Gemma 4 E4B needs ~9.0 GB VRAM. RTX 4090 Laptop 16GB has 16.0 GB. With Q4_K_M quantization, expect ~102 tok/s.
Operating mode
Interactive favors responsiveness, while light API and scale-out lean harder on serving readiness. The fit stays the same, but the recommendation lens changes.
Current mode
Balanced
Balanced for general local use. Keeps the ranking neutral across personal and serving workflows.
Select quantization to explore
Fit status
Runs well
Decode
101.5 tok/s
TTFT
1907 ms
Safe context
104K
Memory
9.0 GB / 16.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 101.5 tok/s | 1040 ms | 104K |
| Coding | A | Runs well | 101.5 tok/s | 1907 ms | 104K |
| Agentic Coding | A | Runs well | 101.5 tok/s | 2774 ms | 104K |
| Reasoning | A | Runs well | 101.5 tok/s | 2254 ms | 104K |
| RAG | A | Runs well | 94.4 tok/s | 3728 ms | 104K |
How Gemma 4 E4B (8B params) fits at each quantization level on RTX 4090 Laptop 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | A74 |
Q3_K_S | 3 | 3.9 GB | Low | A75 |
NVFP4 | 4 |
Copy-paste commands to run Gemma 4 E4B on your machine.
Run
ollama run gemma4:e4bYour hardware
| Model | Params | Grade | Decode | Capabilities |
|---|---|---|---|---|
| 9B | S | 90.2 tok/s | ||
| 14B | S | 58.3 tok/s |
Yes, RTX 4090 Laptop 16GB can run Gemma 4 E4B with a A grade (Runs well). Expected decode speed: 101.5 tok/s.
Gemma 4 E4B (8B parameters) requires approximately 9.0 GB of memory with Q4_K_M quantization.
The recommended quantization for Gemma 4 E4B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4090 Laptop 16GB, Gemma 4 E4B achieves approximately 101.5 tokens per second decode speed with a time-to-first-token of 1907ms using Q4_K_M quantization.
For coding workloads, Gemma 4 E4B on RTX 4090 Laptop 16GB receives a A grade with 101.5 tok/s and 104K context.
On RTX 4090 Laptop 16GB, Gemma 4 E4B can safely use up to 104K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/gemma-4-e4b-on-rtx-4090-laptop-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
4.5 GB |
| Medium |
| A76 |
Q4_K_M | 4 | 4.9 GB | Medium | A76 |
Q5_K_M | 5 | 5.8 GB | High | A77 |
Q6_K | 6 | 6.6 GB | High | A78 |
Q8_0Best for your GPU | 8 | 8.6 GB | Very High | A79 |
F16 | 16 | 16.4 GB | Maximum | F0 |
| 14.7B | S | 55.2 tok/s |
| 21B | A | 51.5 tok/s |
| 14B | S | 58 tok/s |