Model × hardware check
Can Gemma 4 E4B run on NVIDIA GeForce RTX 4060 Ti · 16 GB?
Yes, this is a comfortable estimate at 8,192 tokens. an efficient Gemma variant whose stored embedding tables are larger than its effective parameter count suggests The RTX 4060 Ti 16GB has more capacity than the 3060 but a narrower 288 GB/s memory path, making it a useful capacity-versus-speed comparison.
Short answer
Comfortable fit
At least 20% memory headroom remains after weights, KV cache, and runtime overhead.
- Available VRAM
- 16 GB
- Estimated requirement
- 11.0 GB
- Headroom
- +5.0 GB
- Fit ratio
- 146%
- Recommended quantization
- Q8_0
- Context ceiling
- 131,072 tokens
- Speed estimate
- Estimate · Fast relative band
What the numbers say
The selected Gemma 4 E4B package uses 9.2 GB of weight files. The 8K KV cache estimate is 0.3 GB, and the formula adds 1.5 GB of runtime overhead. That produces a 11.0 GB requirement against 16 GB of VRAM.
The 16 GB version gives compact models more room, but its narrower memory bus can be a speed constraint compared with higher-bandwidth cards.
The context ceiling shown above is the point where the same formula reaches the machine's capacity. It is not a promise that the backend can allocate that full context after the operating system and other applications take their share.
Quantization trade-offs at 8K
| Quantization | Weight files | 8K requirement | Fit on this machine | Context ceiling | Speed estimate | Ollama |
|---|---|---|---|---|---|---|
| Q4_K_M | 5.97 GB | 7.7 GB | Comfortable fit · 207% | 131,072 tokens | Estimate · Fast relative class | ollama run gemma4:e4b-it-q4_K_M |
| Q4_0 | 6.15 GB | 7.9 GB | Comfortable fit · 202% | 131,072 tokens | Estimate · Fast relative class | ollama run gemma4:e4b-it-qat |
| Q8_0 | 9.19 GB | 11.0 GB | Comfortable fit · 146% | 131,072 tokens | Estimate · Fast relative class | ollama run gemma4:e4b-it-q8_0 |
Run commands
ollama run gemma4:e4b-it-q8_0lms get "Gemma 4 E4B"@q8_0
lms load <model-key-from-lms-ls> --gpu max --context-length 8192llama-cli -hf google/gemma-4-E4B-it -c 8192 -ngl 99 -p "Your prompt"mlx_lm.generate --model google/gemma-4-E4B-it --prompt "Your prompt" --max-tokens 8192Commands are examples, not a guarantee that the selected quantization tag is available in every runtime. Confirm the package name and license on the linked model page.
If it does not fit
This pairing already clears the comfort threshold
The current estimate leaves +5.0 GB. A longer context or another GPU application can reduce that margin, so verify free memory before relying on it.
Low-cost setup upgrades
Useful upgrades around NVIDIA GeForce RTX 4060 Ti · 16 GB
These are optional setup improvements, not a claim that an accessory can increase GPU or unified-memory capacity.
External NVMe SSD for model storage
Keeps large GGUF and MLX files off the boot drive. Fast storage reduces load time, but it does not increase the memory available for inference.
Search current listings on AmazonDesktop RAM upgrade kit
Useful for MoE CPU offload and keeping the operating system out of VRAM. Match the motherboard's memory type, slot layout, capacity limit, and validated part numbers.
Search current listings on AmazonGPU support bracket
A lower-cost mechanical upgrade for heavy three-slot cards. Check clearance, mounting height, and whether the bracket blocks fans or case panels.
Search current listings on AmazonAs an Amazon Associate I earn from qualifying purchases. Links are sponsored search links and do not change the price you pay. Affiliate disclosure.
Where this estimate can break
- The download is closer to an 8B model than a 4.5B model because the embedding tables are still stored.
- Vision and audio support require the multimodal projector files.
- Other GPU applications, display buffers, and the operating system reduce usable memory.
- Longer prompts, concurrent requests, and a different backend can raise peak memory.
Compare this pairing
FAQ
Questions about this pairing.
How much memory does Gemma 4 E4B need on NVIDIA GeForce RTX 4060 Ti · 16 GB?
At an 8K context with Q8_0, the estimate is 11.0 GB: 9.2 GB of weights, 0.3 GB of KV cache, and 1.5 GB of runtime overhead. The available VRAM is 16 GB.
What context length can NVIDIA GeForce RTX 4060 Ti · 16 GB handle with this model?
The calculated ceiling is 131,072 tokens for this quantization, capped by the model's native 131,072-token context. Actual usable context can be lower after the OS, other applications, and backend workspace.
How does quantization change the memory requirement on NVIDIA GeForce RTX 4060 Ti · 16 GB?
At 8,192 tokens, the published variants calculate as follows: Q4_K_M: 7.7 GB, comfortable fit; Q4_0: 7.9 GB, comfortable fit; Q8_0: 11.0 GB, comfortable fit. A lower-bit file can move a result from too large to tight, but quality and kernel support may differ.
What can I run if Gemma 4 E4B does not fit on NVIDIA GeForce RTX 4060 Ti · 16 GB?
This selection already clears the comfort threshold. If you later increase the context, monitor the ceiling of 131,072 tokens; smaller alternatives include Gemma 4 12B Unified, Qwen3 8B, DeepSeek R1 8B.
Is the speed estimate for Gemma 4 E4B on NVIDIA GeForce RTX 4060 Ti · 16 GB measured?
No. Fast relative band is a heuristic band derived from 4.5B active parameters and 288 GB/s published memory bandwidth. Backend kernels, batch size, thermals, and CPU offload can change actual tokens per second.