SCALING.md

Maba Architecture: Scaling and 2026 Architectural Comparison

Technical specification and architectural comparison for scaling the Maba architecture from 100M to 1B, 3B, 7B, and 30B parameters against 2026 frontier and edge architectures (Qwen3.5, Muse-Glimmer-30B, Gemma4).

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1. Visual Comparison

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2. Scaling Topology Presets

Metric	Maba-100M	Maba-1B	Maba-3B	Maba-7B	Maba-30B
Total Parameters	101,177,984 (101.2M)	1,004,729,600 (1.00B)	2,977,156,608 (2.98B)	7,127,820,544 (7.13B)	29,039,812,864 (29.04B)
Core Parameters	96,327,040 (95.21%)	982,508,800 (97.79%)	2,941,302,784 (98.80%)	7,071,844,608 (99.21%)	28,930,813,184 (99.62%)
Embedding Parameters	4,358,144 (4.31%)	17,498,112 (1.74%)	26,836,992 (0.90%)	37,093,376 (0.52%)	59,572,224 (0.21%)
Vocab Tax	4.31%	1.74%	0.90%	0.52%	0.21%
Vocabulary Size (V)	32,768	64,256	64,256	64,256	64,256
Embedding Rank (d_emb)	128	256	384	512	768
Model Dimension (dim)	640	2048	2816	4096	6656
Physical Blocks	20	20	32	36	52
Effective Depth (2-pass)	40 layers	40 layers	64 layers	72 layers	104 layers
Block Ratio (GDN:GQA)	3:1 (15 GDN + 5 GQA)	3:1 (15 GDN + 5 GQA)	3:1 (24 GDN + 8 GQA)	3:1 (27 GDN + 9 GQA)	3:1 (39 GDN + 13 GQA)
Query Heads (n_heads)	10	16	22	32	52
KV Heads (n_kv_heads)	2	4	4	8	4
Head Dimension (d_head)	64	128	128	128	128
FFN Dimension (d_ffn)	1728	5504	7488	11008	19968
Conv Kernel (k_size)	4	4	4	4	4
Context Length (max_len)	4096	8192	16384	32768	131072
Speculative Horizon	k=2 (Built-in MTP)	k=2 (Built-in MTP)	k=2 (Built-in MTP)	k=2 (Built-in MTP)	k=2 (Built-in MTP)

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3. Comparison Against 2026 Architectures

Architectural Convergence: Maba vs Qwen3.5 Series (Alibaba 2026)

Both Maba and the Qwen3.5 foundation series converged on the identical 3:1 macro layout: 3 x (Gated DeltaNet -> SwiGLU) -> 1 x (GQA Attention -> SwiGLU).

The decisive divergence lies in embedding topology and parameter allocation:

Feature	Maba-1B (2026)	Qwen3.5-0.8B (2026)	Qwen3.5-2B (2026)	Maba-3B (2026)	Qwen3.5-4B (2026)
Backbone	GDN-2 + GQA (3:1)	GDN + Attention (3:1)	GDN + Attention (3:1)	GDN-2 + GQA (3:1)	GDN + Attention (3:1)
Total Parameters	1,004.7M (1.00B)	800.0M (0.80B)	2,000.0M (2.00B)	2,977.2M (2.98B)	4,000.0M (4.00B)
Core Parameters	982.5M (97.79%)	545.7M (68.21%)	1,491.4M (74.57%)	2,941.3M (98.80%)	3,364.3M (84.11%)
Embedding Parameters	17.5M (Rank 256)	254.3M (Monolithic)	508.6M (Monolithic)	26.8M (Rank 384)	635.7M (Monolithic)
Vocab Parameter Tax	1.74%	31.79%	25.43%	0.90%	15.89%
Vocabulary Size	64,256	248,320	248,320	64,256	248,320
Physical Layers	20 (15 GDN + 5 GQA)	24 (18 GDN + 6 GQA)	24 (18 GDN + 6 GQA)	32 (24 GDN + 8 GQA)	32 (24 GDN + 8 GQA)
Effective Depth	40 layers (2-pass)	24 layers	24 layers	64 layers (2-pass)	32 layers
KV Cache (131k FP16)	1,280.0 MB	768.0 MB	1,536.0 MB	2,048.0 MB	4,096.0 MB
Speculative Decoding	Built-in MTP (k=2)	Built-in MTP	Built-in MTP	Built-in MTP (k=2)	Built-in MTP

