Qwen3.6 35B A3B Claude 4.6 Opus Reasoning Distilled — Hardware Requirements & GPU Compatibility
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Qwen3.6 35B A3B Claude 4.6 Opus Reasoning Distilled is a 36.0B-parameter open language model from hesamation in the Qwen 3.6 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 21.95 GB of VRAM — see which GPUs and Macs can run it below.
4.2K downloads
86 likesSpecifications
- Publisher
- hesamation
- Family
- Qwen 3.6
- Parameters
- 36.0B
- Architecture
- Qwen3_5MoeForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-04-17
- License
- Apache 2.0
Get Started
How Much VRAM Does Qwen3.6 35B A3B Claude 4.6 Opus Reasoning Distilled Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 15.7 GB | 26.3 GB | 15.28 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 17.9 GB | 28.6 GB | 17.53 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 21.9 GB | 32.6 GB | 21.57 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_M | 5.70 | 26 GB | 36.6 GB | 25.62 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_K | 6.60 | 30.0 GB | 40.7 GB | 29.66 GB | 6-bit quantization, very good quality |
| Q8_0 | 8.00 | 36.3 GB | 47.0 GB | 35.95 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 72.3 GB | 82.9 GB | 71.90 GB | Brain floating point 16 — preferred for training |
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 Qwen3.6 35B A3B Claude 4.6 Opus Reasoning Distilled?
Q4_K_M · 21.9 GBQwen3.6 35B A3B Claude 4.6 Opus Reasoning Distilled (Q4_K_M) requires 21.9 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 29+ GB is recommended. Using the full 262K context window can add up to 10.7 GB, bringing total usage to 32.6 GB. 5 GPUs can run it, including NVIDIA GeForce RTX 5090.
All compatible consumer-level GPUs are running near their VRAM limit. You may also want to consider professional GPUs (e.g., NVIDIA A100, H100) which offer significantly more VRAM. For more headroom and better throughput, consider a multi-GPU configuration with tensor parallelism (supported by tools like vLLM, llama.cpp, or text-generation-inference).
Which Devices Can Run Qwen3.6 35B A3B Claude 4.6 Opus Reasoning Distilled?
Q4_K_M · 21.9 GB21 devices with unified memory can run Qwen3.6 35B A3B 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 Qwen3.6 35B A3B 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 Qwen3.6 35B A3B Claude 4.6 Opus Reasoning Distilled need?
Qwen3.6 35B A3B Claude 4.6 Opus Reasoning Distilled requires 21.9 GB of VRAM at Q4_K_M, or 72.3 GB at BF16. Full 262K context adds up to 10.7 GB (32.6 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 36.0B × 4.8 bits ÷ 8 = 21.6 GB
KV Cache + Overhead ≈ 0.3 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 11 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M21.9 GBQ4_K_M + full context32.6 GB- Can NVIDIA GeForce RTX 4090 run Qwen3.6 35B A3B Claude 4.6 Opus Reasoning Distilled?
Yes, at Q4_K_M (21.9 GB) or lower. Higher quantizations like Q5_K_M (26 GB) exceed the NVIDIA GeForce RTX 4090's 24 GB.
- What's the best quantization for Qwen3.6 35B A3B Claude 4.6 Opus Reasoning Distilled?
For Qwen3.6 35B A3B Claude 4.6 Opus Reasoning Distilled, Q4_K_M (21.9 GB) offers the best balance of quality and VRAM usage. Q5_K_M (26 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 15.7 GB.
VRAM requirement by quantization
Q2_K15.7 GBQ4_K_M ★21.9 GBQ5_K_M26.0 GBQ6_K30.0 GBQ8_036.3 GBBF1672.3 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3.6 35B A3B Claude 4.6 Opus Reasoning Distilled on a Mac?
Qwen3.6 35B A3B Claude 4.6 Opus Reasoning Distilled requires at least 15.7 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 Qwen3.6 35B A3B Claude 4.6 Opus Reasoning Distilled locally?
Yes — Qwen3.6 35B A3B Claude 4.6 Opus Reasoning Distilled can run locally on consumer hardware. At Q4_K_M quantization it needs 21.9 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3.6 35B A3B Claude 4.6 Opus Reasoning Distilled?
At Q4_K_M, Qwen3.6 35B A3B Claude 4.6 Opus Reasoning Distilled can reach ~133 tok/s on AMD Instinct MI300X. On NVIDIA GeForce RTX 4090: ~30 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 ÷ 21.9 × 0.55 = ~133 tok/s
Estimated speed at Q4_K_M (21.9 GB)
~133 tok/s~30 tok/s~99 tok/s~82 tok/sReal-world results typically within ±20%. Speed depends on batch size, quantization kernel, and software stack.
- What's the download size of Qwen3.6 35B A3B Claude 4.6 Opus Reasoning Distilled?
At Q4_K_M, the download is about 21.57 GB. The full-precision BF16 version is 71.90 GB. The smallest option (Q2_K) is 15.28 GB.
- Which GPUs can run Qwen3.6 35B A3B Claude 4.6 Opus Reasoning Distilled?
5 consumer GPUs can run Qwen3.6 35B A3B Claude 4.6 Opus Reasoning Distilled at Q4_K_M (21.9 GB). Top options include AMD Radeon RX 7900 XTX, NVIDIA GeForce RTX 3090.
- Which devices can run Qwen3.6 35B A3B Claude 4.6 Opus Reasoning Distilled?
21 devices with unified memory can run Qwen3.6 35B A3B Claude 4.6 Opus Reasoning Distilled at Q4_K_M (21.9 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.