Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled Qx64 Hi MLX — Hardware Requirements & GPU Compatibility
ChatReasoning
8.4K downloads
9 likesSpecifications
- Publisher
- nightmedia
- Family
- Qwen
- Parameters
- 26.9B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-03-08
- License
- lgpl-3.0
Get Started
How Much VRAM Does Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled Qx64 Hi MLX Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 12.2 GB | 69 GB | 11.43 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 12.5 GB | 69.3 GB | 11.77 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 13.9 GB | 70.7 GB | 13.11 GB | 3-bit medium quantization |
| Q4_0 | 4.00 | 14.2 GB | 71.0 GB | 13.45 GB | 4-bit legacy quantization |
| Q4_K_M | 4.80 | 16.9 GB | 73.7 GB | 16.14 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 19.9 GB | 76.7 GB | 19.16 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 22.9 GB | 79.8 GB | 22.19 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 27.6 GB | 84.5 GB | 26.90 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled Qx64 Hi MLX?
Q4_K_M · 16.9 GBQwen3.5 27B Claude 4.6 Opus Reasoning Distilled Qx64 Hi MLX (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, 22+ GB is recommended. Using the full 262K context window can add up to 56.8 GB, bringing total usage to 73.7 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 Qx64 Hi MLX?
Q4_K_M · 16.9 GB21 devices with unified memory can run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled Qx64 Hi MLX, 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 Qx64 Hi MLX need?
Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled Qx64 Hi MLX requires 16.9 GB of VRAM at Q4_K_M, or 27.6 GB at Q8_0. Full 262K context adds up to 56.8 GB (73.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 26.9B × 4.8 bits ÷ 8 = 16.1 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 context73.7 GB- Can NVIDIA GeForce RTX 4090 run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled Qx64 Hi MLX?
Yes, at Q6_K (22.9 GB) or lower. Higher quantizations like Q8_0 (27.6 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 Qx64 Hi MLX?
For Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled Qx64 Hi MLX, Q4_K_M (16.9 GB) offers the best balance of quality and VRAM usage. Q5_K_S (19.2 GB) provides better quality if you have the VRAM. The smallest option is IQ2_XXS at 8.1 GB.
VRAM requirement by quantization
IQ2_XXS8.1 GB~53%Q2_K_S11.5 GB~71%Q3_K_M13.9 GB~83%Q4_K_S15.9 GB~88%Q4_K_M ★16.9 GB~89%Q8_027.6 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled Qx64 Hi MLX on a Mac?
Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled Qx64 Hi MLX requires at least 8.1 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 Qx64 Hi MLX locally?
Yes — Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled Qx64 Hi MLX 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 Qx64 Hi MLX?
At Q4_K_M, Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled Qx64 Hi MLX can reach ~173 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 = ~173 tok/s
Estimated speed at Q4_K_M (16.9 GB)
AMD Instinct MI300X~173 tok/sNVIDIA GeForce RTX 4090~39 tok/sNVIDIA H100 SXM~129 tok/sAMD Instinct MI250X~107 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 Qx64 Hi MLX?
At Q4_K_M, the download is about 16.14 GB. The full-precision Q8_0 version is 26.90 GB. The smallest option (IQ2_XXS) is 7.40 GB.
- Which GPUs can run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled Qx64 Hi MLX?
6 consumer GPUs can run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled Qx64 Hi MLX at Q4_K_M (16.9 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 Qx64 Hi MLX?
21 devices with unified memory can run Qwen3.5 27B Claude 4.6 Opus Reasoning Distilled Qx64 Hi MLX at Q4_K_M (16.9 GB), including Mac Mini M4 (32 GB), Mac Mini M4 Pro (24 GB), Mac Mini M4 Pro (48 GB), Mac Pro M2 Ultra (192 GB). Apple Silicon Macs use unified memory shared between CPU and GPU, making them well-suited for local LLM inference.