RTX 4000 Ada 20GBBudget pick20 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.29 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,250 MSRP
StarCoder2 15B needs ~14.8 GB VRAM. RTX 5070 Ti 16GB has 16.0 GB. With Q5_K_M quantization, expect ~59 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
59.1 tok/s
TTFT
3275 ms
Safe context
16K
Memory
14.8 GB / 16.0 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.
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 | 59.1 tok/s | 1786 ms | 16K |
| Coding | C | Tight fit | 59.1 tok/s | 3275 ms | 16K |
| Agentic Coding | C | Runs with offload (needs ~0 GB host RAM) | 45.0 tok/s | 6261 ms | 16K |
| Reasoning | C | Tight fit | 59.1 tok/s | 3870 ms | 16K |
| RAG | C | Runs with offload (needs ~0 GB host RAM) | 45.0 tok/s | 7827 ms | 16K |
How StarCoder2 15B (15B params) fits at each quantization level on RTX 5070 Ti 16GB (16.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C51 |
Q3_K_S | 3 | 7.4 GB | Low | C53 |
NVFP4 | 4 | 8.4 GB | Medium | C53 |
Q4_K_M | 4 | 9.2 GB | Medium | C53 |
Q5_K_M | 5 | 10.8 GB | High | C52 |
Q6_KBest for your GPU | 6 | 12.3 GB | High | C52 |
Q8_0 | 8 | 16.1 GB | Very High | F0 |
F16 | 16 | 30.7 GB | Maximum | F0 |
Copy-paste commands to run StarCoder2 15B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "bigcode/starcoder2-15b" \
--hf-file "starcoder2-15b-Q5_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
RTX 4000 Ada 20GBBudget pickAdds memory headroom for longer context windows and future model growth.
~$1,250 MSRP
RTX 3090 24GBBest valueAdds memory headroom for longer context windows and future model growth.
~$1,499 MSRP
RTX 4090 24GBNVIDIA upgradeRaises estimated decode speed by about 34%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Yes, RTX 5070 Ti 16GB can run StarCoder2 15B with a C grade (Tight fit). Expected decode speed: 59.1 tok/s.
StarCoder2 15B (15B parameters) requires approximately 14.8 GB of memory with Q5_K_M quantization.
The recommended quantization for StarCoder2 15B is Q5_K_M, which balances quality and memory efficiency.
On RTX 5070 Ti 16GB, StarCoder2 15B achieves approximately 59.1 tokens per second decode speed with a time-to-first-token of 3275ms using Q5_K_M quantization.
For coding workloads, StarCoder2 15B on RTX 5070 Ti 16GB receives a C grade with 59.1 tok/s and 16K context.
On RTX 5070 Ti 16GB, StarCoder2 15B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.
Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/starcoder2-15b-on-rtx-5070-ti-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview: