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Google / gemma4_text

gemma-4-26B-A4B-it

25.8 billion parameters, routing 8 of 128 experts per token.

The official Google Gemma 4 card documents a 256K context and one shared expert for the 26B A4B MoE; its config supplies the 128 total and 8 active routed experts. Open the official context documentation ↗

Open in the calculator →

Architecturepublished data
Quick answer
gemma-4-26B-A4B-it needs about 16.8 GB at Q4_K_M — the format most people download — with 8,192 tokens of context. It fits entirely on 12 of 18 common devices, starting with the NVIDIA GeForce RTX 4090 (24 GB). What else fits in 24 GB. No hardware for it? Rent a GPU that runs it, priced live.

Sized at 8,192 tokens of context with the whole model in device memory, across 18 common devices. Speeds are estimates, not benchmarks.

Find the NVIDIA GeForce RTX 4090, the smallest card listed here that holds it: Amazon ↗ · eBay (new and used) ↗Store links may pay us a commission. They never decide which card is suggested — the memory arithmetic does.

Weight-file size by format

FormatSizeRangeBasis
FP1650.8 GB49.3 GB – 51.9 GBreconstructed size
Q8_027.0 GB26.2 GB – 27.6 GBreconstructed size
Q6_K20.9 GB20.2 GB – 21.3 GBreconstructed size
Q5_K_M18.1 GB17.4 GB – 18.5 GBreconstructed size
Q5_017.6 GB17.0 GB – 18.0 GBreconstructed size
Q4_K_M15.4 GB14.8 GB – 15.8 GBreconstructed size
Q4_014.5 GB13.9 GB – 14.8 GBreconstructed size
Q3_K_M12.7 GB11.6 GB – 13.8 GBreconstructed size
Q2_K9.53 GB8.91 GB – 10.1 GBreconstructed size

A file size is not the memory a run needs: the KV cache and the runtime reserve come on top, and the calculator adds both.

What the labels mean
published data
Read from a published source — a file's byte count, a model's configuration or a manufacturer's specification — or exact arithmetic on such values. Not a measurement on a machine.
reconstructed size
Weight size reconstructed from the pinned architecture, because no published file exists.
size range
Only a lower and an upper bound are claimed for this weight size.

Hardware ladder

Q4_K_M at 8,192 tokens · 12 of 18 devices hold it entirely
DeviceMemoryVerdictDecodeCalculator
NVIDIA GeForce RTX 507016.8 GB of 12 GB10 layers on system RAM~62 tok/s with offloadOpen →
AMD Radeon™ RX 9070 XT16.8 GB of 16 GB2 layers on system RAM~135 tok/s with offloadOpen →
NVIDIA GeForce RTX 408016.8 GB of 16 GB2 layers on system RAM~133 tok/s with offloadOpen →
NVIDIA GeForce RTX 508016.8 GB of 16 GB2 layers on system RAM~161 tok/s with offloadOpen →
NVIDIA GeForce RTX 5070 Ti16.8 GB of 16 GB2 layers on system RAM~154 tok/s with offloadOpen →
NVIDIA GeForce RTX 5060 Ti 16GB16.8 GB of 16 GB2 layers on system RAM~94 tok/s with offloadOpen →
NVIDIA GeForce RTX 409016.8 GB of 24 GBfits~257 tok/sOpen →
AMD Radeon™ RX 7900 XTX16.8 GB of 24 GBfits~281 tok/sOpen →
NVIDIA GeForce RTX 309016.8 GB of 24 GBfits~239 tok/sOpen →
NVIDIA GeForce RTX 509016.8 GB of 32 GBfits~458 tok/sOpen →
NVIDIA RTX 6000 Ada Generation16.8 GB of 48 GBfits~245 tok/sOpen →
Apple M4 Pro16.8 GB of 64 GBfits~62 tok/sOpen →
Apple M5 Pro16.8 GB of 64 GBfits~67 tok/sOpen →
AMD Ryzen AI Max+ 395 with Radeon 8060S16.8 GB of 128 GBfits~75 tok/sOpen →
Apple M3 Max16.8 GB of 128 GBfits~78 tok/sOpen →
Apple M4 Max16.8 GB of 128 GBfits~92 tok/sOpen →
NVIDIA DGX Spark16.8 GB of 128 GBfits~70 tok/sOpen →
Apple M2 Ultra16.8 GB of 192 GBfits~108 tok/sOpen →

Decode is a calibrated estimate from the published calibration; capacity is the manufacturer's published ceiling, not guaranteed free memory. Fitting in memory is not the same as loading: whether the runtime and version you have supports this architecture and format on that machine has not been tested here. Offloaded rows assume there is enough system RAM for the overflow — the calculator checks that against the RAM you declare. Each “Open” link keeps this model, format and context.

Run it in the cloud

Rent a GPU that runs gemma-4-26B-A4B-it

Every rentable machine that holds the whole model, from Vast.ai and RunPod, with this site's speed estimate for each and the provider's own price, read live. Cheapest first; sort by cost per token or speed instead, or switch the format.

