GGUF Model Fits 24GB VRAM | Generated by AI
Question: Can a 15 GB GGUF of the Gemma 4 26B-A4B model fit into a 24GB VRAM GPU?
Answer: Yes, a 15 GB GGUF of the Gemma 4 26B-A4B model will easily fit into a GPU with 24 GB of VRAM (such as an RTX 3090 or RTX 4090).
Here is a breakdown of why this works well and what you can expect:
VRAM Budget Allocation
- Model Weights: The model itself takes up exactly 15 GB of VRAM when fully offloaded to the GPU.
- Remaining VRAM: You have roughly 9 GB of headroom remaining ($24\text{ GB} - 15\text{ GB} = 9\text{ GB}$).
- Operating System & Display: Your OS and background applications usually occupy between 1 GB to 2 GB of VRAM, leaving you with roughly 7 GB to 8 GB of free space.
Context Window and Thinking Mode Impact
The remaining VRAM will act as your buffer for the KV Cache (the model’s memory of the active conversation).
- The 26B-A4B Architecture: Gemma 4 26B-A4B is a Mixture-of-Experts (MoE) model. While it has 25.2B total parameters, only 3.8B parameters are active per token. This keeps the actual inference execution incredibly fast.
- Context Limit: The model natively supports up to a 256K token context window. However, filling up the full 256K context requires a substantial amount of extra VRAM for the KV cache.
- Thinking Mode: Gemma 4 features a built-in reasoning system (
<|think|>). When this mode is active, the model generates internal reasoning tokens before presenting its final answer, which naturally consumes more context space.
Recommendation
With 7 GB to 8 GB of VRAM left over for the context cache, you will comfortably be able to run long-context conversations (around 16K to 32K tokens safely) without running out of memory (OOM). If you intend to stretch the context closer to its 256K maximum, you may need to look into FlashAttention or hybrid CPU/VRAM offloading via llama.cpp.