RTX 3050 8GBBudget pick8 GB VRAM (+2)224 GB/s (+32)
C
Raises estimated decode speed by about 35%.36.9 tok/s decode
Raises estimated decode speed by about 35%.
Adds memory headroom for longer context windows and future model growth.
~$249 MSRP
Yi 1.5 6B needs ~6.1 GB VRAM. RTX 4050 Laptop 6GB has 6.0 GB. With Q4_K_M quantization, expect ~27 tok/s.
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
100 MB over capacity — needs offload or smaller quantization
Fit status
Runs with offload (needs ~0.1 GB host RAM)
Decode
27.4 tok/s
TTFT
7064 ms
Safe context
4K
Memory
6.1 GB / 6.0 GB
This setup is broadly balanced for this model.
Very little memory headroom
You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Tight fit | 38.3 tok/s | 2756 ms | 4K |
| Coding | C | Runs with offload (needs ~0.1 GB host RAM) | 27.4 tok/s | 7064 ms | 4K |
| Agentic Coding | D | Very compromised (needs ~0.6 GB host RAM) | 20.1 tok/s | 14021 ms | 4K |
| Reasoning | C | Runs with offload (needs ~0.1 GB host RAM) | 27.4 tok/s | 8349 ms | 4K |
| RAG | D | Very compromised (needs ~0.6 GB host RAM) | 20.1 tok/s | 17527 ms | 4K |
How Yi 1.5 6B (6B params) fits at each quantization level on RTX 4050 Laptop 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.3 GB | Low | C54 |
Q3_K_S | 3 | 2.9 GB | Low | C54 |
NVFP4Best for your GPU | 4 | 3.4 GB | Medium | C54 |
Q4_K_M | 4 | 3.7 GB | Medium | F0 |
Q5_K_M | 5 | 4.3 GB | High | F0 |
Q6_K | 6 | 4.9 GB | High | F0 |
Q8_0 | 8 | 6.4 GB | Very High | F0 |
F16 | 16 | 12.3 GB | Maximum | F0 |
Copy-paste commands to run Yi 1.5 6B on your machine.
Run
lms load Yi-1.5-6B-Chat && lms server startUpgrade options
RTX 3050 8GBBudget pickRaises estimated decode speed by about 35%.
Adds memory headroom for longer context windows and future model growth.
~$249 MSRP
RTX 5060 8GBBest valueRaises estimated decode speed by about 196%.
Adds memory headroom for longer context windows and future model growth.
~$299 MSRP
RTX 5050 8GBNVIDIA upgradeRaises estimated decode speed by about 104%.
Adds memory headroom for longer context windows and future model growth.
~$299 MSRP
Yes, RTX 4050 Laptop 6GB can run Yi 1.5 6B with a C grade (Runs with offload (needs ~0.1 GB host RAM)). Expected decode speed: 27.4 tok/s.
Yi 1.5 6B (6B parameters) requires approximately 6.1 GB of memory with Q4_K_M quantization.
The recommended quantization for Yi 1.5 6B is Q4_K_M, which balances quality and memory efficiency.
On RTX 4050 Laptop 6GB, Yi 1.5 6B achieves approximately 27.4 tokens per second decode speed with a time-to-first-token of 7064ms using Q4_K_M quantization.
For coding workloads, Yi 1.5 6B on RTX 4050 Laptop 6GB receives a C grade with 27.4 tok/s and 4K context.
On RTX 4050 Laptop 6GB, Yi 1.5 6B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/yi-1.5-6b-on-rtx-4050-laptop-6gb" 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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