OpenHermes 2.5 7B needs ~7.8 GB but GTX 1650 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
3.8 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.7 tok/s
TTFT
71864 ms
Safe context
4K
Memory
7.8 GB / 4.0 GB
Offload
50%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 7.8 GB, but this setup only exposes 4.0 GB of usable VRAM.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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 | 3.4 tok/s | 31442 ms | 4K |
| Coding | F | Too heavy | 2.7 tok/s | 71864 ms | 4K |
| Agentic Coding | F | Too heavy | 2.4 tok/s | 116515 ms | 4K |
| Reasoning | F | Too heavy | 2.7 tok/s | 84930 ms | 4K |
| RAG | F | Too heavy | 2.4 tok/s | 145643 ms | 4K |
How OpenHermes 2.5 7B (7B params) fits at each quantization level on GTX 1650 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 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
RTX 4060 8GBNVIDIA 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.
~$299 MSRP
No, OpenHermes 2.5 7B requires more memory than GTX 1650 4GB provides.
OpenHermes 2.5 7B (7B parameters) requires approximately 7.8 GB of memory with Q4_K_M quantization.
The recommended quantization for OpenHermes 2.5 7B is Q4_K_M, which balances quality and memory efficiency.
On GTX 1650 4GB, OpenHermes 2.5 7B achieves approximately 2.7 tokens per second decode speed with a time-to-first-token of 71864ms using Q4_K_M quantization.
For coding workloads, OpenHermes 2.5 7B on GTX 1650 4GB receives a F grade with 2.7 tok/s and 4K context.
On GTX 1650 4GB, OpenHermes 2.5 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/openhermes-2.5-7b-on-gtx-1650-4gb" 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.
