Gemma 4 26B A4B GPU requirements: 12.9 GB at 4-bit, 51.6 GB at BF16
Gemma 4 26B A4B needs 51.6 GB of GPU memory for its weights at BF16, 25.8 GB at FP8 and 12.9 GB at 4-bit, plus 0.55 GB of KV cache for a 32K context. The cheapest node that runs it here is 1× RTX A4000 at 4-bit, for $0.03 an hour on spot.
Weights plus a 32K 16-bit KV cache, with 10% of the node's memory kept free. Billed per minute.
- Parameters
- 25.2B3.8B active per token
- Weights, 4-bit
- 12.9 GBBF16: 51.6 GB
- KV cache, 32K
- 0.55 GBOne sequence, 16-bit cache
- Context window
- 256KNative
How much VRAM Gemma 4 26B A4B needs, format by format.
Weights are parameters × bytes per parameter. Each context column adds the KV cache of one sequence of that length.
| Format | Weights | + 8Kcontext | + 32Kcontext | + 128Kcontext | + 256Kcontext | Cheapest node, 32K |
|---|---|---|---|---|---|---|
| BF16Published weights, fine-tuning | 51.6 GB | 51.9 GB | 52.2 GB | 53.2 GB | 54.5 GB | 1× A100 PCIe 80GB · $0.09/h |
| FP8Hopper, Ada, Blackwell, MI300 | 25.8 GB | 26.1 GB | 26.4 GB | 27.4 GB | 28.7 GB | 1× RTX PRO 4500 Blackwell · $0.09/h |
| 4-bitAWQ, GPTQ, GGUF Q4 | 12.9 GB | 13.2 GB | 13.5 GB | 14.5 GB | 15.8 GB | 1× RTX A4000 · $0.03/h |
The serving engine adds its own buffers, and an 8-bit KV cache halves the context columns. We keep 10% of a node's memory free when we say a model fits. Speed is a separate question: it follows memory bandwidth and, for a mixture of experts, the active parameters.
GPUs that run Gemma 4 26B A4B, cheapest first.
For each format, the smallest node of each GPU model that holds the weights and a 32K context, sorted by spot price. One row per GPU model; larger nodes of the same GPU work too.
| Format | GPU | GPU memory | Spot | On-demand | 730 h spot | Action |
|---|---|---|---|---|---|---|
| 4-bit | 1× RTX A4000Ampere · 13.5 GB needed | 16 GB | $0.03 | $0.05 | $21.90 | Deploy |
| 4-bit | 1× T4Turing · 13.5 GB needed | 16 GB | $0.04 | $0.07 | $29.20 | Deploy |
| 4-bit | 1× A30Ampere · 13.5 GB needed | 24 GB | $0.05 | $0.08 | $36.50 | Deploy |
| 4-bit | 1× L4Ada Lovelace · 13.5 GB needed | 24 GB | $0.05 | $0.08 | $36.50 | Deploy |
| FP8 | 1× RTX PRO 4500 BlackwellBlackwell · 26.4 GB needed | 32 GB | $0.09 | $0.19 | $65.70 | Deploy |
| FP8 | 1× RTX 5000 AdaAda Lovelace · 26.4 GB needed | 32 GB | $0.09 | $0.17 | $65.70 | Deploy |
| FP8 | 1× RTX 5090Blackwell · 26.4 GB needed | 32 GB | $0.09 | $0.19 | $65.70 | Deploy |
| FP8 | 1× L40Ada Lovelace · 26.4 GB needed | 48 GB | $0.09 | $0.19 | $65.70 | Deploy |
| BF16 | 1× A100 PCIe 80GBAmpere · 52.2 GB needed | 80 GB | $0.09 | $0.19 | $65.70 | Deploy |
| BF16 | 2× V100 32GBVolta · 52.2 GB needed | 64 GB | $0.12 | $0.18 | $87.60 | Deploy |
| BF16 | 4× RTX A4000Ampere · 52.2 GB needed | 64 GB | $0.12 | $0.20 | $87.60 | Deploy |
| BF16 | 2× A40Ampere · 52.2 GB needed | 96 GB | $0.12 | $0.18 | $87.60 | Deploy |
Spot capacity can be reclaimed with a 2-minute notice; for a live endpoint, use on-demand. How spot works · serving a language model.
What a long context costs in memory.
5 global layers (2 KV heads of 512, keys and values shared) and 25 local layers with a 1024-token window.
| Context | KV cache, 1 sequence | 8 sequences |
|---|---|---|
| 8K tokens | 0.29 GB | 2.35 GB |
| 32K tokens | 0.55 GB | 4.36 GB |
| 128K tokens | 1.55 GB | 12.4 GB |
| 256K tokens | 2.89 GB | 23.2 GB |
Serving several users
KV cacheEvery concurrent request keeps its own cache, so memory for context grows with users × context length. A server batching 8 requests of 32K needs eight times the single-sequence figure — 4.36 GB here.
- Recommended node1× RTX A4000
- Weights at 4-bit12.9 GB
- Context that fits, 1 sequence123K
Paged attention (vLLM, SGLang) allocates the cache in blocks as requests grow, so a node holds more short requests than the worst case suggests.
Gemma 4 26B A4B, as published.
- Developer
- GoogleMixture-of-experts Gemma 4, 128 experts
- Parameters
- 25.2B total, 3.8B active per token25.8B in the published checkpoint (embedding tables, prediction or vision modules included); memory figures use this count
- Architecture
- Mixture of experts, 30 layers5 global layers (2 KV heads of 512, keys and values shared) and 25 local layers with a 1024-token window
- Context window
- 262,144 tokensAs set in the published configuration
- Published format
- BF16, 51.6 GB
- Licence
- Apache 2.0
- Released
- 11 March 2026
- Source
- google/gemma-4-26B-A4B-itconfig.json read 26 October 2026
Serve it with vLLM
template: vLLMOn 1× RTX A4000, from the instance terminal:
$ vllm serve google/gemma-4-26B-A4B-it \ --tensor-parallel-size 1 \ --max-model-len 32768
Very recent architectures need a recent vLLM; the model card gives the minimum version and any extra flags.
Gemma 4 26B A4B on a GPU — the questions that come up
How much VRAM does Gemma 4 26B A4B need?
For the weights alone: 51.6 GB at BF16, 25.8 GB at FP8 and 12.9 GB at 4-bit. Add 0.55 GB of KV cache for one 32K-token context, or 2.89 GB at the full 256K window, and keep about 10% of the memory free for activations.
Can Gemma 4 26B A4B run on a single GPU?
One RTX 4090 (24 GB): yes, at 4-bit. One RTX 5090 (32 GB): yes, at FP8. One H100 SXM (80 GB): yes, at BF16. One H200 SXM (141 GB): yes, at BF16. One B200 SXM (180 GB): yes, at BF16. Those answers assume a 32K context and the widest format that still fits.
What is the cheapest way to run Gemma 4 26B A4B?
Here, 1× RTX A4000 at 4-bit: $0.03 per hour on spot and $0.05 on-demand, billed per minute — $21.90 for a 730-hour month on spot. That node has 16 GB of GPU memory for the 13.5 GB this setup needs.
Does Gemma 4 26B A4B need less memory because only 3.8B parameters are active?
No. A mixture-of-experts model routes each token through a few experts, but every expert has to be resident in GPU memory, so the memory bill follows the 25.2B total. What the 3.8B active parameters change is the work per token: generation reads far less of the weights each step than a dense model of the same size would, which is why it is fast for its size.
Which format should I run it in?
BF16 keeps the published weights untouched and is the format for fine-tuning. FP8 halves the memory and runs in hardware on Hopper, Ada, Blackwell and MI300-class GPUs. 4-bit quantisation (AWQ, GPTQ, GGUF) quarters it, at a quality cost that is larger on small models than on big ones; check it against your own evaluation before relying on it.
How do I deploy it?
Pick the node in the table above and launch it with the vLLM template; the model downloads from Hugging Face (google/gemma-4-26B-A4B-it) on first start. Spread the weights over the node with --tensor-parallel-size equal to the GPU count, and set --max-model-len to the context you need. New architectures need a recent vLLM: check the model card for the minimum version.
Sizing another model? GPU requirements for 64 open models · serving a language model · fine-tuning.
Gemma 4 26B A4B on 1× RTX A4000 for $0.03 an hour.
Launch the node with the vLLM template, point it at google/gemma-4-26B-A4B-it, stop it when you are done. Prepaid, billed by the minute.
Billed per minute from the moment the instance is reachable. Minimum credit $30, no subscription.