vntl llama3 8b v2 needs ~7.4 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
3.4 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
2.5 tok/s
TTFT
78548 ms
Safe context
4K
Memory
7.4 GB / 4.0 GB
Offload
50%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 7.4 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 | 2.8 tok/s | 37122 ms | 4K |
| Coding | F | Too heavy | 2.5 tok/s | 78548 ms | 4K |
| Agentic Coding | F | Too heavy | 2.0 tok/s | 140800 ms | 4K |
| Reasoning | F | Too heavy | 2.5 tok/s | 92829 ms | 4K |
| RAG | F | Too heavy | 2.0 tok/s | 176000 ms | 4K |
How vntl llama3 8b v2 (8B params) fits at each quantization level on GTX 1650 4GB (4.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 3.1 GB | Low | F0 |
Q3_K_S | 3 | 3.9 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, vntl llama3 8b v2 requires more memory than GTX 1650 4GB provides.
vntl llama3 8b v2 (8B parameters) requires approximately 7.4 GB of memory with Q4_K_M quantization.
The recommended quantization for vntl llama3 8b v2 is Q4_K_M, which balances quality and memory efficiency.
On GTX 1650 4GB, vntl llama3 8b v2 achieves approximately 2.5 tokens per second decode speed with a time-to-first-token of 78548ms using Q4_K_M quantization.
For coding workloads, vntl llama3 8b v2 on GTX 1650 4GB receives a F grade with 2.5 tok/s and 4K context.
On GTX 1650 4GB, vntl llama3 8b v2 can safely use up to 4K tokens of context. The model's official context limit is —, 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/hf-lmg-anon--vntl-llama3-8b-v2-gguf-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 | 4.9 GB | Medium | F0 |
Q5_K_M | 5 | 5.8 GB | High | F0 |
Q6_K | 6 | 6.6 GB | High | F0 |
Q8_0 | 8 | 8.6 GB | Very High | F0 |
F16 | 16 | 16.4 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.
