The Brutal Reality of Running Gemma 4 Locally

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Now the 11.6GB figure. This laptop has two GPUs: the RTX 3050 (GPU 1) and the AMD Radeon iGPU inside the Ryzen 7 6800H (GPU 0). The AMD iGPU has no dedicated VRAM. It borrows from system RAM dynamically. Windows adds them together:

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# How Windows calculates "total GPU memory" on a dual-GPU laptop



# RTX dedicated VRAM:          4.0 GB  (fast, ~192 GB/s)

# AMD iGPU shared system RAM:  7.6 GB  (slow, ~70-90 GB/s)

# ----------------------------------------

# Windows "GPU Memory":       11.6 GB  (misleading total)



# You do NOT have 11.6GB of fast VRAM

# You have 4GB fast + 7.6GB slow with a PCIe penalty to cross between them

13.2GB of 15.3GB used. 2.1GB available. Ollama is consuming roughly 4GB of system memory alongside the 3.5GB allocated in dedicated VRAM. The actual footprint for Gemma 4 E4B is 7 to 8GB total, split cleanly across two entirely different physical hardware pools running at wildly mismatched speeds. That split is exactly why generation feels slower than the model size alone would suggest.

At the same time, Ollama alone was consuming nearly 8GB of system RAM:

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# "The model loaded" does not mean the system is comfortable



# During Gemma 4 E4B inference on a 4GB RTX 3050 laptop:



# GPU memory pool

# ----------------

# Dedicated VRAM (RTX 3050)      -> 4.0 GB

# Shared DDR5 system memory      -> 7.6 GB

# Effective Windows "GPU Memory" -> 11.6 GB



# Real-world bottlenecks

# ----------------------

# [x] VRAM saturation

# [x] KV cache growth

# [x] Shared memory spillover

# [x] PCIe transfer overhead

# [x] Windows scheduler latency

# [x] Dual-GPU memory juggling



# Result

# ------

# The model technically fits.

# The hardware still struggles.

#

# Local inference on consumer laptops is often a

# memory orchestration problem, not a compute problem.

The result is that local AI performance becomes a memory orchestration problem long before it becomes a compute problem.

Hardware Tiers for Gemma 4 in 2026

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# What you can realistically run locally in 2026

# (and what it costs to buy the hardware right now)



# 4GB VRAM (RTX 3050 — my machine)

#   -> Gemma 4 E2B with Q4 quantization

#   -> short contexts only, KV cache fills fast

#   -> the floor for local AI, barely



# 8GB-12GB VRAM

#   -> comfortable Gemma 4 E4B

#   -> 7B models from other families run well

#   -> context length starts to matter



# 16GB-24GB VRAM

#   -> where Gemma 4 becomes reliable for real work

#   -> this is what Google probably had in mind at I/O

#   -> good luck finding one at a reasonable price



# 36GB-64GB Unified Memory (Apple Silicon)

#   -> best consumer option for serious local AI

#   -> no VRAM/RAM split, no PCIe penalty



# 96GB-192GB Unified Memory

#   -> 70B models, workstation territory

Measure Before You Tune

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# Get a baseline before changing anything

# Run this before and after every config change

./llama-bench -m gemma4-e2b-q4_k_m.gguf -p 512 -n 128

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# Windows: check Ollama RAM usage directly

Get-Process ollama | Select-Object ProcessName,WorkingSet64



# Or watch:

# Task Manager -> Performance -> Memory

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# Linux equivalent

free -h

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# Watch GPU utilization and VRAM together in one view

# util column = compute bound, mem column = memory bound

nvidia-smi dmon -s mu



# Apple Silicon: watch memory pressure in real time

# Red = unified memory is overcommitted

sudo memory_pressure

What Google Got Right and What They Left Out

Gemma 4 E2B running locally on a 4GB VRAM laptop is not nothing. Four years ago that would not have been possible at all. The model quality for its size is genuinely impressive.

But "runs on consumer laptops" and "runs well on consumer laptops" are different claims. The I/O keynote did not mention memory bandwidth, KV cache overflow, or the fact that the hardware shortage means GPUs with enough VRAM for comfortable inference are still expensive and unusually difficult to find.

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# What "model loaded successfully" actually guarantees



# NOT guaranteed:

# [ ] fits comfortably in VRAM

# [ ] KV cache has room to grow

# [ ] throughput will be usable

# [ ] PCIe offloading is avoided



# ONLY guaranteed:

# [x] weights entered memory without crashing

The model loading is the beginning of the problem. What happens after is a memory bandwidth race your hardware either wins or does not. Now you know which race you are in.

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