[GH-ISSUE #1361] [Technical Architecture] Deep-Dive: MoE vs Dense, Position Encoding & Multi-modal Strategies Across 12+ Models #898

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opened 2026-10-26 21:31:58 +00:00 by bsakaguchi · 2 comments

bsakaguchi commented

2026-10-26 21:31:58 +00:00

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Originally created by @hernandez42 on GitHub (May 27, 2026).
Original GitHub issue: https://github.com/deepseek-ai/DeepSeek-V3/issues/1361

Technical Architecture Cross-Comparison

Motivation

While individual model papers describe their own architectures, a systematic cross-comparison reveals critical strategic insights for DeepSeek's next-gen design. This analysis covers 12 models across 6 architectural dimensions.

1. Routing Strategy Spectrum

Model	Strategy	Pros	Cons
GPT-5.6	Mixture of Depths	Dynamic per-token compute	Complex training
DeepSeek V3/R2	Top-2 Hard MoE	Simple, proven	Load imbalance, router jitter
Qwen 3	Dense (small) / MoE (large)	Flexible per-size	Two architectures to maintain
GLM-6	Dense + Shared Attention	Stable training	Higher inference cost
MIMO	Lightweight MoE (4 experts)	Fast inference	Limited capacity
Doubao	Dense + KV-cache optimization	Low latency	No MoE scaling benefit
ERNIE 5.0	Dense + Knowledge Graph injection	Domain accuracy	Hard to scale
Kimi	Long-context Dense	Best recall >200k	Quadratic attention cost
Baichuan 3	MoE (8 experts, Top-2)	Balanced load	Lower utilization
Yi 2.0	Dense	Simple pipeline	High compute
Spark 4.0	Dense + Voice fusion	Multi-modal by design	Modality coupling
SenseChat 6	Dense + Visual grounding	Strong vision-language	Weak pure text

2. Position Encoding & Context Window

Key insight: Claude's Hierarchical RoPE achieves 8% U-curve degradation at 1M context, while YaRN (DeepSeek) degrades 34% at 128k. This is the single largest architectural gap that doesn't require more compute — it requires better engineering.

Tier 1 (1M+ context, <10% degradation): Claude (HPE), Kimi (Ring Attention + sliding window)
Tier 2 (512k-1M, <20% degradation): GPT-5.6 (ALiBi++), Gemini (RoPE+)
Tier 3 (128k-256k, 20-35% degradation): DeepSeek (YaRN), Qwen (Dynamic NTK), GLM (RoPE scaling)
Tier 4 (<128k, severe degradation): MIMO, ERNIE

Model	Vision	Audio	Video	Integration Level
GPT-5.6	✅ Native	✅ Native	✅ Native	Full unified encoder
Gemini 3.5	✅ Native	✅ Native	✅ Native	Full unified encoder
Qwen 3	✅ Bridge	✅ Bridge	❌	Adapter-based
GLM-6	✅ Bridge	❌	❌	VisualGLM adapter
DeepSeek	❌	❌	❌	None (planned: visual bridge)
MIMO	✅ Lightweight	❌	❌	Mobile-optimized
Doubao	✅ Bridge	✅ Native	❌	Text+Image+Audio
ERNIE 5.0	✅ Bridge	✅ Bridge	✅ Bridge	Separate encoders
Spark 4.0	✅ Bridge	✅ Native	❌	Voice-first multi-modal

4. Key Technical Recommendations for DeepSeek Next-Gen

Replace YaRN with Hierarchical RoPE — immediate win for long-context capability
Adopt Soft MoE routing — solves load imbalance (expert utilization 0.2%-8.5% → expected 2%-6%)
Design visual bridge NOW — 72.5-point gap vs GPT-5.6 in video understanding is existential
CANN-compatible architecture — every architectural decision must consider Ascend operator availability

This analysis was compiled from public papers, technical reports, and benchmark results as of May 2026.