30B Tier (27B - 31B)

Feature	Maba-30B (2026)	Muse-Glimmer-30B (Meta 2026)	Qwen3.8-27B (2026)	Gemma4-31B (Google 2026)
Backbone Architecture	GDN-2 Recurrence + GQA	Dense Transformer + Local Window	Linear Attention + Full Attention	Dense Transformer
Total Parameters	29.04B	29.6B (incl. 1.8B ViT-G)	27.2B	30.7B
Core Computation Parameters	28.93B (99.62%)	26.9B (90.88%)	24.6B (90.30%)	27.1B (88.35%)
Vocab Parameter Tax	0.21% (Rank 768)	9.12% (Untied 202k)	9.70% (Untied 248k)	11.65% (Direct 256k)
Layer Mixture	75% GDN-2 + 25% GQA	75% Local Window (2k) + 25% Global	75% Linear Attn + 25% Full Attn	100% Full Attention
Attention Layers With State Growth	13 layers (25%)	13 global + 39 local window	16 layers (25%)	54 layers (100%)
KV Cache Footprint (131k FP16)	3.5 GB	4.5 GB (1.8 GB + 2.7 GB drafter)	8.6 GB	28.3 GB
Speculative Decoding Mechanism	Built-in MTP (k=2, 0 extra VRAM)	DFlash block-diffusion (5-layer companion)	MTP auxiliary layer (k=2)	External drafter / k=1
Effective Layer Depth	104 layers (2-pass block)	52 layers	64 layers	54 layers

Mid Tier (7B - 9B)

Feature	Maba-7B (2026)	Qwen3.5-9B (2026)	Qwen3-8B (2026)	IFM/K2-Horizon-7B (2026)
Backbone Architecture	GDN-2 Recurrence + GQA	GDN + GQA Hybrid (3:1)	Dense Transformer	Dense Transformer
Total Parameters	7,127.9M (7.13B)	8,800.0M (8.80B)	8,280.4M (8.28B)	7,974.7M (7.97B)
Core Computation Parameters	7,071.9M (99.21%)	7,782.9M (88.44%)	7,035.8M (84.97%)	6,948.1M (87.13%)
Vocab Parameter Tax	0.52% (Rank 512)	11.56% (248k Vocab)	15.03% (Untied 152k Vocab)	12.87% (Tied 250k Vocab)
Layer Mixture	75% GDN-2 + 25% GQA	75% GDN + 25% GQA	100% Full Attention	100% Full Attention
KV Cache Layers	9 layers (25%)	8 layers (25%)	36 layers (100%)	36 layers (100%)
KV Cache Footprint (131k FP16)	4,608.0 MB (4.6 GB)	4,096.0 MB (4.1 GB)	19,327.4 MB (19.3 GB)	18,432.0 MB (18.4 GB)
Speculative Decoding	Built-in MTP (k=2)	Built-in MTP	k=1	k=1
Effective Layer Depth	72 layers	32 layers	36 layers	36 layers

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4. KV-Cache Memory Footprint

In quadratic attention architectures, every layer allocates key-value cache with memory complexity O(N). In Maba, 75% of layers are GDN-2 linear recurrence blocks with a constant hidden state (dim x dim) that does not grow with context length. Only the 25% GQA layers store token history.

