K_M · 6.0 GBQwen3.5 9B Claude 4.6 Opus Reasoning Distilled GGUF (Q4_K_M) requires 6.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 8+ GB is recommended. Using the full 262K context window can add up to 34.1 GB, bringing total usage to 40.1 GB. 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 Claude 4.6 Opus Reasoning Distilled GGUF?
Q4_K_M · 6.0 GB33 devices with unified memory can run Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled GGUF, 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 Claude 4.6 Opus Reasoning Distilled GGUF need?
Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled GGUF requires 6.0 GB of VRAM at Q4_K_M, or 9.6 GB at Q8_0. Full 262K context adds up to 34.1 GB (40.1 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 9B × 4.8 bits ÷ 8 = 5.4 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_M6.0 GBQ4_K_M + full context40.1 GB- What's the best quantization for Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled GGUF?
For Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled GGUF, Q4_K_M (6.0 GB) offers the best balance of quality and VRAM usage. Q5_K_S (6.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 4.4 GB.
VRAM requirement by quantization
Q2_K4.4 GB~75%Q3_K_M5.0 GB~83%Q4_K_M ★6.0 GB~89%Q5_K_S6.8 GB~92%Q5_K_M7.0 GB~92%Q8_09.6 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled GGUF on a Mac?
Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled GGUF requires at least 4.4 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 Claude 4.6 Opus Reasoning Distilled GGUF locally?
Yes — Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 6.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled GGUF?
At Q4_K_M, Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled GGUF can reach ~488 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 ÷ 6.0 × 0.55 = ~488 tok/s
Estimated speed at Q4_K_M (6.0 GB)
AMD Instinct MI300X~488 tok/sNVIDIA GeForce RTX 4090~110 tok/sNVIDIA H100 SXM~365 tok/sAMD Instinct MI250X~302 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 Claude 4.6 Opus Reasoning Distilled GGUF?
At Q4_K_M, the download is about 5.40 GB. The full-precision Q8_0 version is 9.00 GB. The smallest option (Q2_K) is 3.83 GB.