RTX 3050 8GBBudget pick8 GB VRAM (+2)224 GB/s (+32)
C
Adds memory headroom for longer context windows and future model growth.33.9 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$249 MSRP
stablelm 2 1 6b chat imatrix needs ~5.9 GB VRAM. RTX 4050 Laptop 6GB has 6.0 GB. With Q4_K_M quantization, expect ~38 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
Fit status
Runs with offload
Decode
35.2 tok/s
TTFT
5495 ms
Safe context
19K
Memory
5.9 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 | 35.2 tok/s | 2997 ms | 19K |
| Coding | C | Runs with offload | 38.3 tok/s | 5055 ms | 19K |
| Agentic Coding | D | Very compromised (needs ~0.3 GB host RAM) | 21.9 tok/s | 12885 ms | 19K |
| Reasoning | C | Runs with offload | 35.2 tok/s | 6494 ms | 19K |
| RAG | D | Very compromised (needs ~0.3 GB host RAM) | 21.9 tok/s | 16106 ms |
How stablelm 2 1 6b chat imatrix (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 |
Copy-paste commands to run stablelm 2 1 6b chat imatrix on your machine.
Run
lms load hf-crataco--stablelm-2-1-6b-chat-imatrix-gguf && lms server startUpgrade options
RTX 3050 8GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$249 MSRP
RTX 5060 8GBBest valueRaises estimated decode speed by about 112%.
Adds memory headroom for longer context windows and future model growth.
~$299 MSRP
RTX 5050 8GBNVIDIA upgradeRaises estimated decode speed by about 46%.
Adds memory headroom for longer context windows and future model growth.
~$299 MSRP
Yes, RTX 4050 Laptop 6GB can run stablelm 2 1 6b chat imatrix with a C grade (Runs with offload). Expected decode speed: 38.3 tok/s.
stablelm 2 1 6b chat imatrix (6B parameters) requires approximately 5.9 GB of memory with Q4_K_M quantization.
The recommended quantization for stablelm 2 1 6b chat imatrix is Q4_K_M, which balances quality and memory efficiency.
On RTX 4050 Laptop 6GB, stablelm 2 1 6b chat imatrix achieves approximately 38.3 tokens per second decode speed with a time-to-first-token of 5055ms using Q4_K_M quantization.
For coding workloads, stablelm 2 1 6b chat imatrix on RTX 4050 Laptop 6GB receives a C grade with 38.3 tok/s and 19K context.
On RTX 4050 Laptop 6GB, stablelm 2 1 6b chat imatrix can safely use up to 19K 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-crataco--stablelm-2-1-6b-chat-imatrix-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:
| 19K |
| 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 |
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.