Qwen 3.6 is a toy compared to DeepSeek V4 Flash or Pro. These models can now run on Apple Silicon hardware with as little as 32GB RAM for the Flash (with 2-bit quant, which is still quite capable) using SSD offloading, with just-about-reasonable performance for interactive use, and far better performance on longer contexts than Qwen (due to the more efficient KV cache/attention mechanisms in DeepSeek).

Very significant improvements may be viable for unattended inference via large-scale batches, which can reuse sparse experts and thereby mask some of the latency involved - this is quite unique to DeepSeek, again due to its efficient KV cache.

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greenavocado 2 hours ago | parent [-]

Qwen 3.6 27B still curb stomps Deepseek V4 in coding

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epolanski an hour ago | parent [-]

1. Deepseek V4 is still in preview (training is not finished)

2. Qwen is much more demanding and borderline unusable on consumer hardware because it's a dense model. The 27B parameters are active all time for each token. It's not a MoE architecture where a router activates only some of them.

3. Qwen doesn't like quantization at all.

	▲	kgeist 10 minutes ago \| parent \| next [-]
		I have to disagree with most claims. I run Qwen3.6-27b at 260k context and 40-60 tok/sec. It handles most coding problems as well as Sonnet 4.6 under OpenCode on our production tasks. (As an experiment, I run the same prompts for the same issues in parallel for Qwen 3.6 and Sonnet 4.6 and usually see little difference in performance). I see zero degradation from quantization in practice. Settings: RTX 5090, 5-bit weights (Unsloth), FP8 KV cache. Last time I tried running large MoEs on this PC, they had inferior quality at 2-3 bits compared to much smaller dense models at 5-6 bits, and were slower anyway.
	▲	trollbridge 8 minutes ago \| parent \| prev [-]
		You can run the 35B A3B model which is an MoE. Runs great on a 5090.