Originally created by @hernandez42 on GitHub (May 27, 2026). Original GitHub issue: https://github.com/deepseek-ai/DeepSeek-V3/issues/1361 # Technical Architecture Cross-Comparison ## Motivation While individual model papers describe their own architectures, a systematic cross-comparison reveals critical strategic insights for DeepSeek's next-gen design. This analysis covers 12 models across 6 architectural dimensions. ## 1. Routing Strategy Spectrum | Model | Strategy | Pros | Cons | |---|---|---|---| | GPT-5.6 | Mixture of Depths | Dynamic per-token compute | Complex training | | DeepSeek V3/R2 | Top-2 Hard MoE | Simple, proven | Load imbalance, router jitter | | Qwen 3 | Dense (small) / MoE (large) | Flexible per-size | Two architectures to maintain | | GLM-6 | Dense + Shared Attention | Stable training | Higher inference cost | | MIMO | Lightweight MoE (4 experts) | Fast inference | Limited capacity | | Doubao | Dense + KV-cache optimization | Low latency | No MoE scaling benefit | | ERNIE 5.0 | Dense + Knowledge Graph injection | Domain accuracy | Hard to scale | | Kimi | Long-context Dense | Best recall >200k | Quadratic attention cost | | Baichuan 3 | MoE (8 experts, Top-2) | Balanced load | Lower utilization | | Yi 2.0 | Dense | Simple pipeline | High compute | | Spark 4.0 | Dense + Voice fusion | Multi-modal by design | Modality coupling | | SenseChat 6 | Dense + Visual grounding | Strong vision-language | Weak pure text | ## 2. Position Encoding & Context Window **Key insight**: Claude's Hierarchical RoPE achieves 8% U-curve degradation at 1M context, while YaRN (DeepSeek) degrades 34% at 128k. This is the single largest architectural gap that doesn't require more compute — it requires better engineering. - **Tier 1 (1M+ context, <10% degradation):** Claude (HPE), Kimi (Ring Attention + sliding window) - **Tier 2 (512k-1M, <20% degradation):** GPT-5.6 (ALiBi++), Gemini (RoPE+) - **Tier 3 (128k-256k, 20-35% degradation):** DeepSeek (YaRN), Qwen (Dynamic NTK), GLM (RoPE scaling) - **Tier 4 (<128k, severe degradation):** MIMO, ERNIE ## 3. Multi-modal Architecture Comparison | Model | Vision | Audio | Video | Integration Level | |---|---|---|---|---| | GPT-5.6 | ✅ Native | ✅ Native | ✅ Native | Full unified encoder | | Gemini 3.5 | ✅ Native | ✅ Native | ✅ Native | Full unified encoder | | Qwen 3 | ✅ Bridge | ✅ Bridge | ❌ | Adapter-based | | GLM-6 | ✅ Bridge | ❌ | ❌ | VisualGLM adapter | | DeepSeek | ❌ | ❌ | ❌ | None (planned: visual bridge) | | MIMO | ✅ Lightweight | ❌ | ❌ | Mobile-optimized | | Doubao | ✅ Bridge | ✅ Native | ❌ | Text+Image+Audio | | ERNIE 5.0 | ✅ Bridge | ✅ Bridge | ✅ Bridge | Separate encoders | | Spark 4.0 | ✅ Bridge | ✅ Native | ❌ | Voice-first multi-modal | ## 4. Key Technical Recommendations for DeepSeek Next-Gen 1. **Replace YaRN with Hierarchical RoPE** — immediate win for long-context capability 2. **Adopt Soft MoE routing** — solves load imbalance (expert utilization 0.2%-8.5% → expected 2%-6%) 3. **Design visual bridge NOW** — 72.5-point gap vs GPT-5.6 in video understanding is existential 4. **CANN-compatible architecture** — every architectural decision must consider Ascend operator availability *This analysis was compiled from public papers, technical reports, and benchmark results as of May 2026.*

bsakaguchi

2026-10-26 21:31:58 +00:00

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bsakaguchi commented

2026-10-26 21:31:59 +00:00

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@github-actions[bot] commented on GitHub (Jul 5, 2026):

This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. If you believe this issue is still relevant, please leave a comment to keep it open. Thank you for your contributions!

@github-actions[bot] commented on GitHub (Jul 5, 2026): This issue has been automatically marked as stale because it has not had recent activity. It will be closed if no further activity occurs. If you believe this issue is still relevant, please leave a comment to keep it open. Thank you for your contributions!

bsakaguchi commented

2026-10-26 21:31:59 +00:00

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@github-actions[bot] commented on GitHub (Aug 1, 2026):

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