InternLM 7B needs ~13.7 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
9.7 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
5.3 tok/s
TTFT
36800 ms
Safe context
4K
Memory
13.7 GB / 4.0 GB
Offload
70%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 13.7 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 | 5.3 tok/s | 20073 ms | 4K |
| Coding | F | Too heavy | 5.3 tok/s | 36800 ms | 4K |
| Agentic Coding | F | Too heavy | 5.3 tok/s | 53527 ms | 4K |
| Reasoning | F | Too heavy | 5.3 tok/s | 43491 ms | 4K |
| RAG | F | Too heavy | 5.3 tok/s | 66909 ms | 4K |
How InternLM 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
RTX 5060 Ti 16GBBudget 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.
~$449 MSRP
RTX 4060 Ti 16GBBest 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.
~$499 MSRP
RTX 2000 Ada 16GBNVIDIA upgradeMakes 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.
~$625 MSRP
No, InternLM 7B requires more memory than RTX 3050 Ti Laptop 4GB provides.
InternLM 7B (7B parameters) requires approximately 13.7 GB of memory with Q4_K_M quantization.
The recommended quantization for InternLM 7B is Q4_K_M, which balances quality and memory efficiency.
On RTX 3050 Ti Laptop 4GB, InternLM 7B achieves approximately 5.3 tokens per second decode speed with a time-to-first-token of 36800ms using Q4_K_M quantization.
For coding workloads, InternLM 7B on RTX 3050 Ti Laptop 4GB receives a F grade with 5.3 tok/s and 4K context.
On RTX 3050 Ti Laptop 4GB, InternLM 7B can safely use up to 4K tokens of context. The model's official context limit is 8K, 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/internlm-7b-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:
| 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.
