RTX 3060 12GBコスパ最良12 GB VRAM (+1)
B
Makes the model fit on the accelerator instead of staying completely out of reach.30.2 tok/sデコード
Makes the model fit on the accelerator instead of staying completely out of reach.
〜$329 MSRP
Can StarCoder 7B run on GTX 1080 Ti 11GB?
YES — With Q3_K_S
B58Good
Estimated from fit model
StarCoder 7B needs ~13.1 GB VRAM. GTX 1080 Ti 11GB has 11.0 GB. With Q3_K_S quantization, expect ~39 tok/s.
Runtime: OllamaCapacity: OffloadBandwidth: MediumStack: BasicBottleneck: Host offload
Operating mode
Choose the run profile you care about
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.
StarCoder 7B at Q4_K_M needs 13.9 GB — too much for GTX 1080 Ti 11GB (11.0 GB). Runs at Q3_K_S (13.1 GB) with low quality. 2 quantization levels fit.
Select quantization to explore
Q4_K_M (Medium quality) — 13.9 GB, exceeds 11.0 GB available
13.9 GB required11.0 GB available
126% VRAM needed
2.9 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
29.2 tok/s
TTFT
6629 ms
Safe context
8K
Memory
13.9 GB / 11.0 GB
Offload
20%
Memory breakdown
Weights4.3 GB
KV Cache7.3 GB
Runtime1.2 GB
Headroom1.1 GB
See how fast it feels
With memory offload — actual speed may be lowerSee how fast it feelsStarCoder 7B on GTX 1080 Ti 11GB
1st promptCold start — includes initialization
>Write a Python function to sort a list
Here's a Python function that sorts a list using quicksort:
def quicksort(arr):
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)
# Example usage
numbers = [3, 6, 8, 10, 1, 2, 1]
print(quicksort(numbers)) # [1, 1, 2, 3, 6, 8, 10]
The algorithm picks a pivot, partitions the list into smaller, equal, and larger elements, then recursively sorts each partition and concatenates the results.
2nd promptWarm — model ready, no init overhead
>Now add type hints and a docstring
def quicksort(arr: list[int]) -> list[int]:
"""Sort a list using the quicksort algorithm.
Args:
arr: List of integers to sort.
Returns:
A new sorted list.
"""
if len(arr) <= 1:
return arr
pivot = arr[len(arr) // 2]
left = [x for x in arr if x < pivot]
middle = [x for x in arr if x == pivot]
right = [x for x in arr if x > pivot]
return quicksort(left) + middle + quicksort(right)
Added type hints for the input and return type, plus a Google-style docstring.
Estimated: 29.2 tok/s decode · 6.6s TTFT (warm) · 73 tok/s prefill
What limits this setup
It fits through host-memory offload, and offload is the main reason performance drops.
CPU or host-memory offload is active
About 20% 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.
Best improvement path
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 0.5 GB of extra host RAM just for the offloaded portion, before OS and other tools.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Tight fit | 66.9 tok/s | 1579 ms | 8K |
| Coding | F | Too heavy | 29.2 tok/s | 6629 ms | 8K |
| Agentic Coding | F | Too heavy | 11.5 tok/s | 24432 ms | 8K |
| Reasoning | F | Too heavy | 29.2 tok/s | 7834 ms | 8K |
| RAG | F | Too heavy | 11.5 tok/s | 30540 ms | 8K |
Quantization options
How StarCoder 7B (7B params) fits at each quantization level on GTX 1080 Ti 11GB (11.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | A73 |
Q3_K_S | 3 | 3.4 GB | Low | A74 |
NVFP4 | 4 | 3.9 GB | Medium | A75 |
Q4_K_M | 4 | 4.3 GB | Medium | A75 |
Q5_K_M | 5 | 5.0 GB | High | A76 |
Q6_K | 6 | 5.7 GB | High | A76 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | A75 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Get started
Copy-paste commands to run StarCoder 7B on your machine.
Run
lms load starcoder-7b && lms server startアップグレードオプション
StarCoder 7Bを快適に動かすハードウェア
RTX 3060 12GBコスパ最良RTX 5060 Ti 16GBコスパ重視16 GB VRAM (+5)
A
Makes the model fit on the accelerator instead of staying completely out of reach.65 tok/sデコード
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.
〜$449 MSRP
RTX 4060 Ti 16GBNVIDIAアップグレード16 GB VRAM (+5)
A
Makes the model fit on the accelerator instead of staying completely out of reach.49.2 tok/sデコード
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.
〜$499 MSRP
Frequently asked questions
Can GTX 1080 Ti 11GB run StarCoder 7B?
Yes, GTX 1080 Ti 11GB can run StarCoder 7B at Q3_K_S quantization (Very compromised (needs ~0.5 GB host RAM)). The recommended Q4_K_M requires 13.9 GB which exceeds available memory, but at Q3_K_S it needs only 13.1 GB. Expected decode speed: 38.8 tok/s.
How much VRAM does StarCoder 7B need?
StarCoder 7B (7B parameters) requires approximately 13.9 GB at Q4_K_M quantization. On GTX 1080 Ti 11GB, it fits at Q3_K_S using 13.1 GB.
What is the best quantization for StarCoder 7B?
The recommended quantization is Q4_K_M, but on GTX 1080 Ti 11GB the best fitting quantization is Q3_K_S, which uses 13.1 GB.
What speed will StarCoder 7B run at on GTX 1080 Ti 11GB?
On GTX 1080 Ti 11GB, StarCoder 7B achieves approximately 38.8 tokens per second decode speed with a time-to-first-token of 4993ms using Q3_K_S quantization.
Can GTX 1080 Ti 11GB run StarCoder 7B for coding?
For coding workloads, StarCoder 7B on GTX 1080 Ti 11GB receives a F grade with 29.2 tok/s and 8K context.
What context window can StarCoder 7B use on GTX 1080 Ti 11GB?
On GTX 1080 Ti 11GB, StarCoder 7B can safely use up to 8K tokens of context at Q3_K_S quantization. The model's official context limit is 8K, but available memory constrains the safe maximum.
What should I upgrade first if StarCoder 7B feels slow on GTX 1080 Ti 11GB?
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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