Prices · 18:30 UTC, 22 Sept
27 configurations in stock · 6 out of stock
MachineSpeedPer hourPer M tokensWhereRent
2× RTX 306024 GBcheapest~183 tok/sFaster than you read$0.11$0.16Vast.aiMarketplace · 98.0% reliableRent on Vast.ai
1× RTX 309024 GBrecommendedbest value~239 tok/sFaster than you read$0.12$0.14Vast.aiMarketplace · 99.5% reliableRunPod $0.50/h Rent on Vast.ai
1× RTX 3090 Ti24 GB~257 tok/sFaster than you read$0.19$0.20Vast.aiMarketplace · 99.6% reliableRunPod $0.27/h Rent on Vast.ai
2× RTX 407024 GB~257 tok/sFaster than you read$0.19$0.20Vast.aiMarketplace · 99.8% reliableRent on Vast.ai
2× RTX 4060 Ti 16GB32 GB~147 tok/sFaster than you read$0.22$0.42Vast.aiMarketplace · 99.7% reliableRent on Vast.ai
1× L424 GB~76 tok/sFaster than you read$0.26$0.93Vast.aiMarketplace · 98.9% reliableRunPod $0.49/h Rent on Vast.ai
2× RTX 5060 Ti 16GB32 GB~228 tok/sFaster than you read$0.26$0.31Vast.aiMarketplace · 99.5% reliableRent on Vast.ai
1× RTX 6000 Ada48 GB~245 tok/sFaster than you read$0.26$0.30Vast.aiMarketplace · 99.9% reliableRunPod $0.84/h Rent on Vast.ai

No GPU at all

Use gemma-4-26B-A4B-it by the token

Here the rented card is as cheap as the API or cheaper per token ($0.14 per million), and it keeps your data on a machine you control. The API is still the easier start: nothing to set up and nothing to switch off.

First time renting a GPU? How it works, in four steps
  1. Create an account and add credit. Both providers are prepaid: $10 is enough to try any card on this page for hours. A new RunPod account that arrives through a referral link gets a one-time $5 credit when it first adds $10.
  2. Pick the card and the count shown here, and a template: llama.cpp or vLLM for an OpenAI-compatible API, or one with a web chat if you only want to talk to the model. “How to launch it” below gives the exact image and command.
  3. Wait for the download. The weights are fetched on the machine; tens of gigabytes take a few minutes on a data-centre connection. You pay by the second from the moment it starts.
  4. Stop it when you are done. A running machine bills even when idle. A stopped one costs nothing per hour, though its disk is usually still billed until you delete it.
How to launch it

No Q4_K_M file of gemma-4-26B-A4B-it is catalogued here, so there is no exact command to vouch for. Start the machine from the llama.cpp server image ghcr.io/ggml-org/llama.cpp:server-cuda and point -hf at a Q4_K_M build from the publisher or a quantizer you trust — search Hugging Face for one. Check its file size against the memory figure on this page before you rent.

Every machine holds the whole model at Q4_K_M and 8,192 tokens of context, no offload. Speeds are this site’s single-stream decode estimates; prices are what each provider’s own API quoted, on-demand, for the whole machine. Vast hosts below 98% measured reliability are left out.

Referral links Vast.ai, RunPod and Novita pay us a share of what you spend if you sign up through these buttons. It costs you nothing, and it never decides an order or a recommendation: both are computed from the live price and the speed, and options that pay us nothing are listed and recommended on the same terms. How we rank

Buying a card instead, or paying by the token? Compare a year of gemma-4-26B-A4B-it three ways →

Under the hood

Architecture, read from the publisher’s file

The numbers every figure above is computed from, with the file they came from.

Architecture

✓ Architecture read from the published config.json

Retrieved 2026-09-01 at pinned commit 4d7ae4984b7d; parameter count from the safetensors index at the same revision.

Architecture
gemma4_text
Layers
30
Hidden size
2,816
Attention heads
16
KV heads
8
Head dimension
256
Feed-forward width
2,112
Vocabulary
262,144
Context ceiling
262,144
Sliding window
1,024
Experts
128
Experts / token
8
Expert width
704
Shared experts
1

Where the memory goes

tokenembedding× 30 decoder blocksGrouped-query attention16 query · 8 KV headswindow 1,024Routed experts8 of 128 per tokenall residentoutputprojectiongrows with contextcapacity ≠ traffic

Grouped-query attention shares each key/value head across 2 query heads, so the KV cache is 50% of what multi-head attention would need at the same context.

This is a multimodal checkpoint (vision); the diagram and memory sizing above cover the text decoder. Encoder/audio/image activations are not included in the KV or weight figures.

Open the exact official config.json ↗

gemma-4-26B-A4B-it, card by card

One page per device: whether gemma-4-26B-A4B-it fits, at which formats, and how fast.

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Derivatives this page also answers for

Each of these repositories declares gemma-4-26B-A4B-it as its base, and its published configuration matches this one on every field that decides memory. Its page lists what was compared and any files it publishes. Paste any other repository.

google/gemma-4-26B-A4B-it-qat-q4_0-unquantizedhiwaifu-research/WaifuGemma4-26b-a4b-v1

More from Google

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Use this answer in your own product

As JSON, from the API
curl -s https://llmbottleneck.com/v1/analyze \
  -H "Authorization: Bearer $LLMB_KEY" -H "content-type: application/json" \
  -d '{"model":"google-gemma-4-26b-a4b-it","quantization":"Q4_K_M","context":8192,"hardware":"nvidia-geforce-rtx-4090"}'

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<script src="https://llmbottleneck.com/widget.js"
  data-model="google-gemma-4-26b-a4b-it" data-quantization="Q4_K_M"
  data-hardware="nvidia-geforce-rtx-4090" data-context="8192"></script>

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Citing this page

LLM Bottleneck. “gemma-4-26B-A4B-it VRAM and hardware requirements.” Architecture from google/gemma-4-26B-A4B-it at revision 4d7ae4984b7d, retrieved 2026-09-01. https://llmbottleneck.com/models/google-gemma-4-26b-a4b-it

Every figure above is either the published value or a reconstruction whose measured error is on the accuracy page.

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catalogue 2026-10-03models 327devices 135