Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled — Hardware Requirements & GPU Compatibility
ChatReasoning
The full-precision version of Jackrong's Qwen3.5 27B reasoning distillation from Claude 4.6 Opus. With 27.8 billion parameters in unquantized form, this model preserves the maximum quality from the distillation process but requires significantly more VRAM, typically 56 GB or more in BF16. It is primarily intended for users with professional-grade GPUs or multi-GPU setups. This variant is ideal for further fine-tuning, experimentation, or running at full fidelity when hardware allows. Most users looking to run the model locally for inference should consider the GGUF-quantized version instead, which offers a much better tradeoff between quality and resource usage.
61.6K downloads
695 likesSpecifications
- Publisher
- Jackrong
- Family
- Qwen 3.5
- Parameters
- 27.8B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-02-27
- License
- Apache 2.0
Get Started
How Much VRAM Does Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 12.6 GB | 69.4 GB | 11.81 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 12.9 GB | 69.7 GB | 12.15 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 14.3 GB | 71.1 GB | 13.54 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 14.6 GB | 71.5 GB | 13.89 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 17.4 GB | 74.2 GB | 16.67 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 20.5 GB | 77.4 GB | 19.79 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 23.7 GB | 80.5 GB | 22.92 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 28.5 GB | 85.3 GB | 27.78 GB | 8-bit quantization, near-lossless |
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 27B Claude 4.6 Opus Reasoning Distilled?
Q4_K_M · 17.4 GBQwen3.5 27B Claude 4.6 Opus Reasoning Distilled (Q4_K_M) requires 17.4 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 74.2 GB. 8 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?
Q4_K_M · 17.4 GB41 devices with unified memory can run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 Pro (24 GB).
Runs great
— Plenty of headroomDecent
— Enough memory, may be tightWhere to Download Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled
Community quantizations of this model — GGUF for llama.cpp, Ollama, and LM Studio, plus AWQ/MLX variants where available.
Related Models
Frequently Asked Questions
- How much VRAM does Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled need?
Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled requires 17.4 GB of VRAM at Q4_K_M, or 56.3 GB at BF16. Full 262K context adds up to 56.8 GB (74.2 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 27.8B × 4.8 bits ÷ 8 = 16.7 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 57.5 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M17.4 GBQ4_K_M + full context74.2 GB- Can NVIDIA GeForce RTX 4090 run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled?
Yes, at Q6_K (23.7 GB) or lower. Higher quantizations like Q8_0 (28.5 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?
For Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled, Q4_K_M (17.4 GB) offers the best balance of quality and VRAM usage. Q5_K_S (19.9 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 8.4 GB.
VRAM requirement by quantization
IQ2_XXS8.4 GBIQ3_XS12.2 GBQ3_K_M14.3 GBQ4_K_M ★17.4 GBQ5_K_S19.9 GBBF1656.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled on a Mac?
Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled requires at least 8.4 GB at IQ2_XXS, 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 locally?
Yes — Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled can run locally on consumer hardware. At Q4_K_M quantization it needs 17.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled?
At Q4_K_M, Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled can reach ~253 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~38 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 ÷ 17.4 × 0.65 = ~299 tok/s
Estimated speed at Q4_K_M (17.4 GB)
~299 tok/s~38 tok/s~299 tok/s~253 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?
At Q4_K_M, the download is about 16.67 GB. The full-precision BF16 version is 55.56 GB. The smallest option (IQ2_XXS) is 7.64 GB.
- Which GPUs can run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled?
8 consumer GPUs can run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled at Q4_K_M (17.4 GB). Top options include NVIDIA GeForce RTX 5090, AMD Radeon RX 7900 XT, AMD Radeon RX 7900 XTX. 1 GPU have plenty of headroom for comfortable inference.
- Which devices can run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled?
41 devices with unified memory can run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled at Q4_K_M (17.4 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Asus ROG Flow Z13 (2025, Ryzen AI Max+ 395, 128 GB), Beelink GTR9 Pro (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.