InternLM Chat 7B needs ~13.9 GB but RTX 4050 Laptop 6GB only has 6.0 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
7.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
4.9 tok/s
TTFT
39320 ms
Safe context
4K
Memory
13.9 GB / 6.0 GB
Offload
60%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 13.9 GB, but this setup only exposes 6.0 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 | 8.4 tok/s | 12509 ms | 4K |
| Coding | F | Too heavy | 4.9 tok/s | 39320 ms | 4K |
| Agentic Coding | F | Too heavy | 4.9 tok/s | 57193 ms | 4K |
| Reasoning | F | Too heavy | 4.9 tok/s | 46470 ms | 4K |
| RAG | F | Too heavy | 4.9 tok/s | 71492 ms | 4K |
How InternLM Chat 7B (7B params) fits at each quantization level on RTX 4050 Laptop 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | A75 |
Q3_K_SBest for your GPU | 3 | 3.4 GB | Low | A74 |
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 5070 Ti 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.
~$749 MSRP
No, InternLM Chat 7B requires more memory than RTX 4050 Laptop 6GB provides.
InternLM Chat 7B (7B parameters) requires approximately 13.9 GB of memory with Q4_K_M quantization.
The recommended quantization for InternLM Chat 7B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4050 Laptop 6GB, InternLM Chat 7B achieves approximately 4.9 tokens per second decode speed with a time-to-first-token of 39320ms using Q4_K_M quantization.
For coding workloads, InternLM Chat 7B on RTX 4050 Laptop 6GB receives a F grade with 4.9 tok/s and 4K context.
On RTX 4050 Laptop 6GB, InternLM Chat 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-chat-7b-on-rtx-4050-laptop-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4 |
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.
