starcoder2 15b instruct v0.1 needs ~12.9 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q4_K_M quantization, expect ~16 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
1.9 GB over capacity — needs offload or smaller quantization
Fit status
Very compromised (needs ~1.4 GB host RAM)
Decode
16.0 tok/s
TTFT
12083 ms
Safe context
4K
Memory
12.9 GB / 11.0 GB
Offload
10%
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.
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.
Remove offload with more accelerator memory
Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
Buy headroom, not only minimum fit
A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.
Increase host RAM if you keep offloading
This setup may need roughly 1.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Very compromised (needs ~0.8 GB host RAM) | 18.7 tok/s | 5645 ms | 4K |
| Coding | D | Very compromised (needs ~1.4 GB host RAM) | 16.0 tok/s | 12083 ms | 4K |
| Agentic Coding | F | Too heavy | 12.1 tok/s | 23264 ms | 4K |
| Reasoning | D | Very compromised (needs ~1.4 GB host RAM) | 16.0 tok/s | 14280 ms | 4K |
| RAG | F | Too heavy | 12.1 tok/s | 29080 ms | 4K |
Inference speed
Estimated decode speed (tokens/sec) for starcoder2 15b instruct v0.1 at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~131 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.
| GPU / Mac | Memory | Quant | Speed (tok/s) | Fits? |
|---|---|---|---|---|
 RTX 5090 32GB | 32 GB | Q4_K_M | 131.2 | Fits |
| RTX 4090 24GB | 24 GB | Q4_K_M | 83.7 | Fits |
RX 7900 XTX 24GB | 24 GB | Q4_K_M | 75.5 | Fits |
| RTX 3090 24GB | 24 GB | Q4_K_M | 71.6 | Fits |
| RTX 4080 Super 16GB | 16 GB | Q4_K_M | 68.3 | Tight |
Mac Studio M3 Ultra 256GB | 256 GB | Q4_K_M | 60.9 | Fits |
Mac Studio M2 Ultra 128GB | 128 GB | Q4_K_M | 50.7 | Fits |
Mac Studio M1 Ultra 128GB | 128 GB | Q4_K_M | 48.1 | Fits |
MacBook Pro M4 Max 128GB | 128 GB | Q4_K_M | 34.7 | Fits |
MacBook Pro M4 Max 64GB | 64 GB | Q4_K_M | 34.7 | Fits |
| RTX 4070 12GB | 12 GB | Q4_K_M | 26.7 | Heavy offload |
MacBook Pro M3 Max 64GB | 64 GB | Q4_K_M | 26.2 | Fits |
MacBook Pro M1 Max 64GB | 64 GB | Q4_K_M | 24.0 | Fits |
MacBook Pro M4 Pro 48GB | 48 GB | Q4_K_M | 21.2 | Fits |
| RTX 3060 12GB | 12 GB | Q4_K_M | 15.7 | Heavy offload |
| RTX 4060 8GB | 8 GB | Q4_K_M | 5.9 | Too big |
Estimates for single-stream decoding at Q4_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.
How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C52 |
Q3_K_SBest for your GPU | 3 | 7.4 GB | Low | C51 |
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 |
Copy-paste commands to run starcoder2 15b instruct v0.1 on your machine.
Run
lms load hf-lmstudio-community--starcoder2-15b-instruct-v0-1-gguf && lms server startアップグレードオプション
RTX 5060 Ti 16GBコスパ重視Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 79%.
〜$449 MSRP
RTX 4060 Ti 16GBコスパ最良Removes host-memory offload, which is usually the single biggest latency and throughput win.
Raises estimated decode speed by about 47%.
〜$499 MSRP
RTX 5070 12GBNVIDIAアップグレードRaises estimated decode speed by about 77%.
〜$549 MSRP
Yes, GTX 1080 Ti 11GB can run starcoder2 15b instruct v0.1 with a D grade (Very compromised (needs ~1.4 GB host RAM)). Expected decode speed: 16.0 tok/s.
starcoder2 15b instruct v0.1 (15B parameters) requires approximately 12.9 GB of memory with Q4_K_M quantization.
The recommended quantization for starcoder2 15b instruct v0.1 is Q4_K_M, which balances quality and memory efficiency.
On GTX 1080 Ti 11GB, starcoder2 15b instruct v0.1 achieves approximately 16.0 tokens per second decode speed with a time-to-first-token of 12083ms using Q4_K_M quantization.
For coding workloads, starcoder2 15b instruct v0.1 on GTX 1080 Ti 11GB receives a D grade with 16.0 tok/s and 4K context.
On GTX 1080 Ti 11GB, starcoder2 15b instruct v0.1 can safely use up to 4K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.
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<iframe src="https://willitrunai.com/embed/hf-lmstudio-community--starcoder2-15b-instruct-v0-1-gguf-on-gtx-1080-ti-11gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
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