RTX 4000 Ada 20GBOpção econômica20 GB VRAM (+4)
C
Adds memory headroom for longer context windows and future model growth.29 tok/s decodificação
Adds memory headroom for longer context windows and future model growth.
~$1,250 MSRP
Can StarCoder2 15B run on RTX 5000 Ada Laptop 16GB?
YES — Tight Fit
C53Usable
Estimated from fit model
StarCoder2 15B needs ~14.5 GB VRAM. RTX 5000 Ada Laptop 16GB has 16.0 GB. With Q5_K_M quantization, expect ~44 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: MediumStack: StandardBottleneck: 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
Q5_K_M (High quality) — 14.5 GB, 44.3 tok/s, Tight fit
14.5 GB required16.0 GB available
91% VRAM used
Fit status
Tight fit
Decode
44.3 tok/s
TTFT
4367 ms
Safe context
16K
Memory
14.5 GB / 16.0 GB
Memory breakdown
Weights10.8 GB
KV Cache1.2 GB
Runtime0.9 GB
Headroom1.6 GB
See how fast it feels
See how fast it feelsStarCoder2 15B on RTX 5000 Ada Laptop 16GB
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: 44.3 tok/s decode · 4.4s TTFT (warm) · 111 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 | Tight fit | 44.3 tok/s | 2382 ms | 16K |
| Coding | C | Tight fit | 44.3 tok/s | 4367 ms | 16K |
| Agentic Coding | C | Runs with offload | 44.3 tok/s | 6351 ms | 16K |
| Reasoning | C | Tight fit | 44.3 tok/s | 5161 ms | 16K |
| RAG | C | Runs with offload | 44.3 tok/s | 7939 ms | 16K |
Quantization options
How StarCoder2 15B (15B params) fits at each quantization level on RTX 5000 Ada Laptop 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 |
Get started
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 99Opções de upgrade
Hardware que roda bem StarCoder2 15B
RTX 4000 Ada 20GBOpção econômicaRTX 3090 24GBMelhor custo-benefício24 GB VRAM (+8)936 GB/s (+360)
B
Raises estimated decode speed by about 46%.64.5 tok/s decodificação
Raises estimated decode speed by about 46%.
Adds memory headroom for longer context windows and future model growth.
~$1,499 MSRP
RTX 4090 24GBUpgrade NVIDIA24 GB VRAM (+8)1008 GB/s (+432)
B
Raises estimated decode speed by about 82%.80.8 tok/s decodificação
Raises estimated decode speed by about 82%.
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Frequently asked questions
Can RTX 5000 Ada Laptop 16GB run StarCoder2 15B?
Yes, RTX 5000 Ada Laptop 16GB can run StarCoder2 15B with a C grade (Tight fit). Expected decode speed: 44.3 tok/s.
How much VRAM does StarCoder2 15B need?
StarCoder2 15B (15B parameters) requires approximately 14.5 GB of memory with Q5_K_M quantization.
What is the best quantization for StarCoder2 15B?
The recommended quantization for StarCoder2 15B is Q5_K_M, which balances quality and memory efficiency.
What speed will StarCoder2 15B run at on RTX 5000 Ada Laptop 16GB?
On RTX 5000 Ada Laptop 16GB, StarCoder2 15B achieves approximately 44.3 tokens per second decode speed with a time-to-first-token of 4367ms using Q5_K_M quantization.
Can RTX 5000 Ada Laptop 16GB run StarCoder2 15B for coding?
For coding workloads, StarCoder2 15B on RTX 5000 Ada Laptop 16GB receives a C grade with 44.3 tok/s and 16K context.
What context window can StarCoder2 15B use on RTX 5000 Ada Laptop 16GB?
On RTX 5000 Ada Laptop 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.
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