Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled NVFP4 — Hardware Requirements & GPU Compatibility
ChatReasoning
2.1K downloads
5 likesSpecifications
- Publisher
- mconcat
- Family
- Qwen
- Parameters
- 22.1B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-03-11
- License
- Apache 2.0
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How Much VRAM Does Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled NVFP4 Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 10.2 GB | 67.0 GB | 9.41 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 10.4 GB | 67.3 GB | 9.69 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 11.5 GB | 68.4 GB | 10.80 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 11.8 GB | 68.6 GB | 11.07 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 14.0 GB | 70.8 GB | 13.29 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 16.5 GB | 73.3 GB | 15.78 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 19.0 GB | 75.8 GB | 18.27 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 22.9 GB | 79.7 GB | 22.15 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled NVFP4?
Q4_K_M · 14.0 GBQwen3.5 27B Claude 4.6 Opus Reasoning Distilled NVFP4 (Q4_K_M) requires 14.0 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 19+ GB is recommended. Using the full 262K context window can add up to 56.8 GB, bringing total usage to 70.8 GB. 17 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti, NVIDIA GeForce RTX 5080.
Runs great
— Plenty of headroom
Decent
— Enough VRAM, may be tightWhich Devices Can Run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled NVFP4?
Q4_K_M · 14.0 GB27 devices with unified memory can run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled NVFP4, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, Mac Mini M4 (16 GB).
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled NVFP4 need?
Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled NVFP4 requires 14.0 GB of VRAM at Q4_K_M, or 22.9 GB at Q8_0. Full 262K context adds up to 56.8 GB (70.8 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 22.1B × 4.8 bits ÷ 8 = 13.3 GB
KV Cache + Overhead ≈ 0.7 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 57.6 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M14.0 GBQ4_K_M + full context70.8 GB- What's the best quantization for Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled NVFP4?
For Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled NVFP4, Q4_K_M (14.0 GB) offers the best balance of quality and VRAM usage. Q5_K_S (16.0 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 6.8 GB.
VRAM requirement by quantization
IQ2_XXS6.8 GB~53%Q2_K_S9.6 GB~71%Q3_K_M11.5 GB~83%Q4_K_S13.2 GB~88%Q4_K_M ★14.0 GB~89%Q8_022.9 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled NVFP4 on a Mac?
Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled NVFP4 requires at least 6.8 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 NVFP4 locally?
Yes — Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled NVFP4 can run locally on consumer hardware. At Q4_K_M quantization it needs 14.0 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled NVFP4?
At Q4_K_M, Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled NVFP4 can reach ~208 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~47 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 ÷ 14.0 × 0.55 = ~208 tok/s
Estimated speed at Q4_K_M (14.0 GB)
AMD Instinct MI300X~208 tok/sNVIDIA GeForce RTX 4090~47 tok/sNVIDIA H100 SXM~155 tok/sAMD Instinct MI250X~128 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 NVFP4?
At Q4_K_M, the download is about 13.29 GB. The full-precision Q8_0 version is 22.15 GB. The smallest option (IQ2_XXS) is 6.09 GB.
- Which GPUs can run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled NVFP4?
17 consumer GPUs can run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled NVFP4 at Q4_K_M (14.0 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090, NVIDIA GeForce RTX 3090 Ti, AMD Radeon RX 6800. 5 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled NVFP4?
27 devices with unified memory can run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled NVFP4 at Q4_K_M (14.0 GB), including Mac Mini M4 (16 GB), Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.