Qwen4 27B vs Hy4 770B: Local Deployment Hardware Reality
This analysis compares Qwen4's anticipated 27B model with Hunyuan Hy4's 770B MoE, showing Hy4 demands an 8-GPU cluster (385GB+ VRAM) impossible for consumer hardware, while Qwen4 27B runs on a single 24-32GB GPU, making Qwen the only viable local choice.
Release Status: Hy4 Available, Qwen4 Still a Roadmap
As of the article's writing date (November 3), Hunyuan Hy4 preview has been open-source for three months. The article states Tencent released and open-sourced Hy4 on August 28, 2026, with 770B total parameters, 49B activated parameters, and 1M context length. Weights are downloadable from Hugging Face, ModelScope, and GitCode. Cloud pricing is set at 6 yuan per million input tokens and 18 yuan per million output tokens via Tencent Cloud Tokenhub and OpenRouter.
In contrast, Qwen4 remains at the "official roadmap" stage. Alibaba has not published any Qwen4 weights, model card, license, or benchmark scores on Hugging Face. The four tiers (Max, Plus, Flash, 27B) were outlined at the September Yunqi conference, and the October 29 keynote by Alibaba Cloud CTO Li Feifei added a "next week" timing hint pointing to a November 2–8 release window. Therefore, the current "showdown" compares a released model (Hy4) against an expected one (Qwen4).
⚠️ Any Qwen4 figures labeled "projected" or "estimated" come from community reverse-engineering of PRs and kernel clues; Alibaba has not officially confirmed them. Do not treat them as a spec sheet.
Hard Parameter Comparison: The Gap Is Deployability, Not Just Strength
Scale differs by nearly 30×. Hy4 is a 770B total-parameter flagship MoE; Qwen4's open-weight tier is 27B (estimated ~7.4B activated parameters based on Qwen3.8-27B dense architecture). This is an order-of-magnitude difference, not merely "a bit smaller."
Context length matches at 1M. Hy4 natively supports 1M tokens; Qwen4 is projected to have 262K native context with extension to 1M. Both suffice for long-document tasks.
Open weights: Hy4 already available, Qwen4 27B expected. This is the critical factor for local users, but availability does not guarantee runnability.
Local Deployment Reality: Hy4 Cannot Run on Consumer Hardware
The core finding: Hunyuan's official vLLM deployment recipe for Hy4 specifies tensor-parallel-size 8 — eight GPUs . At 770B parameters, even FP8 precision requires 770 GB VRAM; 4-bit quantization still needs ~385 GB. The consequences are absolute:
All Macs (including 512 GB M5 Ultra): cannot run Hy4. 512 GB unified memory cannot fit the 385 GB Q4 quantized model, let alone FP8.
Consumer GPUs (24/32 GB): completely out of reach.
Only 8× H100-class clusters can handle it.
Meanwhile, Qwen4's 27B tier (assuming it follows Qwen3.8-27B's dense path, ~15 GB at Q4) fits comfortably on a single 24 GB RTX 3090, 32 GB RTX 5090, 32 GB M6, or 64 GB MacBook Pro . Even if it adopts a light MoE design like Qwen3.8-Flash, its footprint remains far below Hy4's 770B.
Thus, hardware has already cast the vote for local users: Hy4 is cloud-API only; for local large-model work, wait for Qwen4 27B weights or use Qwen3.8-27B today.
Hardware-Specific Recommendations
32 GB M6 / 64 GB MBP (M5 Pro): Qwen4 27B (expected) or current Qwen3.8-27B run easily; no Hunyuan model fits. → Bet on Qwen series.
128 GB M5 Max: 27B runs smoothly; can even accommodate 70B models. Hy4 impossible; Hy3 (295B/21B) needs 512 GB. → Bet on Qwen series; Hunyuan not an option.
256 / 512 GB M5 Ultra: Entire Qwen lineup plus 405B models run; Hy3 (295B/21B) becomes testable, but Hy4 still fails. → Dual track: Qwen main + Hy3 for Hunyuan experimentation.
Gaming PC (24/32 GB GPU): Same logic as Mac — 27B single-GPU viable, Hy4 impossible. → Bet on Qwen4 27B.
8× H100 cluster: Both families fully supported; no forced choice. → Only tier where "both" is realistic.
Author's Conclusions
This week's "showdown" is a cloud API battle, not a local one. The contest is about intelligence ceiling, pricing aggression, and agent capabilities — judgment reserved until Qwen4 actually releases weights and benchmarks.
Local users' bets are welded shut by hardware. In the 20–35B sweet spot, Alibaba fields Qwen3.8-27B now and Qwen4 27B soon; Tencent's open weights jump from 0.5–7B straight to 295B (Hy3) and 770B (Hy4), leaving the local sweet spot entirely empty.
Bottom line: bet on Qwen series for local deployment; treat Hunyuan Hy4 as a cloud productivity tool, not something you can bring home.
The author promises to benchmark Qwen4 27B locally the moment weights drop — follow "Lao Guo's Learning Space" for updates.
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