Gemma 4 E4B IT
E4BGoogle Gemma 4
Google's on-device Gemma 4: about 4.7B parameters doing the work plus a 2.8B per-layer embedding table, a 128K window, and image and audio input. Only 4 of its 42 layers keep a cache that grows with context, so the full 128K window costs about 2.0 GB of cache. At Q4 it is about 4.9 GB at 4K and 7.0 GB at the full window — an 8 GB card runs it comfortably at everyday context lengths. Sizes here count the whole model in GPU memory, which is what a Mac or a full GPU load uses; llama.cpp keeps the embedding table in system RAM instead, so on a discrete card it needs roughly 1.6 GB less VRAM than shown at Q4. Sizes use the calculator's generic rates — no GGUF file size could be checked.
131K
Max Context
2
Quant Variants
GGUF Q8_0
Best Quality
—
Accuracy Retained
Quantization Variants
Per-quant VRAM, quality loss, and inference speed on RTX 4090
Measured = run on this site · Estimated = not run here: calculated, or a figure this site has not verified · Community = public reports
Similar models
Compare with Gemma 4Gemma 4 E2B IT
Google Gemma 4
The smallest Gemma 4: about 2.3B parameters doing the work plus a 2.3B per-layer embedding table, a 128K window, and image and audio input. Its cache barely grows — 3 of 35 layers track the full context, about 0.8 GB at 128K. At Q4 it is about 3.0 GB at 4K and 3.8 GB at the full window — comfortable on any card in this index and on an 8 GB Mac. Sizes count the whole model in GPU memory; llama.cpp keeps the embedding table in system RAM, so on a discrete card it needs roughly 1.3 GB less VRAM than shown at Q4. Sizes use the calculator's generic rates — no GGUF file size could be checked.
Gemma 4 26B-A4B IT
Google Gemma 4
Google's Gemma 4 mixture-of-experts model: about 25B parameters in the text model, roughly 4B active per token, a 256K window and image input. Only 5 of its 30 layers keep a cache that grows with context, and those use fewer, wider heads, so the cache stays small — about 0.9 GB at 32K and 5.3 GB at the full 256K. At Q4 that is about 17 GB at 32K: comfortable on a 24 GB card, which can still load the whole window (tight). A 16 GB card is just too small. Sizes use the calculator's generic rates — no GGUF file size could be checked, and no per-level quality loss has been published.
Gemma 4 31B IT
Google Gemma 4
Google's dense Gemma 4 flagship: about 31B parameters, a 256K window and image input. Only 10 of its 60 layers cache the full context, and those use four wide KV heads, so 128K of context adds about 11 GB of cache where a conventional 60-layer design would need about 120 GB. At Q4 it is about 21 GB at 4K — on a 24 GB card that is right at the edge of comfortable, and tight from 32K. A 32 GB card holds 32K comfortably. Sizes use the calculator's generic rates — no GGUF file size could be checked, and no per-level quality loss has been published.
Gemma 3 4B IT
Google Gemma 3
Google Gemma 3 multimodal 4B. 128K context; strong vision + text on 8GB cards.
Running Gemma 4 E4B IT locally
At Q4_K_M and 4K of context, Gemma 4 E4B IT needs about 4.9 GB — 4.3 GB of weights, 0.10 GB of KV cache and a 0.4 GB activation buffer. The smallest cards in this index that clear that comfortably are 8 GB ones — 10 of them, from the Mac M1 8G up — and 70 of the 70 cards here do. These are calculated figures, not measurements: the estimate stops counting a card as comfortable at 88% of its VRAM, which is roughly the room a desktop session needs.
What longer context costs
Going from 4K to 32K adds about 0.44 GB, taking the total to 5.4 GB. That is gentler than the parameter count suggests: only 4 of 42 layers keep a KV cache that grows with context, and 20 more cap theirs at a sliding window, so the cache scales at a fraction of the usual rate. The model's native window is 128K; holding all of it at Q4_K_M would need about 7 GB.
Which build to download
This index only tracks Gemma 4 E4B IT in GGUF, across 2 levels (Q4_K_M, Q8_0). No per-level perplexity sweep has been published for this model, so the quality column is empty rather than estimated — within one model, more bits per weight is the only ordering the data supports. No throughput has been measured for this model on this site's hardware. GGUF runs on llama.cpp and Ollama across NVIDIA, AMD and Apple silicon; AWQ and GPTQ target vLLM on CUDA and ROCm; EXL2 is ExLlamaV2 and CUDA only.
Common questions
- How much VRAM does Gemma 4 E4B IT need?
- About 4.9 GB at Q4_K_M with 4K of context and batch 1, rising to roughly 5.4 GB at 32K. That figure is weights plus KV cache plus a 10% activation buffer, calculated from the model's architecture rather than measured on a card.
- Will Gemma 4 E4B IT run on a 8GB GPU?
- Yes — at Q4_K_M and 4K context it needs about 4.9 GB, which leaves 1.1 GB spare on a Mac M1 8G, or on any of the other 8 GB cards here (10 in all). That is the smallest size in this index that clears it comfortably; 70 of 70 do.
- Which quantization of Gemma 4 E4B IT should I use?
- No published quality comparison exists for this model, so pick by footprint: Q4_K_M is the level most people run, and a higher bits-per-weight level is more faithful. All 2 levels in the index are Q4_K_M, Q8_0.
Where this model fits
Sized at Q4_K_M with a 4K context window, smallest card first. Comfortable means the estimate uses at most 88% of the memory.
- Runs comfortably on
- Mac M1 8G8GB · +1.1Mac M3 8G8GB · +1.1RTX 40608GB · +3.1RTX 4060 Ti 8G8GB · +3.1Radeon RX 9060 XT 8G8GB · +3.1
- Ships in
- GGUF
- Try it yourself
- Size it in the calculatorGenerate the run command
How to actually run this
Deployment guides for this model and this class of hardware.
This page's figures change when the model or the runtime does.Last updated 2026-10-02 RSS → /feed.xml