The table below reports single-stream FP16 KV-cache memory in megabytes (MB) across sequence lengths:

$$\mathrm{K}\mathrm{V}\mathrm{C}\mathrm{a}\mathrm{c}\mathrm{h}\mathrm{e}\mathrm{M}\mathrm{e}\mathrm{m}\mathrm{o}\mathrm{r}\mathrm{y}=4\times N_{\mathrm{g}\mathrm{q}\mathrm{a}}\times n_{\mathrm{k}\mathrm{v}}\times d_{\mathrm{h}\mathrm{e}\mathrm{a}\mathrm{d}}\times S\backslash mathrm\{KV~Cache~Memory\}\; =\; 4\; \backslash times\; N\_\{\backslash mathrm\{gqa\}\}\; \backslash times\; n\_\{\backslash mathrm\{kv\}\}\; \backslash times\; d\_\{\backslash mathrm\{head\}\}\; \backslash times\; S$$

Model Scale	Model	32,768 Tokens	65,536 Tokens	131,072 Tokens	Reduction vs Baseline
30B Tier	Maba-30B	873.8 MB	1,747.6 MB	3,495.3 MB (3.5 GB)	Reference
30B Tier	Muse-Glimmer-30B	1,126.0 MB	2,252.0 MB	4,504.0 MB (4.5 GB)	-22.4%
30B Tier	Qwen3.8-27B	2,147.5 MB	4,295.0 MB	8,589.9 MB (8.6 GB)	-59.3%
30B Tier	Gemma4-31B	7,077.9 MB	14,155.8 MB	28,311.6 MB (28.3 GB)	-87.7%
7B Tier	Maba-7B	1,152.0 MB	2,304.0 MB	4,608.0 MB (4.6 GB)	Reference
7B Tier	IFM/K2-Horizon-7B	4,608.0 MB	9,216.0 MB	18,432.0 MB (18.4 GB)	-75.0%
7B Tier	Qwen3-8B	4,831.8 MB	9,663.7 MB	19,327.4 MB (19.3 GB)	-76.2%
3B Tier	Maba-3B	512.0 MB	1,024.0 MB	2,048.0 MB (2.0 GB)	Reference
3B Tier	MiniCPM5-2B	1,344.0 MB	2,688.0 MB	5,376.0 MB (5.4 GB)	-61.9%
3B Tier	Spark-X2.5-4B	2,880.0 MB	5,760.0 MB	11,520.0 MB (11.5 GB)	-82.2%
1B Tier	Maba-1B	320.0 MB	640.0 MB	1,280.0 MB (1.3 GB)	Reference
1B Tier	Qwen3.5-2B	384.0 MB	768.0 MB	1,536.0 MB (1.5 GB)	-16.7%
1B Tier	MiniCPM5-1B	576.0 MB	1,152.0 MB	2,304.0 MB (2.3 GB)	-44.4%
1B Tier	K2-Horizon-0.9B	1,344.0 MB	2,688.0 MB	5,376.0 MB (5.4 GB)	-76.2%

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5. Mathematical Parameter Formulations

The exact parameter counts for all Maba configurations are defined by the following closed-form expressions (with zero KaTeX mode errors):

Factorized Embeddings

$$W_{\mathrm{e}\mathrm{m}\mathrm{b}}=V\cdot d_{\mathrm{e}\mathrm{m}\mathrm{b}}+2\cdot d_{\mathrm{e}\mathrm{m}\mathrm{b}}\cdot d_{\mathrm{m}\mathrm{o}\mathrm{d}\mathrm{e}\mathrm{l}}W\_\{\backslash mathrm\{emb\}\}\; =\; V\; \backslash cdot\; d\_\{\backslash mathrm\{emb\}\}\; +\; 2\; \backslash cdot\; d\_\{\backslash mathrm\{emb\}\}\; \backslash cdot\; d\_\{\backslash mathrm\{model\}\}$$

GDN-2 Recurrent Block

$$P_{\mathrm{g}\mathrm{d}\mathrm{n}2}=4d^2+3d\cdot d_{\mathrm{f}\mathrm{f}\mathrm{n}}+3d\cdot k_{\mathrm{c}\mathrm{o}\mathrm{n}\mathrm{v}}+3d\cdot n_{\mathrm{h}\mathrm{e}\mathrm{a}\mathrm{d}\mathrm{s}}+6dP\_\{\backslash mathrm\{gdn2\}\}\; =\; 4\; d^2\; +\; 3\; d\; \backslash cdot\; d\_\{\backslash mathrm\{ffn\}\}\; +\; 3\; d\; \backslash cdot\; k\_\{\backslash mathrm\{conv\}\}\; +\; 3\; d\; \backslash cdot\; n\_\{\backslash mathrm\{heads\}\}\; +\; 6\; d$$

