Meta Llama 3 8B Instruct needs ~7.4 GB but RTX 3050 Ti Laptop 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
6.3 tok/s
TTFT
30862 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.
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 | 7.2 tok/s | 14672 ms | 4K |
| Coding | F | Too heavy | 6.3 tok/s | 30862 ms | 4K |
| Agentic Coding | F | Too heavy | 4.9 tok/s | 57671 ms | 4K |
| Reasoning | F | Too heavy | 6.3 tok/s | 36473 ms | 4K |
| RAG | F | Too heavy | 4.9 tok/s | 72089 ms | 4K |
How Meta Llama 3 8B Instruct (8B params) fits at each quantization level on RTX 3050 Ti Laptop 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 | 4.5 GB | 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 |
Upgrade-Optionen
RTX 3050 8GBGünstige 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. $249 MSRP
RTX 5060 8GBBestes 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. $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.
ca. $299 MSRP
No, Meta Llama 3 8B Instruct requires more memory than RTX 3050 Ti Laptop 4GB provides.
Meta Llama 3 8B Instruct (8B parameters) requires approximately 7.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Meta Llama 3 8B Instruct is Q4_K_M, which balances quality and memory efficiency.
On RTX 3050 Ti Laptop 4GB, Meta Llama 3 8B Instruct achieves approximately 6.3 tokens per second decode speed with a time-to-first-token of 30862ms using Q4_K_M quantization.
For coding workloads, Meta Llama 3 8B Instruct on RTX 3050 Ti Laptop 4GB receives a F grade with 6.3 tok/s and 4K context.
On RTX 3050 Ti Laptop 4GB, Meta Llama 3 8B Instruct can safely use up to 4K tokens of context. The model's official context limit is —, 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/hf-maziyarpanahi--meta-llama-3-8b-instruct-gguf-on-rtx-3050-ti-laptop-4gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
