StarCoder 15B needs ~27.4 GB but RTX 2060 Super 8GB only has 8.0 GB. Try a smaller quantization or lighter model.
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
19.4 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.7 tok/s
TTFT
52593 ms
Safe context
4K
Memory
27.4 GB / 8.0 GB
Offload
70%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 27.4 GB, but this setup only exposes 8.0 GB of usable VRAM.
Older PCIe generation
PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | F | Too heavy | 3.7 tok/s | 28687 ms | 4K |
| Coding | F | Too heavy | 3.7 tok/s | 52593 ms | 4K |
| Agentic Coding | F | Too heavy | 3.7 tok/s | 76500 ms | 4K |
| Reasoning | F | Too heavy | 3.7 tok/s | 62156 ms | 4K |
| RAG | F | Too heavy | 3.7 tok/s | 95624 ms | 4K |
How StarCoder 15B (15B params) fits at each quantization level on RTX 2060 Super 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | F0 |
Q3_K_S | 3 | 7.4 GB | Low | F0 |
NVFP4 | 4 | 8.4 GB | Medium | F0 |
Q4_K_M | 4 | 9.2 GB | Medium | F0 |
Q5_K_M | 5 | 10.8 GB | High | F0 |
Q6_K | 6 | 12.3 GB | High | F0 |
Q8_0 | 8 | 16.1 GB | Very High | F0 |
F16 | 16 | 30.7 GB | Maximum | F0 |
アップグレードオプション
RTX 5090 32GBコスパ重視Makes 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.
〜$1,999 MSRP
RTX PRO 4500 Blackwell 32GBコスパ最良Makes 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.
〜$2,499 MSRP
RTX 5000 Ada 32GBNVIDIAアップグレードMakes 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.
〜$4,000 MSRP
No, StarCoder 15B requires more memory than RTX 2060 Super 8GB provides.
StarCoder 15B (15B parameters) requires approximately 27.4 GB of memory with Q5_K_M quantization.
The recommended quantization for StarCoder 15B is Q5_K_M, which balances quality and memory efficiency.
On RTX 2060 Super 8GB, StarCoder 15B achieves approximately 3.7 tokens per second decode speed with a time-to-first-token of 52593ms using Q5_K_M quantization.
For coding workloads, StarCoder 15B on RTX 2060 Super 8GB receives a F grade with 3.7 tok/s and 4K context.
On RTX 2060 Super 8GB, StarCoder 15B can safely use up to 4K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/starcoder-15b-on-rtx-2060-super-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
