NVIDIA H200 141GBコスパ重視141 GB VRAM (+13)4800 GB/s (+4527)
A
Raises estimated decode speed by about 138%.98 tok/sデコード
Raises estimated decode speed by about 138%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
Can SQLCoder 7B run on NVIDIA DGX Spark 128GB?
YES — With F16
A72Great
Estimated from fit model
SQLCoder 7B needs ~30.6 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~17 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.
SQLCoder 7B at Q4_K_M needs 7.4 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (30.6 GB) with maximum quality. 8 quantization levels fit.
Select quantization to explore
Q4_K_M (Medium quality) — 20.5 GB, 41.2 tok/s, Runs well
20.5 GB required108.8 GB available
19% VRAM used
Fit status
Runs well
Decode
41.2 tok/s
TTFT
4695 ms
Safe context
8K
Memory
20.5 GB / 108.8 GB
Memory breakdown
Weights4.3 GB
KV Cache2.0 GB
Runtime1.2 GB
Headroom13.1 GB
See how fast it feels
See how fast it feelsSQLCoder 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: 41.2 tok/s decode · 4.7s TTFT (warm) · 103 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 | F | Too heavy | 6.9 tok/s | 15293 ms | 4K |
| Coding | F | Too heavy | 6.9 tok/s | 28038 ms | 4K |
| Agentic Coding | F | Too heavy | 6.9 tok/s | 40783 ms | 4K |
| Reasoning | F | Too heavy | 6.9 tok/s | 33136 ms | 4K |
| RAG | F | Too heavy | 6.9 tok/s | 50978 ms | 4K |
Quantization options
How SQLCoder 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 | B69 |
Q3_K_S | 3 | 3.4 GB | Low | B69 |
NVFP4 | 4 | 3.9 GB | Medium | B69 |
Q4_K_M | 4 | 4.3 GB | Medium | B69 |
Q5_K_M | 5 | 5.0 GB | High | B69 |
Q6_K | 6 | 5.7 GB | High | B69 |
Q8_0 | 8 | 7.5 GB | Very High | B69 |
F16Best for your GPU | 16 | 14.3 GB | Maximum | B70 |
Get started
Copy-paste commands to run SQLCoder 7B on your machine.
Run
ollama run sqlcoderアップグレードオプション
SQLCoder 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)
A
Raises estimated decode speed by about 138%.98 tok/sデコード
Raises estimated decode speed by about 138%.
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)
A
Raises estimated decode speed by about 138%.98 tok/sデコード
Raises estimated decode speed by about 138%.
Adds memory headroom for longer context windows and future model growth.
〜$30,000 MSRP
Frequently asked questions
Can NVIDIA DGX Spark 128GB run SQLCoder 7B?
Yes, NVIDIA DGX Spark 128GB can run SQLCoder 7B at F16 quantization (Runs well). The recommended Q4_K_M requires 7.4 GB which exceeds available memory, but at F16 it needs only 30.6 GB. Expected decode speed: 17.2 tok/s.
How much VRAM does SQLCoder 7B need?
SQLCoder 7B (7B parameters) requires approximately 7.4 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 30.6 GB.
What is the best quantization for SQLCoder 7B?
The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 30.6 GB.
What speed will SQLCoder 7B run at on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, SQLCoder 7B achieves approximately 17.2 tokens per second decode speed with a time-to-first-token of 11270ms using F16 quantization.
Can NVIDIA DGX Spark 128GB run SQLCoder 7B for coding?
For coding workloads, SQLCoder 7B on NVIDIA DGX Spark 128GB receives a F grade with 6.9 tok/s and 4K context.
What context window can SQLCoder 7B use on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, SQLCoder 7B can safely use up to 8K tokens of context at F16 quantization. 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 SQLCoder 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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