InternLM 7B needs ~14.1 GB but RTX 3070 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
16.8 tok/s
TTFT
11557 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 | 33.2 tok/s | 3181 ms | 4K |
| Coding | F | Too heavy | 16.8 tok/s | 11557 ms | 4K |
| Agentic Coding | F | Too heavy | 11.0 tok/s | 25562 ms | 4K |
| Reasoning | F | Too heavy | 16.8 tok/s | 13659 ms | 4K |
| RAG | F | Too heavy | 11.0 tok/s | 31952 ms | 4K |
How InternLM 7B (7B params) fits at each quantization level on RTX 3070 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 |
Upgrade-Optionen
RTX 5060 Ti 16GBGünstige WahlMakes 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.
ca. $449 MSRP
RTX 4060 Ti 16GBBestes Preis-Leistungs-VerhältnisMakes 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.
ca. $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.
ca. $625 MSRP
No, InternLM 7B requires more memory than RTX 3070 8GB provides.
InternLM 7B (7B parameters) requires approximately 14.1 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 3070 8GB, InternLM 7B achieves approximately 16.8 tokens per second decode speed with a time-to-first-token of 11557ms using Q4_K_M quantization.
For coding workloads, InternLM 7B on RTX 3070 8GB receives a F grade with 16.8 tok/s and 4K context.
On RTX 3070 8GB, 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.
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/internlm-7b-on-rtx-3070-8gb" 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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