Raises estimated decode speed by about 43%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
starcoder2 7b needs ~6.8 GB VRAM. RX 9060 8GB has 8.0 GB. With Q4_K_M quantization, expect ~43 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
Tight fit
Decode
42.5 tok/s
TTFT
4556 ms
Safe context
40K
Memory
6.8 GB / 8.0 GB
This setup is broadly balanced for this model.
No major red flags
This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 42.5 tok/s | 2485 ms | 40K |
| Coding | C | Tight fit | 42.5 tok/s | 4556 ms | 40K |
| Agentic Coding | C | Runs with offload | 42.5 tok/s | 6627 ms | 40K |
| Reasoning | C | Tight fit | 42.5 tok/s | 5385 ms | 40K |
| RAG | C | Runs with offload | 42.5 tok/s | 8284 ms | 40K |
How starcoder2 7b (7B params) fits at each quantization level on RX 9060 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C53 |
Q3_K_S | 3 | 3.4 GB | Low | C53 |
NVFP4 | 4 |
Copy-paste commands to run starcoder2 7b on your machine.
Run
lms load hf-quantfactory--starcoder2-7b-gguf && lms server startUpgrade options
Raises estimated decode speed by about 43%.
Adds memory headroom for longer context windows and future model growth.
~$449 MSRP
Adds memory headroom for longer context windows and future model growth.
~$479 MSRP
Raises estimated decode speed by about 119%.
Adds memory headroom for longer context windows and future model growth.
~$479 MSRP
Yes, RX 9060 8GB can run starcoder2 7b with a C grade (Tight fit). Expected decode speed: 42.5 tok/s.
starcoder2 7b (7B parameters) requires approximately 6.8 GB of memory with Q4_K_M quantization.
The recommended quantization for starcoder2 7b is Q4_K_M, which balances quality and memory efficiency.
On RX 9060 8GB, starcoder2 7b achieves approximately 42.5 tokens per second decode speed with a time-to-first-token of 4556ms using Q4_K_M quantization.
For coding workloads, starcoder2 7b on RX 9060 8GB receives a C grade with 42.5 tok/s and 40K context.
On RX 9060 8GB, starcoder2 7b can safely use up to 40K 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-quantfactory--starcoder2-7b-gguf-on-rx-9060-8gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| C53 |
Q4_K_M | 4 | 4.3 GB | Medium | C53 |
Q5_K_MBest for your GPU | 5 | 5.0 GB | High | C52 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |