Models / Gemma 4 26B A4B

Gemma 4 26B A4B

MoE enthusiast
HuggingFace → HuggingBay → Google → Find for my hardware

25.2B total, 3.8B active per token (MoE: 128 experts, top-8 + 1 shared). Multimodal (text + image). The small-MoE sweet spot - near-4B decode speed with 25B-class quality. NVFP4 (~14GB) needs NVIDIA Blackwell (vLLM) or Apple Silicon (MLX); Q4_K_M (~13GB weights, ~18GB runtime) for Ollama/llama.cpp.

AI-generated content marks

The provider reports that this model does not add embedded watermarks or provenance metadata to generated output.

Provider transparency docs →

coding reasoning chat general vision
Parameters
25.2B
Context
256k
License
other
Developer
Google
Origin
🇺🇸 USA
Released
Apr 2026

Scores

Coding
85
Reasoning
88
General
84

Score per dollar

2000 pts per $/M input

general_score (84) divided by cheapest input price ($0.04/M). Higher is better value. See live pricing.

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Guides covering Gemma 4 26B A4B

How AT&T cut AI coding costs 56% with open-weight models and LiteLLM Aug 28, 2026 DGX Spark benchmarks: which local model wins in 2026 Aug 19, 2026

Save your hardware and every model page answers the real question: will it run on your machine, and how fast?

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Run it locally

Per-quant memory needs and a static "can you run it?" reference - no rig entry required

NVFP4
14.0GB 15.0GB min 18.0GB rec
NVIDIA FP4 - Blackwell or Apple MLX, near-lossless
Q4_K_M
13.0GB 16.0GB min 18.0GB rec
Balanced - the usual local sweet spot
Q8_0
25.0GB 27.0GB min 30.0GB rec
Near-lossless
BF16
50.0GB 52.0GB min 56.0GB rec
Full quality, largest

The reference hardware

Schematic of the NVIDIA Jetson Orin NX 16GB reference rig - 16GB unified memory, 102 GB/s aggregate bandwidth
Schematic of the Single GTX 1080 Ti (11GB) reference rig - 11GB VRAM, 484 GB/s aggregate bandwidth
Schematic of the 4x H100 80GB (320GB) reference rig - 320GB VRAM, 13400 GB/s aggregate bandwidth
Schematic of the NVIDIA DGX Station 748GB reference rig - 748GB unified memory, 8000 GB/s aggregate bandwidth
Schematic of the 8x RTX 3090 rack (192GB) reference rig - 192GB VRAM, 7489 GB/s aggregate bandwidth
Schematic of the 4x RTX 5090 (128GB) reference rig - 128GB VRAM, 7168 GB/s aggregate bandwidth
Schematic of the AMD Instinct MI300X (192GB) reference rig - 192GB VRAM, 5324 GB/s aggregate bandwidth
Schematic of the 4x RTX 4090 (96GB) reference rig - 96GB VRAM, 4032 GB/s aggregate bandwidth
Schematic of the 2x RTX 5090 (64GB) reference rig - 64GB VRAM, 3584 GB/s aggregate bandwidth
Schematic of the 2x RTX 3090 (48GB) reference rig - 48GB VRAM, 1872 GB/s aggregate bandwidth
Schematic of the Single RTX 5090 (32GB) reference rig - 32GB VRAM, 1792 GB/s aggregate bandwidth
Schematic of the RTX PRO 6000 Blackwell (96GB) reference rig - 96GB VRAM, 1792 GB/s aggregate bandwidth
Schematic of the Mac Studio M4 Ultra 192GB reference rig - 192GB unified memory, 1092 GB/s aggregate bandwidth
Schematic of the Mac Studio M4 Ultra 512GB reference rig - 512GB unified memory, 1092 GB/s aggregate bandwidth
Schematic of the Single RTX 4090 (24GB) reference rig - 24GB VRAM, 1008 GB/s aggregate bandwidth
Schematic of the MacBook Pro M5 Max 128GB reference rig - 128GB unified memory, 614 GB/s aggregate bandwidth
Schematic of the Dual EPYC 9004 + 768GB DDR5-4800 reference rig - 768GB unified memory, 460 GB/s aggregate bandwidth
Schematic of the DGX Spark 128GB unified reference rig - 128GB unified memory, 273 GB/s aggregate bandwidth
Schematic of the Ryzen AI Max+ 395 128GB reference rig - 128GB unified memory, 256 GB/s aggregate bandwidth
Schematic of the Jetson AGX Orin 64GB reference rig - 64GB unified memory, 204 GB/s aggregate bandwidth
Schematic of the Epyc + 512GB DDR4-3200 + 2x RTX 3090 reference rig - 560GB unified memory, 204 GB/s aggregate bandwidth
Schematic of the Epyc + 512GB DDR4-2400 + 2x RTX 3090 reference rig - 560GB unified memory, 153 GB/s aggregate bandwidth

22 reference configs, drawn in-house. Scroll for more.

