Will It Run AI

Can Granite Code 34B run on NVIDIA DGX Spark 128GB?

YES — Runs Great

B69Good
Estimated from fit model

Granite Code 34B needs ~38.7 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~9 tok/s.

Runtime: OllamaCapacity: RoomyBandwidth: LowStack: BasicBottleneck: Balanced
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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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 38.7 GB, 8.6 tok/s, Runs well
38.7 GB required108.8 GB available
36% VRAM used

Fit status

Runs well

Decode

8.6 tok/s

TTFT

22628 ms

Safe context

8K

Memory

38.7 GB / 108.8 GB

Memory breakdown

Weights20.7 GB
KV Cache3.7 GB
Runtime1.2 GB
Headroom13.1 GB

See how fast it feels

See how fast it feelsGranite Code 34B 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: 8.6 tok/s decode · 22.6s TTFT (warm) · 21 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

WorkloadGradeFitDecodeTTFTContext
ChatBRuns well8.6 tok/s12342 ms8K
CodingBRuns well8.6 tok/s22628 ms8K
Agentic CodingBRuns well8.6 tok/s32913 ms8K
ReasoningBRuns well8.6 tok/s26742 ms8K
RAGBRuns well8.6 tok/s41141 ms8K

Quantization options

How Granite Code 34B (34B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
13.3 GB
LowB67
Q3_K_S
3
16.7 GB
LowB67
NVFP4
4
19.0 GB
MediumB68
Q4_K_M
4
20.7 GB
MediumB68
Q5_K_M
5
24.5 GB
HighB69
Q6_K
6
27.9 GB
HighB69
Q8_0
8
36.4 GB
Very HighA71
F16Best for your GPU
16
69.7 GB
MaximumA75

Get started

Copy-paste commands to run Granite Code 34B on your machine.

Run

ollama run granite-code:34b

アップグレードオプション

Granite Code 34Bを快適に動かすハードウェア

NVIDIANVIDIA H200 141GBコスパ重視
141 GB VRAM (+13)4800 GB/s (+4527)
A
Raises estimated decode speed by about 2349%.210.6 tok/sデコード

Raises estimated decode speed by about 2349%.

Adds memory headroom for longer context windows and future model growth.

〜$30,000 MSRP

NVIDIANVIDIA H200 PCIe 141GBコスパ最良
141 GB VRAM (+13)4800 GB/s (+4527)
A
Raises estimated decode speed by about 2349%.210.6 tok/sデコード

Raises estimated decode speed by about 2349%.

Adds memory headroom for longer context windows and future model growth.

〜$30,000 MSRP

NVIDIANVIDIA B200 180GBNVIDIAアップグレード
180 GB VRAM (+52)8000 GB/s (+7727)
A
Raises estimated decode speed by about 3981%.351 tok/sデコード

Raises estimated decode speed by about 3981%.

Adds memory headroom for longer context windows and future model growth.

〜$30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Granite Code 34B?

Yes, NVIDIA DGX Spark 128GB can run Granite Code 34B with a B grade (Runs well). Expected decode speed: 8.6 tok/s.

How much VRAM does Granite Code 34B need?

Granite Code 34B (34B parameters) requires approximately 38.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Granite Code 34B?

The recommended quantization for Granite Code 34B is Q4_K_M, which balances quality and memory efficiency.

What speed will Granite Code 34B run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Granite Code 34B achieves approximately 8.6 tokens per second decode speed with a time-to-first-token of 22628ms using Q4_K_M quantization.

Can NVIDIA DGX Spark 128GB run Granite Code 34B for coding?

For coding workloads, Granite Code 34B on NVIDIA DGX Spark 128GB receives a B grade with 8.6 tok/s and 8K context.

What context window can Granite Code 34B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Granite Code 34B 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 Granite Code 34B?

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.

See all results for NVIDIA DGX Spark 128GBSee all hardware for Granite Code 34B
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