Mac Studio M3 Ultra 256GBBudget pick
256 GB Unified (+128)819 GB/s (+546)
C
Adds memory headroom for longer context windows and future model growth.42 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$6,999 MSRP
Can StarCoder2 3B run on NVIDIA DGX Spark 128GB?
YES — With F16
C42Usable
○Estimated from fit model
StarCoder2 3B needs ~20.9 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~41 tok/s.
Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced
Share:
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.
StarCoder2 3B at Q4_K_M needs 3.5 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (20.9 GB) with maximum quality. 8 quantization levels fit.
Select quantization to explore
Q4_K_M (Medium quality) — 16.5 GB, 42.0 tok/s, Runs well
16.5 GB required108.8 GB available
15% VRAM used
Fit status
Runs well
Decode
42.0 tok/s
TTFT
4610 ms
Safe context
16K
Memory
16.5 GB / 108.8 GB
Memory breakdown
Weights1.8 GB
KV Cache0.5 GB
Runtime1.2 GB
Headroom13.1 GB
See how fast it feels
See how fast it feelsStarCoder2 3B on NVIDIA DGX Spark 128GB
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: 42.0 tok/s decode · 4.6s TTFT (warm) · 105 tok/s prefill
What limits this setup
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
Best improvement path
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 42.0 tok/s | 2514 ms | 16K |
| Coding | F | Too heavy | 16.1 tok/s | 12016 ms | 4K |
| Agentic Coding | C | Runs well | 42.0 tok/s | 6705 ms | 16K |
| Reasoning | C | Runs well | 42.0 tok/s | 5448 ms | 16K |
| RAG | C | Runs well | 42.0 tok/s | 8381 ms | 16K |
Quantization options
How StarCoder2 3B (3B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | D38 |
Q3_K_S | 3 | 1.5 GB | Low | D38 |
NVFP4 | 4 |
Get started
Copy-paste commands to run StarCoder2 3B on your machine.
Run
ollama run starcoder2:3bUpgrade options