aya expanse 8b needs ~6.7 GB VRAM. RTX 4050 Laptop 6GB has 6.0 GB. With Q3_K_S quantization, expect ~20 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
1.6 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
13.0 tok/s
TTFT
14854 ms
Safe context
4K
Memory
7.6 GB / 6.0 GB
Offload
20%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 0.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Very compromised (needs ~0.8 GB host RAM) | 14.9 tok/s | 7088 ms | 4K |
| Coding | F | Too heavy | 13.0 tok/s | 14854 ms | 4K |
| Agentic Coding | F | Too heavy | 10.2 tok/s | 27586 ms | 4K |
| Reasoning | F | Too heavy | 13.0 tok/s | 17555 ms | 4K |
| RAG | F | Too heavy | 10.2 tok/s | 34482 ms | 4K |
How aya expanse 8b (8B params) fits at each quantization level on RTX 4050 Laptop 6GB (6.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_KBest for your GPU | 2 | 3.1 GB | Low | C54 |
Q3_K_S | 3 | 3.9 GB | Low | F0 |
Copy-paste commands to run aya expanse 8b on your machine.
Run
lms load hf-bartowski--aya-expanse-8b-gguf && lms server startUpgrade 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
Yes, RTX 4050 Laptop 6GB can run aya expanse 8b at Q3_K_S quantization (Very compromised (needs ~0.4 GB host RAM)). The recommended Q4_K_M requires 7.6 GB which exceeds available memory, but at Q3_K_S it needs only 6.7 GB. Expected decode speed: 20.0 tok/s.
aya expanse 8b (8B parameters) requires approximately 7.6 GB at Q4_K_M quantization. On RTX 4050 Laptop 6GB, it fits at Q3_K_S using 6.7 GB.
The recommended quantization is Q4_K_M, but on RTX 4050 Laptop 6GB the best fitting quantization is Q3_K_S, which uses 6.7 GB.
On RTX 4050 Laptop 6GB, aya expanse 8b achieves approximately 20.0 tokens per second decode speed with a time-to-first-token of 9662ms using Q3_K_S quantization.
For coding workloads, aya expanse 8b on RTX 4050 Laptop 6GB receives a F grade with 13.0 tok/s and 4K context.
On RTX 4050 Laptop 6GB, aya expanse 8b can safely use up to 5K tokens of context at Q3_K_S quantization. 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-bartowski--aya-expanse-8b-gguf-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>
Preview:
| 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 |
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.