japanese stablelm instruct gamma 7B needs ~6.7 GB but RTX 3050 Ti Laptop 4GB only has 4.0 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
2.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
8.9 tok/s
TTFT
21732 ms
Safe context
4K
Memory
6.7 GB / 4.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 6.7 GB, but this setup only exposes 4.0 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 | 10.2 tok/s | 10376 ms | 4K |
| Coding | F | Too heavy | 8.9 tok/s | 21732 ms | 4K |
| Agentic Coding | F | Too heavy | 7.0 tok/s | 40324 ms | 4K |
| Reasoning | F | Too heavy | 8.9 tok/s | 25684 ms | 4K |
| RAG | F | Too heavy | 7.0 tok/s | 50405 ms | 4K |
How japanese stablelm instruct gamma 7B (7B params) fits at each quantization level on RTX 3050 Ti Laptop 4GB (4.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | F0 |
Q3_K_S | 3 | 3.4 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
GTX 1660 Super 6GBNVIDIA upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 162%.
~$229 MSRP
RTX 3050 8GBBudget pickMakes 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.
~$249 MSRP
RTX 5060 8GBBest valueMakes 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.
~$299 MSRP
No, japanese stablelm instruct gamma 7B requires more memory than RTX 3050 Ti Laptop 4GB provides.
japanese stablelm instruct gamma 7B (7B parameters) requires approximately 6.7 GB of memory with Q4_K_M quantization.
The recommended quantization for japanese stablelm instruct gamma 7B is Q4_K_M, which balances quality and memory efficiency.
On RTX 3050 Ti Laptop 4GB, japanese stablelm instruct gamma 7B achieves approximately 8.9 tokens per second decode speed with a time-to-first-token of 21732ms using Q4_K_M quantization.
For coding workloads, japanese stablelm instruct gamma 7B on RTX 3050 Ti Laptop 4GB receives a F grade with 8.9 tok/s and 4K context.
On RTX 3050 Ti Laptop 4GB, japanese stablelm instruct gamma 7B can safely use up to 4K tokens of context. The model's official context limit is —, 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/hf-thebloke--japanese-stablelm-instruct-gamma-7b-gguf-on-rtx-3050-ti-laptop-4gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| F0 |
Q4_K_M | 4 | 4.3 GB | Medium | F0 |
Q5_K_M | 5 | 5.0 GB | High | F0 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
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.
