NVIDIA H200 141GB经济之选141 GB VRAM (+13)4800 GB/s (+4527)
B
Adds memory headroom for longer context windows and future model growth.71.4 tok/s 解码
Adds memory headroom for longer context windows and future model growth.
~$30,000 MSRP
Can Gemma 4 E2B run on NVIDIA DGX Spark 128GB?
YES — With F16
B65Good
Estimated from fit model
Gemma 4 E2B needs ~25.2 GB VRAM. NVIDIA DGX Spark 128GB has 0 MB. With F16 quantization, expect ~24 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.
Gemma 4 E2B at Q4_K_M needs 4.8 GB — too much for NVIDIA DGX Spark 128GB (0.0 GB). Runs at F16 (25.2 GB) with maximum quality. 8 quantization levels fit.
Select quantization to explore
Q4_K_M (Medium quality) — 17.9 GB, 57.3 tok/s, Runs well
17.9 GB required108.8 GB available
16% VRAM used
Fit status
Runs well
Decode
57.3 tok/s
TTFT
3381 ms
Safe context
128K
Memory
17.9 GB / 108.8 GB
Memory breakdown
Weights3.1 GB
KV Cache0.5 GB
Runtime1.2 GB
Headroom13.1 GB
See how fast it feels
See how fast it feelsGemma 4 E2B 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: 57.3 tok/s decode · 3.4s TTFT (warm) · 143 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 | 57.3 tok/s | 1844 ms | 128K |
| Coding | F | Too heavy | 9.5 tok/s | 20428 ms | 4K |
| Agentic Coding | B | Runs well | 57.3 tok/s | 4918 ms | 128K |
| Reasoning | B | Runs well | 57.3 tok/s | 3996 ms | 128K |
| RAG | B | Runs well | 57.3 tok/s | 6148 ms | 128K |
Quantization options
How Gemma 4 E2B (5.099999904632568B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.0 GB | Low | B62 |
Q3_K_S | 3 | 2.5 GB | Low | B62 |
NVFP4 | 4 | 2.9 GB | Medium | B62 |
Q4_K_M | 4 | 3.1 GB | Medium | B62 |
Q5_K_M | 5 | 3.7 GB | High | B62 |
Q6_K | 6 | 4.2 GB | High | B62 |
Q8_0 | 8 | 5.5 GB | Very High | B62 |
F16Best for your GPU | 16 | 10.5 GB | Maximum | B62 |
Get started
Copy-paste commands to run Gemma 4 E2B on your machine.
Run
ollama run gemma4:e2b升级选项
能流畅运行 Gemma 4 E2B 的硬件
Frequently asked questions
Can NVIDIA DGX Spark 128GB run Gemma 4 E2B?
Yes, NVIDIA DGX Spark 128GB can run Gemma 4 E2B at F16 quantization (Runs well). The recommended Q4_K_M requires 4.8 GB which exceeds available memory, but at F16 it needs only 25.2 GB. Expected decode speed: 23.9 tok/s.
How much VRAM does Gemma 4 E2B need?
Gemma 4 E2B (5.099999904632568B parameters) requires approximately 4.8 GB at Q4_K_M quantization. On NVIDIA DGX Spark 128GB, it fits at F16 using 25.2 GB.
What is the best quantization for Gemma 4 E2B?
The recommended quantization is Q4_K_M, but on NVIDIA DGX Spark 128GB the best fitting quantization is F16, which uses 25.2 GB.
What speed will Gemma 4 E2B run at on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, Gemma 4 E2B achieves approximately 23.9 tokens per second decode speed with a time-to-first-token of 8116ms using F16 quantization.
Can NVIDIA DGX Spark 128GB run Gemma 4 E2B for coding?
For coding workloads, Gemma 4 E2B on NVIDIA DGX Spark 128GB receives a F grade with 9.5 tok/s and 4K context.
What context window can Gemma 4 E2B use on NVIDIA DGX Spark 128GB?
On NVIDIA DGX Spark 128GB, Gemma 4 E2B can safely use up to 128K tokens of context at F16 quantization. The model's official context limit is 128K, but available memory constrains the safe maximum.
Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Gemma 4 E2B?
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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