InternLM Chat 7B needs ~14.1 GB but RTX 5060 Ti 8GB only has 8.0 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
6.1 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
15.7 tok/s
TTFT
12336 ms
Safe context
4K
Memory
14.1 GB / 8.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 14.1 GB, but this setup only exposes 8.0 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 | 30.4 tok/s | 3468 ms | 4K |
| Coding | F | Too heavy | 15.7 tok/s | 12336 ms | 4K |
| Agentic Coding | F | Too heavy | 9.8 tok/s | 28860 ms | 4K |
| Reasoning | F | Too heavy | 15.7 tok/s | 14579 ms | 4K |
| RAG | F | Too heavy | 9.8 tok/s | 36075 ms | 4K |
How InternLM Chat 7B (7B params) fits at each quantization level on RTX 5060 Ti 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | A74 |
Q3_K_S | 3 | 3.4 GB | Low | A74 |
NVFP4 | 4 | 3.9 GB | Medium | A74 |
Q4_K_M | 4 | 4.3 GB | Medium | A74 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | A73 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
アップグレードオプション
RTX 5060 Ti 16GBコスパ重視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.
〜$449 MSRP
RTX 4060 Ti 16GBコスパ最良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.
〜$499 MSRP
RTX 2000 Ada 16GBNVIDIAアップグレード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.
〜$625 MSRP
No, InternLM Chat 7B requires more memory than RTX 5060 Ti 8GB provides.
InternLM Chat 7B (7B parameters) requires approximately 14.1 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 5060 Ti 8GB, InternLM Chat 7B achieves approximately 15.7 tokens per second decode speed with a time-to-first-token of 12336ms using Q4_K_M quantization.
For coding workloads, InternLM Chat 7B on RTX 5060 Ti 8GB receives a F grade with 15.7 tok/s and 4K context.
On RTX 5060 Ti 8GB, 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.
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.
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<iframe src="https://willitrunai.com/embed/internlm-chat-7b-on-rtx-5060-ti-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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