Can you run it? - reference rigs

Rig NVFP4 Q4_K_M Q8_0 BF16
NVIDIA Jetson Orin NX 16GB no -> cloud tight no -> cloud no -> cloud
Single GTX 1080 Ti (11GB) no -> cloud offload no -> cloud no -> cloud
4x H100 80GB (320GB) no -> cloud fast 1526.4t/s fast 1110.3t/s fast 708.1t/s
NVIDIA DGX Station 748GB no -> cloud fast 911.3t/s fast 662.9t/s fast 422.8t/s
8x RTX 3090 rack (192GB) no -> cloud fast 853.2t/s fast 620.6t/s fast 395.8t/s
4x RTX 5090 (128GB) fast 791.8t/s fast 816.5t/s fast 593.9t/s fast 378.8t/s
AMD Instinct MI300X (192GB) no -> cloud fast 606.6t/s fast 441.2t/s fast 281.4t/s
4x RTX 4090 (96GB) no -> cloud fast 459.3t/s fast 334.1t/s fast 213.1t/s
2x RTX 5090 (64GB) fast 395.9t/s fast 408.3t/s fast 297.0t/s fast 189.4t/s
2x RTX 3090 (48GB) no -> cloud fast 213.3t/s fast 155.1t/s offload
Single RTX 5090 (32GB) fast 198.0t/s fast 204.1t/s fast 148.5t/s offload
RTX PRO 6000 Blackwell (96GB) fast 198.0t/s fast 204.1t/s fast 148.5t/s fast 94.7t/s
Mac Studio M4 Ultra 192GB fast 131.6t/s fast 135.7t/s fast 98.7t/s fast 63.0t/s
Mac Studio M4 Ultra 512GB fast 131.6t/s fast 135.7t/s fast 98.7t/s fast 63.0t/s
Single RTX 4090 (24GB) no -> cloud fast 114.8t/s offload no -> cloud
MacBook Pro M5 Max 128GB fast 74.0t/s fast 76.3t/s fast 55.5t/s fast 35.4t/s
Dual EPYC 9004 + 768GB DDR5-4800 no -> cloud fast 52.5t/s fast 38.2t/s fast 24.4t/s
DGX Spark 128GB unified fast 30.2t/s fast 31.1t/s fast 22.6t/s ok 14.4t/s
Ryzen AI Max+ 395 128GB no -> cloud fast 29.2t/s fast 21.2t/s ok 13.5t/s
Jetson AGX Orin 64GB no -> cloud fast 23.3t/s ok 17.0t/s ok 10.8t/s
Epyc + 512GB DDR4-3200 + 2x RTX 3090 no -> cloud fast 23.3t/s ok 17.0t/s ok 10.8t/s
Epyc + 512GB DDR4-2400 + 2x RTX 3090 no -> cloud ok 17.5t/s ok 12.7t/s ok 8.1t/s

Fit tiers use the same will-it-run logic as the rig finder. For comfortable fits, the badge reflects decode speed: fast >=20 t/s, ok 8-20 t/s, slow <8 t/s. t/s is a bandwidth estimate, not a measured benchmark.

Download options

NVFP4 official -1% vs fp16
14.0GB dl 15.0GB min 18.0GB rec
REC RAM vs largest quant
NVFP4 - NVIDIA Blackwell via vLLM (nvidia/Gemma-4-26B-A4B-NVFP4) or Apple Silicon via MLX (ollama run gemma4:26b-nvfp4). Not supported on Ada/Ampere/Hopper/AMD.
GGUF on HF →
Q4_K_M official -5% vs fp16
13.0GB dl 16.0GB min 18.0GB rec
REC RAM vs largest quant
Q8_0 official -1% vs fp16
25.0GB dl 27.0GB min 30.0GB rec
REC RAM vs largest quant
BF16 official
50.0GB dl 52.0GB min 56.0GB rec
REC RAM vs largest quant
GGUF on HF →

Or run it in the cloud

Live per-provider pricing, throughput and uptime - refreshed less than a minute ago via OpenRouter. Click a column to sort.

Provider Type Input $/M Output $/M Cache $/M Tok/s Latency Uptime Value
Darkbloom
API 0.04 0.22 0.021 - - 100.00% best uptime
DekaLLM
API 0.06 0.33 0.040 - - 100.00%
DeepInfra
API 0.07 0.34 0.042 - - 100.00%
NextBit
API 0.08 0.26 0.042 - - 100.00%
Cloudflare
API 0.10 0.30 0.050 - - 100.00%
Makora
API 0.10 0.28 0.050 - - 100.00%
CoreWeave
API 0.10 0.30 0.050 - - 100.00%
Io Net
API 0.12 0.35 0.049 - - 100.00%
Novita
API 0.13 0.40 0.042 - - 100.00%
Parasail
API 0.13 0.40 0.050 - - 100.00%
Venice
API 0.13 0.40 0.050 - - 100.00%
SiliconFlow
API 0.14 0.40 0.050 - - 100.00%
Google
API 0.15 0.60 0.042 - - 100.00%

Default order: throughput among 95%+ uptime providers, then latency; subscriptions last. Sort by any column. Subscription rows show $/mo in the Value column - per-token columns are "-". Affiliate links are marked sponsored / nofollow. Confirm current pricing on the provider's site before committing.

Detailed API pricing page + JSON endpoint →

See who runs Google in production →

PRICE HISTORY

Inference cost over time

Data accumulates from the first daily sync - longer ranges populate over time. Prices come from OpenRouter snapshots, not a historical API.

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