Gemma 4 on 16GB GPU | Generated by AI

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Question: Can Gemma 4 run on a RunPod RTX 2000 Ada 16GB with SGLang?

Answer:

Yes, some Gemma 4 variants can run, but not the flagship ones. Gemma 4 is actually interesting for your GPU because Google released smaller and efficient variants, including E2B/E4B, 12B, 26B MoE, and 31B dense models. (blog.google)

Your 16GB VRAM changes the answer:

Gemma 4 variant Params RTX 2000 Ada 16GB
Gemma 4 E2B ~2B effective ✅ Easy
Gemma 4 E4B ~4B effective ✅ Best fit
Gemma 4 12B 12B ⚠️ INT4 possible
Gemma 4 26B A4B MoE 26B total / 4B active ⚠️ Maybe with aggressive quantization
Gemma 4 31B dense 31B ❌ Not practical

(Google DeepMind)

The interesting one: Gemma 4 E4B

This is probably the model I would actually deploy on your pod.

Why:

(Google DeepMind)

Expected memory:

Gemma 4 E4B BF16:

weights:
~8GB

runtime:
~1-2GB

KV cache:
several GB available

Good SGLang target.

Example:

python -m sglang.launch_server \
  --model-path google/gemma-4-E4B-it \
  --host 0.0.0.0 \
  --port 30000 \
  --max-model-len 16384

Gemma 4 26B A4B MoE

This is more interesting architecturally.

MoE:

total parameters:
26B

active per token:
4B

So compute is closer to a small model, but VRAM still stores experts.

A common mistake:

4B active != 4B memory

You still need to load most/all experts.

On 16GB:

Q4 quantized:
maybe possible

SGLang:
depends on quant backend
context length will be limited

I would not make this the production choice.

Gemma 4 31B

No.

Even INT4:

31B * 4 bits

≈ 15.5GB raw weights
+
runtime overhead
+
KV cache

Your GPU:

16GB total

means almost no room.

It may load, but it is not a useful server.

Compared with Qwen

For your exact GPU:

Model Quality Fit
Gemma 4 E4B ⭐⭐⭐⭐ ✅ excellent
Qwen3-14B-AWQ ⭐⭐⭐⭐⭐ ✅
Qwen2.5-Coder-14B-AWQ ⭐⭐⭐⭐⭐ coding ✅
Gemma 4 26B MoE ⭐⭐⭐⭐⭐ ⚠️
Gemma 4 31B ⭐⭐⭐⭐⭐ ❌

My pick:

General agent

Gemma 4 E4B

Coding agent

Qwen2.5-Coder-14B-AWQ

Maximum intelligence experiment

Gemma 4 26B-A4B Q4

One more thing: SGLang support matters. Gemma 4 uses newer architecture features, so before committing, check your installed SGLang/Transformers version. New model architectures often need the latest releases.

For this RunPod, I would probably benchmark:

1. Gemma-4-E4B-it
2. Qwen3-14B-AWQ
3. Qwen2.5-Coder-14B-AWQ

Those are the realistic “2026 local AI workstation” choices on 16GB VRAM. (Google DeepMind)

References:

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