Raises estimated decode speed by about 80%.
Adds memory headroom for longer context windows and future model growth.
~$1,250 MSRP
StarCoder2 7B needs ~7.9 GB VRAM. NVIDIA A2 16GB has 16.0 GB. With Q4_K_M quantization, expect ~37 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 well
Decode
36.5 tok/s
TTFT
5299 ms
Safe context
174K
Memory
7.9 GB / 16.0 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 | 36.5 tok/s | 2890 ms | 174K |
| Coding | C | Runs well | 36.5 tok/s | 5299 ms | 174K |
| Agentic Coding | C | Runs well | 36.5 tok/s | 7708 ms | 174K |
| Reasoning | C | Runs well | 36.5 tok/s | 6263 ms | 174K |
| RAG | C | Runs well | 36.5 tok/s | 9635 ms | 174K |
How StarCoder2 7B (7B params) fits at each quantization level on NVIDIA A2 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | C47 |
Q3_K_S | 3 | 3.4 GB | Low | C47 |
NVFP4 | 4 |
Copy-paste commands to run StarCoder2 7B on your machine.
Run
lms load hf-second-state--starcoder2-7b-gguf && lms server startUpgrade options
Raises estimated decode speed by about 80%.
Adds memory headroom for longer context windows and future model growth.
~$1,250 MSRP
Raises estimated decode speed by about 168%.
Adds memory headroom for longer context windows and future model growth.
~$1,499 MSRP
Raises estimated decode speed by about 168%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Yes, NVIDIA A2 16GB can run StarCoder2 7B with a C grade (Runs well). Expected decode speed: 36.5 tok/s.
StarCoder2 7B (7B parameters) requires approximately 7.9 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 NVIDIA A2 16GB, StarCoder2 7B achieves approximately 36.5 tokens per second decode speed with a time-to-first-token of 5299ms using Q4_K_M quantization.
For coding workloads, StarCoder2 7B on NVIDIA A2 16GB receives a C grade with 36.5 tok/s and 174K context.
On NVIDIA A2 16GB, StarCoder2 7B can safely use up to 174K 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-second-state--starcoder2-7b-gguf-on-a2-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| C48 |
Q4_K_M | 4 | 4.3 GB | Medium | C48 |
Q5_K_M | 5 | 5.0 GB | High | C49 |
Q6_K | 6 | 5.7 GB | High | C49 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | C51 |
F16 | 16 | 14.3 GB | Maximum | F0 |