Best GPU for Gemma 4 26B-A4B Locally
Real-time prices and hardware recommendations updated for August 2026.
Gemma 4 26B-A4B is a mixture-of-experts model: all 26B parameters must sit in VRAM, but only 4B activate per token — so it generates far faster than a dense model of the same size, while still demanding the memory of one.
To run Gemma 4 26B-A4B locally you need roughly 16 GB of VRAM at Q4_K_M quantization with a 64k token context. The best-value card that fits is the Radeon RX 9060 XT 16GB (16 GB), which should generate around 28 tokens per second.
Adjust Context Length
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At 64k context, weights + KV cache reaches ~12GB — 16GB cards provide reliable headroom for the full context window.
The ideal consumer tier. High active token speed with native GQA optimization.
Recommended Hardware
Q8_0 loads all 26B expert weights at once (~30 GB). Requires a 32 GB+ card — the RTX 5090 is the only consumer option; workstation cards offer more memory headroom.
Other models with the same VRAM requirement
Because Gemma 4 26B-A4B's weights fit a 16 GB card, other models of a similar size run on the same GPU. Generation speed varies — mixture-of-experts models are faster, dense models slower — but any of these load in the same VRAM:
Optimizing Setup for Gemma 4 26B-A4B
Quantization Recommendations
For daily coding and reasoning tasks, Q4_K_M (4-bit quantization) offers the best balance of quality and memory efficiency — it reduces memory requirements by over 70% with minimal quality loss compared to FP16. Q8 and higher presets preserve more fidelity at the cost of significantly higher VRAM usage, which may force layer offloading and hurt throughput.
Recommended Local Software
We recommend using Ollama as the primary runner for local inference due to its automated GPU model splitting and context cache optimizations. For advanced fine-tuning or quantization splits, llama.cpp with Flash Attention compiled natively provides the best granular control.
Running Gemma 4 26B-A4B locally — FAQ
How much VRAM do I need to run Gemma 4 26B-A4B?
At a 64k context with KV cache quantization on, Gemma 4 26B-A4B needs about 16 GB of VRAM at Q4_K_M — the quantization most people should use. Dropping to Q3_K_M brings that down to roughly 12.2 GB at some quality cost, while Q8_0 needs about 30.9 GB for the best quality this model can give.
What size graphics card does Gemma 4 26B-A4B fit on?
Gemma 4 26B-A4B needs about 16 GB at Q4_K_M, so a 16 GB card is the smallest common size that holds it entirely in VRAM. Anything smaller has to offload layers to system RAM, which typically costs you most of your generation speed.
Which quantization should I use for Gemma 4 26B-A4B?
Use Q4_K_M unless you have VRAM to spare. It needs about 16 GB and loses very little quality against full precision. Q8_0 needs about 30.9 GB for a quality gain most people cannot detect in everyday coding and chat. Spend spare VRAM on a longer context instead.
How does context length affect the VRAM Gemma 4 26B-A4B needs?
Model weights are fixed, but the KV cache grows linearly with context. For Gemma 4 26B-A4B at a 64k context the cache is about 1 GB; doubling to 128k takes it to roughly 2 GB. Turning KV cache quantization off doubles those figures again.
Why is Gemma 4 26B-A4B faster than its parameter count suggests?
Gemma 4 26B-A4B is a mixture-of-experts model. Its 26B parameters all have to be held in VRAM, but only 4B are used to produce each token. Generation speed is bound by streaming those 4B active parameters, so it feels much closer to a 4B model than a 26B one — while still needing memory for the full 26B.
Can the same GPU run other models similar to Gemma 4 26B-A4B?
Yes. Gemma 4 26B-A4B needs about 16 GB at Q4_K_M, and any model whose weights fit the same card runs on it — including DiffusionGemma 26B-A4B (25.2B), Qwen3-14B (14.8B), Qwen2.5-14B (14.7B). The weights fit the same GPU; generation speed varies (mixture-of-experts models are faster, dense models slower).
▸How token speeds are estimated
Two metrics are shown per GPU: Read tok/s (how fast the model ingests your prompt) and Decode tok/s (how fast it streams tokens back). They model fundamentally different bottlenecks.
Read (Prefill)
The prompt is processed in one parallel pass. This is compute-bound: it saturates the GPU's tensor cores.
Decode (Generation)
Each new token requires loading the entire model's active weights from VRAM. This is memory-bandwidth-bound: the GPU stalls waiting for data, not computing.
Weights = (activeParams × bits ÷ 8) × 1.15 overhead. KV cache per step = activeParams × multiplier × contextK.
Architecture utilization factors
Left: decode factor — Right: read factor
Data sources
TFLOPS and memory bandwidth are read from the GPU database. When missing, bandwidth falls back to a hardcoded dictionary.
Limitations
These are analytical estimates, not benchmark results. Use them as a relative comparison, not an absolute performance guarantee.