Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled GGUF — Hardware Requirements & GPU Compatibility
ChatReasoning
A GGUF-quantized version of Jackrong's Qwen3.5 27B model, distilled from Claude 4.6 Opus with a focus on reasoning capabilities. This 27-billion-parameter model aims to capture the structured thinking and chain-of-thought abilities of a much larger frontier model in a size that can run on high-end consumer hardware. Available in multiple quantization levels, it offers a practical way to get strong reasoning performance locally without needing datacenter GPUs. As a distilled model, expect solid performance on logic puzzles, math, and multi-step problem solving, though it will not fully match its teacher model. The GGUF format makes it easy to run with llama.cpp, Ollama, or LM Studio. Best suited for users who prioritize analytical and reasoning tasks over raw creative generation.
144.6K downloads
209 likesSpecifications
- Publisher
- Jackrong
- Family
- Qwen
- Parameters
- 27B
- 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 27B 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 | 12.2 GB | 69.0 GB | 11.47 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 12.6 GB | 69.4 GB | 11.81 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 13.9 GB | 70.7 GB | 13.16 GB | 3-bit medium quantization |
| Q4_K_S | 4.50 | 15.9 GB | 72.8 GB | 15.19 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 16.9 GB | 73.8 GB | 16.20 GB | 4-bit medium quantization — most popular sweet spot |
| Q8_0 | 8.00 | 27.8 GB | 84.6 GB | 27.00 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled GGUF?
Q4_K_M · 16.9 GBQwen3.5 27B Claude 4.6 Opus Reasoning Distilled GGUF (Q4_K_M) requires 16.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 23+ GB is recommended. Using the full 262K context window can add up to 56.8 GB, bringing total usage to 73.8 GB. 6 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroom
Which Devices Can Run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled GGUF?
Q4_K_M · 16.9 GB21 devices with unified memory can run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled GGUF, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled GGUF need?
Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled GGUF requires 16.9 GB of VRAM at Q4_K_M, or 27.8 GB at Q8_0. Full 262K context adds up to 56.8 GB (73.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 27B × 4.8 bits ÷ 8 = 16.2 GB
KV Cache + Overhead ≈ 0.8 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 57.6 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M16.9 GBQ4_K_M + full context73.8 GB- Can NVIDIA GeForce RTX 4090 run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled GGUF?
Yes, at Q4_K_M (16.9 GB) or lower. Higher quantizations like Q8_0 (27.8 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled GGUF?
For Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled GGUF, Q4_K_M (16.9 GB) offers the best balance of quality and VRAM usage. Q8_0 (27.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 12.2 GB.
VRAM requirement by quantization
Q2_K12.2 GB~75%Q3_K_S12.6 GB~77%Q3_K_M13.9 GB~83%Q4_K_S15.9 GB~88%Q4_K_M ★16.9 GB~89%Q8_027.8 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled GGUF on a Mac?
Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled GGUF requires at least 12.2 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 27B Claude 4.6 Opus Reasoning Distilled GGUF locally?
Yes — Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled GGUF can run locally on consumer hardware. At Q4_K_M quantization it needs 16.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled GGUF?
At Q4_K_M, Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled GGUF can reach ~172 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~39 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 ÷ 16.9 × 0.55 = ~172 tok/s
Estimated speed at Q4_K_M (16.9 GB)
AMD Instinct MI300X~172 tok/sNVIDIA GeForce RTX 4090~39 tok/sNVIDIA H100 SXM~129 tok/sAMD Instinct MI250X~106 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 27B Claude 4.6 Opus Reasoning Distilled GGUF?
At Q4_K_M, the download is about 16.20 GB. The full-precision Q8_0 version is 27.00 GB. The smallest option (Q2_K) is 11.47 GB.