Mac Studio M3 Ultra 256GBコスパ重視
256 GB Unified (+128)
C
Adds memory headroom for longer context windows and future model growth.42 tok/sデコード
Adds memory headroom for longer context windows and future model growth.
〜$6,999 MSRP
Can StarCoder2 3B run on AMD Instinct MI250 128GB?
YES — Runs Great
C41Usable
Estimated from fit model
StarCoder2 3B needs ~16.0 GB VRAM. AMD Instinct MI250 128GB has 128.0 GB. With Q4_K_M quantization, expect ~42 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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
Q4_K_M (Medium quality) — 16.0 GB, 42.0 tok/s, Runs well
16.0 GB required128.0 GB available
13% VRAM used
Fit status
Runs well
Decode
42.0 tok/s
TTFT
4610 ms
Safe context
16K
Memory
16.0 GB / 128.0 GB
Memory breakdown
Weights1.8 GB
KV Cache0.5 GB
Runtime0.9 GB
Headroom12.8 GB
See how fast it feels
See how fast it feelsStarCoder2 3B on AMD Instinct MI250 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.
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 | 42.0 tok/s | 2514 ms | 16K |
| Coding | C | Runs well | 42.0 tok/s | 4610 ms | 16K |
| 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 AMD Instinct MI250 128GB (128.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 1.2 GB | Low | D37 |
Q3_K_S | 3 | 1.5 GB | Low | D37 |
NVFP4 | 4 | 1.7 GB | Medium | D37 |
Q4_K_M | 4 | 1.8 GB | Medium | D37 |
Q5_K_M | 5 | 2.2 GB | High | D37 |
Q6_K | 6 | 2.5 GB | High | D37 |
Q8_0 | 8 | 3.2 GB | Very High | D37 |
F16Best for your GPU | 16 | 6.1 GB | Maximum | D37 |
Get started
Copy-paste commands to run StarCoder2 3B on your machine.
Run
ollama run starcoder2:3bアップグレードオプション
StarCoder2 3Bを快適に動かすハードウェア
Frequently asked questions
Can AMD Instinct MI250 128GB run StarCoder2 3B?
Yes, AMD Instinct MI250 128GB can run StarCoder2 3B with a C grade (Runs well). Expected decode speed: 42.0 tok/s.
How much VRAM does StarCoder2 3B need?
StarCoder2 3B (3B parameters) requires approximately 16.0 GB of memory with Q4_K_M quantization.
What is the best quantization for StarCoder2 3B?
The recommended quantization for StarCoder2 3B is Q4_K_M, which balances quality and memory efficiency.
What speed will StarCoder2 3B run at on AMD Instinct MI250 128GB?
On AMD Instinct MI250 128GB, StarCoder2 3B achieves approximately 42.0 tokens per second decode speed with a time-to-first-token of 4610ms using Q4_K_M quantization.
Can AMD Instinct MI250 128GB run StarCoder2 3B for coding?
For coding workloads, StarCoder2 3B on AMD Instinct MI250 128GB receives a C grade with 42.0 tok/s and 16K context.
What context window can StarCoder2 3B use on AMD Instinct MI250 128GB?
On AMD Instinct MI250 128GB, StarCoder2 3B can safely use up to 16K tokens of context. The model's official context limit is 16K, but available memory constrains the safe maximum.
Embed this result▼
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<iframe src="https://willitrunai.com/embed/starcoder2-3b-on-instinct-mi250-128gb" 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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