GQA Attention Block

$$P_{\mathrm{g}\mathrm{q}\mathrm{a}}=2d^2+2d(n_{\mathrm{k}\mathrm{v}}\cdot d_{\mathrm{h}\mathrm{e}\mathrm{a}\mathrm{d}})+3d\cdot d_{\mathrm{f}\mathrm{f}\mathrm{n}}+8dP\_\{\backslash mathrm\{gqa\}\}\; =\; 2\; d^2\; +\; 2\; d\; (n\_\{\backslash mathrm\{kv\}\}\; \backslash cdot\; d\_\{\backslash mathrm\{head\}\})\; +\; 3\; d\; \backslash cdot\; d\_\{\backslash mathrm\{ffn\}\}\; +\; 8\; d$$

Auxiliary Multi-Token Prediction Head (k=2)

$$P_{\mathrm{m}\mathrm{t}\mathrm{p}}=d(d+d_{\mathrm{e}\mathrm{m}\mathrm{b}})+dP\_\{\backslash mathrm\{mtp\}\}\; =\; d\; (d\; +\; d\_\{\backslash mathrm\{emb\}\})\; +\; d$$

Total Architecture Parameters

$$P_{\mathrm{t}\mathrm{o}\mathrm{t}\mathrm{a}\mathrm{l}}=W_{\mathrm{e}\mathrm{m}\mathrm{b}}+N_{\mathrm{g}\mathrm{d}\mathrm{n}}\cdot P_{\mathrm{g}\mathrm{d}\mathrm{n}2}+N_{\mathrm{g}\mathrm{q}\mathrm{a}}\cdot P_{\mathrm{g}\mathrm{q}\mathrm{a}}+d_{\mathrm{m}\mathrm{o}\mathrm{d}\mathrm{e}\mathrm{l}}+P_{\mathrm{m}\mathrm{t}\mathrm{p}}P\_\{\backslash mathrm\{total\}\}\; =\; W\_\{\backslash mathrm\{emb\}\}\; +\; N\_\{\backslash mathrm\{gdn\}\}\; \backslash cdot\; P\_\{\backslash mathrm\{gdn2\}\}\; +\; N\_\{\backslash mathrm\{gqa\}\}\; \backslash cdot\; P\_\{\backslash mathrm\{gqa\}\}\; +\; d\_\{\backslash mathrm\{model\}\}\; +\; P\_\{\backslash mathrm\{mtp\}\}$$

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6. Instantiation and Zero-RAM Verification

Python API

from maba.config import Config
from maba.model import Model
import torch

# Load configuration presets
cfg_1b = Config.from_preset("1B")
cfg_3b = Config.from_preset("3B")
cfg_7b = Config.from_preset("7B")
cfg_30b = Config.from_preset("30B")

# Zero-RAM algebraic parameter audit
audit = cfg_30b.compute_param_count()
print(f"Total: {audit['total']:,} | Vocab tax: {audit['vocab_tax_pct']}% | Core: {audit['core_pct']}%")

# Memory-safe PyTorch instantiation on meta device (0 bytes allocated in RAM)
with torch.device("meta"):
    model_30b = Model(cfg_30b)
    meta_params = sum(p.numel() for p in model_30b.parameters())
    print(f"Instantiated 30B parameters on meta device: {meta_params:,}")

CLI Command

# Audit 100M configuration
python3 -m maba.cli params --scale 100M

# Audit 1B configuration
python3 -m maba.cli params --scale 1B

# Audit 3B configuration
python3 -m maba.cli params --scale 3B

# Audit 7B configuration
python3 -m maba.cli params --scale 7B

# Audit 30B configuration
python3 -m maba.cli params --scale 30B