Qwen3.6 35B A3B REAP 90pct — Hardware Requirements & GPU Compatibility
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Qwen3.6 35B A3B REAP 90pct is a 6.1B-parameter open language model from DJLougen in the Qwen 3.6 family. It supports a context window of up to 262,144 tokens. At Q4_K_M it needs about 4.07 GB of VRAM — see which GPUs and Macs can run it below.
185 downloads
12 likesSpecifications
- Publisher
- DJLougen
- Family
- Qwen 3.6
- Parameters
- 6.1B
- Architecture
- Qwen3_5MoeForConditionalGeneration
- Context Length
- 262,144 tokens
- Vocabulary Size
- 248,320
- Release Date
- 2026-06-14
- License
- Apache 2.0
Get Started
HuggingFace
How Much VRAM Does Qwen3.6 35B A3B REAP 90pct Need?
Select a quantization to see compatible GPUs below.
| Quantization | Bits | VRAM | + Context | File Size | Quality |
|---|---|---|---|---|---|
| Q2_Kest. | 3.40 | 3 GB | 13.7 GB | 2.61 GB | 2-bit quantization with K-quant improvements |
| Q3_K_Mest. | 3.90 | 3.4 GB | 14.0 GB | 3.00 GB | 3-bit medium quantization |
| Q4_K_M | 4.80 | 4.1 GB | 14.7 GB | 3.69 GB | 4-bit medium quantization — most popular sweet spot |
| Q5_K_Mest. | 5.70 | 4.8 GB | 15.4 GB | 4.38 GB | 5-bit medium quantization — good quality/size tradeoff |
| Q6_Kest. | 6.60 | 5.5 GB | 16.1 GB | 5.07 GB | 6-bit quantization, very good quality |
| Q8_0est. | 8.00 | 6.5 GB | 17.2 GB | 6.15 GB | 8-bit quantization, near-lossless |
| BF16est. | 16.00 | 12.7 GB | 23.3 GB | 12.30 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 REAP 90pct?
Q4_K_M · 4.1 GBQwen3.6 35B A3B REAP 90pct (Q4_K_M) requires 4.1 GB of VRAM to load the model weights. For comfortable inference with headroom for KV cache and system overhead, 6+ GB is recommended. Using the full 262K context window can add up to 10.7 GB, bringing total usage to 14.7 GB. 50 GPUs can run it, including NVIDIA GeForce RTX 5090, NVIDIA GeForce RTX 3090 Ti.
Runs great
— Plenty of headroomWhich Devices Can Run Qwen3.6 35B A3B REAP 90pct?
Q4_K_M · 4.1 GB59 devices with unified memory can run Qwen3.6 35B A3B REAP 90pct, including NVIDIA DGX H100, NVIDIA DGX A100 640GB, iPhone 17.
Runs great
— Plenty of headroom
Decent
— Enough memory, may be tightWhere to Download Qwen3.6 35B A3B REAP 90pct
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 REAP 90pct need?
Qwen3.6 35B A3B REAP 90pct requires 4.1 GB of VRAM at Q4_K_M, or 12.7 GB at BF16. Full 262K context adds up to 10.7 GB (14.7 GB total).
VRAM = Weights + KV Cache + Overhead
Weights = 6.1B × 4.8 bits ÷ 8 = 3.7 GB
KV Cache + Overhead ≈ 0.4 GB (at 2K context + ~0.3 GB framework)
KV Cache + Overhead ≈ 11 GB (at full 262K context)
VRAM usage by quantization
Q4_K_M4.1 GBQ4_K_M + full context14.7 GB- What's the best quantization for Qwen3.6 35B A3B REAP 90pct?
For Qwen3.6 35B A3B REAP 90pct, Q4_K_M (4.1 GB) offers the best balance of quality and VRAM usage. Q5_K_M (4.8 GB) provides better quality if you have the VRAM. The smallest option is Q2_K at 3 GB.
VRAM requirement by quantization
Q2_K3.0 GBQ4_K_M ★4.1 GBQ5_K_M4.8 GBQ6_K5.5 GBQ8_06.5 GBBF1612.7 GB★ Recommended — best balance of quality and VRAM usage.
- Can I run Qwen3.6 35B A3B REAP 90pct on a Mac?
Qwen3.6 35B A3B REAP 90pct requires at least 3 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 REAP 90pct locally?
Yes — Qwen3.6 35B A3B REAP 90pct can run locally on consumer hardware. At Q4_K_M quantization it needs 4.1 GB of VRAM. Popular tools include Ollama, LM Studio, and llama.cpp.
- How fast is Qwen3.6 35B A3B REAP 90pct?
At Q4_K_M, Qwen3.6 35B A3B REAP 90pct can reach ~1081 tok/s on AMD Instinct MI350X. On NVIDIA GeForce RTX 4090: ~161 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 ÷ 4.1 × 0.65 = ~1278 tok/s
Estimated speed at Q4_K_M (4.1 GB)
~1278 tok/s~161 tok/s~1278 tok/s~1081 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 REAP 90pct?
At Q4_K_M, the download is about 3.69 GB. The full-precision BF16 version is 12.30 GB. The smallest option (Q2_K) is 2.61 GB.
- Which GPUs can run Qwen3.6 35B A3B REAP 90pct?
50 consumer GPUs can run Qwen3.6 35B A3B REAP 90pct at Q4_K_M (4.1 GB). Top options include AMD Radeon RX 6700 XT, AMD Radeon RX 6800, AMD Radeon RX 6800 XT. 50 GPUs have plenty of headroom for comfortable inference.
- Which devices can run Qwen3.6 35B A3B REAP 90pct?
59 devices with unified memory can run Qwen3.6 35B A3B REAP 90pct at Q4_K_M (4.1 GB), including AMD Ryzen AI 9 HX 370 (Strix Point) Laptop, ASUS Ascent GX10, Apple iPhone 17 Pro, Asus ROG Flow Z13 (2025, 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.