RTX 5090 32GBコスパ重視32 GB VRAM (+8)1792 GB/s (+1492)
C
Raises estimated decode speed by about 516%.131.2 tok/sデコード
Raises estimated decode speed by about 516%.
Adds memory headroom for longer context windows and future model growth.
〜$1,999 MSRP
Can starcoder2 15b instruct v0.1 run on NVIDIA L4 24GB?
YES — Runs Great
C50Usable
Estimated from fit model
starcoder2 15b instruct v0.1 needs ~14.5 GB VRAM. NVIDIA L4 24GB has 24.0 GB. With Q4_K_M quantization, expect ~21 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced
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.
Select quantization to explore
Q4_K_M (Medium quality) — 14.5 GB, 21.3 tok/s, Runs well
14.5 GB required24.0 GB available
60% VRAM used
Fit status
Runs well
Decode
21.3 tok/s
TTFT
9084 ms
Safe context
102K
Memory
14.5 GB / 24.0 GB
Memory breakdown
Weights9.2 GB
KV Cache1.8 GB
Runtime1.2 GB
Headroom2.4 GB
See how fast it feels
See how fast it feelsstarcoder2 15b instruct v0.1 on NVIDIA L4 24GB
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: 21.3 tok/s decode · 9.1s TTFT (warm) · 53 tok/s prefill
What limits this setup
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.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 21.3 tok/s | 4955 ms | 102K |
| Coding | C | Runs well | 21.3 tok/s | 9084 ms | 102K |
| Agentic Coding | C | Runs well | 21.3 tok/s | 13214 ms | 102K |
| Reasoning | C | Runs well | 21.3 tok/s | 10736 ms | 102K |
| RAG | C | Runs well | 21.3 tok/s | 16517 ms | 102K |
Quantization options
How starcoder2 15b instruct v0.1 (15B params) fits at each quantization level on NVIDIA L4 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 5.9 GB | Low | C46 |
Q3_K_S | 3 | 7.4 GB | Low | C47 |
NVFP4 | 4 | 8.4 GB | Medium | C47 |
Q4_K_M | 4 | 9.2 GB | Medium | C48 |
Q5_K_M | 5 | 10.8 GB | High | C49 |
Q6_K | 6 | 12.3 GB | High | C50 |
Q8_0Best for your GPU | 8 | 16.1 GB | Very High | C49 |
F16 | 16 | 30.7 GB | Maximum | F0 |
Get started
Copy-paste commands to run starcoder2 15b instruct v0.1 on your machine.
Run
lms load hf-bartowski--starcoder2-15b-instruct-v0-1-gguf && lms server startアップグレードオプション
starcoder2 15b instruct v0.1を快適に動かすハードウェア
RTX 5090 32GBコスパ重視RTX PRO 4500 Blackwell 32GBコスパ最良32 GB VRAM (+8)896 GB/s (+596)
C
Raises estimated decode speed by about 286%.82.3 tok/sデコード
Raises estimated decode speed by about 286%.
Adds memory headroom for longer context windows and future model growth.
〜$2,499 MSRP
RTX 5000 Ada 32GBNVIDIAアップグレード32 GB VRAM (+8)576 GB/s (+276)
C
Raises estimated decode speed by about 137%.50.4 tok/sデコード
Raises estimated decode speed by about 137%.
Adds memory headroom for longer context windows and future model growth.
〜$4,000 MSRP
Frequently asked questions
Can NVIDIA L4 24GB run starcoder2 15b instruct v0.1?
Yes, NVIDIA L4 24GB can run starcoder2 15b instruct v0.1 with a C grade (Runs well). Expected decode speed: 21.3 tok/s.
How much VRAM does starcoder2 15b instruct v0.1 need?
starcoder2 15b instruct v0.1 (15B parameters) requires approximately 14.5 GB of memory with Q4_K_M quantization.
What is the best quantization for starcoder2 15b instruct v0.1?
The recommended quantization for starcoder2 15b instruct v0.1 is Q4_K_M, which balances quality and memory efficiency.
What speed will starcoder2 15b instruct v0.1 run at on NVIDIA L4 24GB?
On NVIDIA L4 24GB, starcoder2 15b instruct v0.1 achieves approximately 21.3 tokens per second decode speed with a time-to-first-token of 9084ms using Q4_K_M quantization.
Can NVIDIA L4 24GB run starcoder2 15b instruct v0.1 for coding?
For coding workloads, starcoder2 15b instruct v0.1 on NVIDIA L4 24GB receives a C grade with 21.3 tok/s and 102K context.
What context window can starcoder2 15b instruct v0.1 use on NVIDIA L4 24GB?
On NVIDIA L4 24GB, starcoder2 15b instruct v0.1 can safely use up to 102K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.
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<iframe src="https://willitrunai.com/embed/hf-bartowski--starcoder2-15b-instruct-v0-1-gguf-on-l4-24gb" 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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