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. GTX 1060 6GB has 6.0 GB. With Q4_K_M quantization, expect ~31 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
31.0 tok/s
TTFT
6255 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.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
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 | 31.0 tok/s | 3412 ms | 19K |
| Coding | C | Runs with offload | 31.0 tok/s | 6255 ms | 19K |
| Agentic Coding | D | Very compromised (needs ~0.3 GB host RAM) | 18.5 tok/s | 15205 ms | 19K |
| Reasoning | C | Runs with offload | 31.0 tok/s | 7392 ms | 19K |
| RAG | D | Very compromised (needs ~0.3 GB host RAM) | 18.5 tok/s | 19007 ms |
How stablelm 2 1 6b chat imatrix (6B params) fits at each quantization level on GTX 1060 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 141%.
Adds memory headroom for longer context windows and future model growth.
~$299 MSRP
RTX 5050 8GBNVIDIA upgradeRaises estimated decode speed by about 66%.
Adds memory headroom for longer context windows and future model growth.
~$299 MSRP
Yes, GTX 1060 6GB can run stablelm 2 1 6b chat imatrix with a C grade (Runs with offload). Expected decode speed: 31.0 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 GTX 1060 6GB, stablelm 2 1 6b chat imatrix achieves approximately 31.0 tokens per second decode speed with a time-to-first-token of 6255ms using Q4_K_M quantization.
For coding workloads, stablelm 2 1 6b chat imatrix on GTX 1060 6GB receives a C grade with 31.0 tok/s and 19K context.
On GTX 1060 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-gtx-1060-6gb" 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.