MLX Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled 4bit — Hardware Requirements & GPU Compatibility
ChatReasoning
1.3K downloads
2 likesSpecifications
- Publisher
- Jackrong
- Family
- Qwen
- Parameters
- 1.4B
- Architecture
- Qwen3_5ForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-03-06
- License
- Apache 2.0
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How Much VRAM Does MLX Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled 4bit Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_K | 3.40 | 1.2 GB | 35.3 GB | 0.59 GB | 2-bit quantization with K-quant improvements |
| Q3_K_S | 3.50 | 1.2 GB | 35.3 GB | 0.61 GB | 3-bit small quantization |
| Q3_K_M | 3.90 | 1.3 GB | 35.3 GB | 0.68 GB | 3-bit medium quantization |
| Q3_K_L | 4.10 | 1.3 GB | 35.4 GB | 0.72 GB | 3-bit large quantization |
| Q4_K_S | 4.50 | 1.4 GB | 35.5 GB | 0.79 GB | 4-bit small quantization |
| Q4_K_M | 4.80 | 1.4 GB | 35.5 GB | 0.84 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_S | 5.50 | 1.5 GB | 35.6 GB | 0.96 GB | 5-bit small quantization |
| Q5_K_M | 5.70 | 1.6 GB | 35.7 GB | 1.00 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 1.7 GB | 35.8 GB | 1.15 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 2.0 GB | 36.1 GB | 1.40 GB | 8-bit quantization, near-lossless |
Which GPUs Can Run MLX Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled 4bit?
Q4_K_M · 1.4 GBMLX Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled 4bit (Q4_K_M) requires 1.4 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 2+ GB is recommended. Using the full 262K context window can add up to 34.1 GB, bringing total usage to 35.5 GB. 35 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run MLX Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled 4bit?
Q4_K_M · 1.4 GB33 devices with unified memory can run MLX Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled 4bit, including NVIDIA DGX H100, NVIDIA DGX A100 640GB.
Runs great
— Plenty of headroomRelated Models
Frequently Asked Questions
- How much VRAM does MLX Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled 4bit need?
MLX Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled 4bit requires 1.4 GB of VRAM at Q4_K_M, or 2.0 GB at Q8_0. Full 262K context adds up to 34.1 GB (35.5 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 1.4B × 4.8 bits ÷ 8 = 0.8 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_M1.4 GBQ4_K_M + full context35.5 GB- What's the best quantization for MLX Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled 4bit?
For MLX Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled 4bit, Q4_K_M (1.4 GB) offers the best balance of quality and VRAM usage. Q5_K_S (1.5 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 1.2 GB.
VRAM requirement by quantization
Q2_K1.2 GB~75%Q3_K_M1.3 GB~83%Q4_K_M ★1.4 GB~89%Q5_K_S1.5 GB~92%Q5_K_M1.6 GB~92%Q8_02.0 GB~99%★ Recommended — best balance of quality and VRAM usage.
- Can I run MLX Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled 4bit on a Mac?
MLX Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled 4bit requires at least 1.2 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 MLX Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled 4bit locally?
Yes — MLX Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled 4bit can run locally on consumer hardware. At Q4_K_M quantization it needs 1.4 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is MLX Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled 4bit?
At Q4_K_M, MLX Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled 4bit can reach ~2067 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~465 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 ÷ 1.4 × 0.55 = ~2067 tok/s
Estimated speed at Q4_K_M (1.4 GB)
AMD Instinct MI300X~2067 tok/sNVIDIA GeForce RTX 4090~465 tok/sNVIDIA H100 SXM~1545 tok/sAMD Instinct MI250X~1278 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of MLX Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled 4bit?
At Q4_K_M, the download is about 0.84 GB. The full-precision Q8_0 version is 1.40 GB. The smallest option (Q2_K) is 0.59 GB.
- Which GPUs can run MLX Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled 4bit?
35 consumer GPUs can run MLX Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled 4bit at Q4_K_M (1.4 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 35 GPUs have plenty of headroom for comfortable inference.
- Which devices can run MLX Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled 4bit?
33 devices with unified memory can run MLX Qwen3.5 9B Claude 4.6 Opus Reasoning Distilled 4bit at Q4_K_M (1.4 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.