Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 1012%.
~$1,999 MSRP
Gemma 4 31B needs ~37.0 GB but NVIDIA L4 24GB only has 24.0 GB. Try a smaller quantization or lighter model.
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
13.0 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.3 tok/s
TTFT
58641 ms
Safe context
4K
Memory
37.0 GB / 24.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 37.0 GB, but this setup only exposes 24.0 GB of usable VRAM.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 5.0 tok/s | 21103 ms | 4K |
| Coding | F | Too heavy | 3.1 tok/s | 61573 ms | 4K |
| Agentic Coding | F | Too heavy | 2.0 tok/s | 140800 ms | 4K |
| Reasoning | F | Too heavy | 3.1 tok/s | 72768 ms | 4K |
| RAG | F | Too heavy | 2.0 tok/s | 176000 ms | 4K |
How Gemma 4 31B (30.700000762939453B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 12.0 GB | Low | S87 |
Q3_K_S | 3 | 15.0 GB | Low | S87 |
NVFP4 | 4 |
Upgrade options
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 1012%.
~$1,999 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 597%.
~$2,499 MSRP
Makes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$4,650 MSRP
No, Gemma 4 31B requires more memory than NVIDIA L4 24GB provides.
Gemma 4 31B (30.700000762939453B parameters) requires approximately 37.0 GB of memory with Q4_K_M quantization.
The recommended quantization for Gemma 4 31B is Q4_K_M, which balances quality and memory efficiency.
On NVIDIA L4 24GB, Gemma 4 31B achieves approximately 3.1 tokens per second decode speed with a time-to-first-token of 61573ms using Q4_K_M quantization.
For coding workloads, Gemma 4 31B on NVIDIA L4 24GB receives a F grade with 3.1 tok/s and 4K context.
On NVIDIA L4 24GB, Gemma 4 31B can safely use up to 4K tokens of context. The model's official context limit is 256K, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/gemma-4-31b-on-l4-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
17.2 GB |
| Medium |
| S86 |
Q4_K_MBest for your GPU | 4 | 18.7 GB | Medium | S86 |
Q5_K_M | 5 | 22.1 GB | High | F0 |
Q6_K | 6 | 25.2 GB | High | F0 |
Q8_0 | 8 | 32.8 GB | Very High | F0 |
F16 | 16 | 62.9 GB | Maximum | F0 |