NVIDIA H200 141GBコスパ重視141 GB VRAM (+13)4800 GB/s (+4527)
B
Raises estimated decode speed by about 155%.98 tok/sデコード
Raises estimated decode speed by about 155%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
Can StarCoder 7B run on NVIDIA DGX Spark 128GB?
YES — Runs Great
B67Good
Estimated from fit model
StarCoder 7B needs ~25.9 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~38 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) — 25.9 GB, 38.4 tok/s, Runs well
25.9 GB required108.8 GB available
24% VRAM used
Fit status
Runs well
Decode
38.4 tok/s
TTFT
5047 ms
Safe context
8K
Memory
25.9 GB / 108.8 GB
Memory breakdown
Weights4.3 GB
KV Cache7.3 GB
Runtime1.2 GB
Headroom13.1 GB
See how fast it feels
See how fast it feelsStarCoder 7B 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: 38.4 tok/s decode · 5.0s TTFT (warm) · 96 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 | B | Runs well | 38.4 tok/s | 2753 ms | 8K |
| Coding | B | Runs well | 38.4 tok/s | 5047 ms | 8K |
| Agentic Coding | B | Runs well | 38.4 tok/s | 7341 ms | 8K |
| Reasoning | B | Runs well | 38.4 tok/s | 5964 ms | 8K |
| RAG | B | Runs well | 38.4 tok/s | 9176 ms | 8K |
Quantization options
How StarCoder 7B (7B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B63 |
Q3_K_S | 3 | 3.4 GB | Low | B63 |
NVFP4 | 4 | 3.9 GB | Medium | B63 |
Q4_K_M | 4 | 4.3 GB | Medium | B63 |
Q5_K_M | 5 | 5.0 GB | High | B63 |
Q6_K | 6 | 5.7 GB | High | B63 |
Q8_0 | 8 | 7.5 GB | Very High | B63 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | B63 |
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
NVIDIA H200 141GBコスパ重視NVIDIA H200 PCIe 141GBコスパ最良141 GB VRAM (+13)4800 GB/s (+4527)
B
Raises estimated decode speed by about 155%.98 tok/sデコード
Raises estimated decode speed by about 155%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
NVIDIA B200 180GBNVIDIAアップグレード180 GB VRAM (+52)8000 GB/s (+7727)
B
Raises estimated decode speed by about 155%.98 tok/sデコード
Raises estimated decode speed by about 155%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
Frequently asked questions
Can NVIDIA DGX Spark 128GB run StarCoder 7B?
Yes, NVIDIA DGX Spark 128GB can run StarCoder 7B with a B grade (Runs well). Expected decode speed: 38.4 tok/s.
How much VRAM does StarCoder 7B need?
StarCoder 7B (7B parameters) requires approximately 25.9 GB of memory with Q4_K_M quantization.
What is the best quantization for StarCoder 7B?
The recommended quantization for StarCoder 7B is Q4_K_M, which balances quality and memory efficiency.
What speed will StarCoder 7B run at on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, StarCoder 7B achieves approximately 38.4 tokens per second decode speed with a time-to-first-token of 5047ms using Q4_K_M quantization.
Can NVIDIA DGX Spark 128GB run StarCoder 7B for coding?
For coding workloads, StarCoder 7B on NVIDIA DGX Spark 128GB receives a B grade with 38.4 tok/s and 8K context.
What context window can StarCoder 7B use on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, StarCoder 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for StarCoder 7B?
Not always. NVIDIA DGX Spark 128GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.
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