RTX 3050 8GBBudget pick8 GB VRAM (+2)
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 zephyr 1 6b needs ~5.9 GB VRAM. RTX 2060 6GB has 6.0 GB. With Q4_K_M quantization, expect ~52 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
52.3 tok/s
TTFT
3700 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 | 52.3 tok/s | 2018 ms | 19K |
| Coding | C | Runs with offload | 52.3 tok/s | 3700 ms | 19K |
| Agentic Coding | C | Very compromised | 31.3 tok/s | 8994 ms | 19K |
| Reasoning | C | Runs with offload | 52.3 tok/s | 4372 ms | 19K |
| RAG | C | Very compromised (needs ~0.3 GB host RAM) | 31.3 tok/s | 11242 ms | 19K |
How stablelm 2 zephyr 1 6b (6B params) fits at each quantization level on RTX 2060 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 zephyr 1 6b on your machine.
Run
lms load hf-stabilityai--stablelm-2-zephyr-1-6b && 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 43%.
Adds memory headroom for longer context windows and future model growth.
~$299 MSRP
RTX 5050 8GBNVIDIA upgradeAdds memory headroom for longer context windows and future model growth.
~$299 MSRP
Yes, RTX 2060 6GB can run stablelm 2 zephyr 1 6b with a C grade (Runs with offload). Expected decode speed: 52.3 tok/s.
stablelm 2 zephyr 1 6b (6B parameters) requires approximately 5.9 GB of memory with Q4_K_M quantization.
The recommended quantization for stablelm 2 zephyr 1 6b is Q4_K_M, which balances quality and memory efficiency.
On RTX 2060 6GB, stablelm 2 zephyr 1 6b achieves approximately 52.3 tokens per second decode speed with a time-to-first-token of 3700ms using Q4_K_M quantization.
For coding workloads, stablelm 2 zephyr 1 6b on RTX 2060 6GB receives a C grade with 52.3 tok/s and 19K context.
On RTX 2060 6GB, stablelm 2 zephyr 1 6b 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-stabilityai--stablelm-2-zephyr-1-6b-on-rtx-2060-6gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| 4 |
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.