Can Yi Coder 9B run on NVIDIA DGX Spark 128GB?

YES — Runs Great

C55Usable

Estimated from fit model

Yi Coder 9B needs ~21.2 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q4_K_M quantization, expect ~32 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.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 21.2 GB, 32.4 tok/s, Runs well

21.2 GB required108.8 GB available

19% VRAM used

Fit status

Runs well

Decode

32.4 tok/s

TTFT

5967 ms

Safe context

131K

Memory

21.2 GB / 108.8 GB

Memory breakdown

Weights5.5 GB

KV Cache1.5 GB

Runtime1.2 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsYi Coder 9B 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: 32.4 tok/s decode · 6.0s TTFT (warm) · 81 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	C	Runs well	32.4 tok/s	3255 ms	131K
Coding	C	Runs well	32.4 tok/s	5967 ms	131K
Agentic Coding	F	Too heavy	5.4 tok/s	52435 ms	4K
Reasoning	C	Runs well	32.4 tok/s	7052 ms	131K
RAG	B	Runs well	32.4 tok/s	10849 ms	131K

Quantization options

How Yi Coder 9B (9B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.5 GB	Low	C51
Q3_K_S	3	4.4 GB	Low	C51
NVFP4	4	5.0 GB	Medium	C51
Q4_K_M	4	5.5 GB	Medium	C51
Q5_K_M	5	6.5 GB	High	C51
Q6_K	6	7.4 GB	High	C52
Q8_0	8	9.6 GB	Very High	C52
F16Best for your GPU	16	18.5 GB	Maximum	C53

Get started

Copy-paste commands to run Yi Coder 9B on your machine.

Run

lms load Yi-Coder-9B-Chat && lms server start

Opções de upgrade

Hardware que roda bem Yi Coder 9B

NVIDIA H200 141GBOpção econômica

141 GB VRAM (+13)4800 GB/s (+4527)

Raises estimated decode speed by about 289%.126 tok/s decodificação

Raises estimated decode speed by about 289%.

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

~$30,000 MSRP

NVIDIA H200 PCIe 141GBMelhor custo-benefício

141 GB VRAM (+13)4800 GB/s (+4527)

Raises estimated decode speed by about 289%.126 tok/s decodificação

Raises estimated decode speed by about 289%.

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

~$30,000 MSRP

NVIDIA B200 180GBUpgrade NVIDIA

180 GB VRAM (+52)8000 GB/s (+7727)

Raises estimated decode speed by about 289%.126 tok/s decodificação

Raises estimated decode speed by about 289%.

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

~$30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run Yi Coder 9B?

Yes, NVIDIA DGX Spark 128GB can run Yi Coder 9B with a C grade (Runs well). Expected decode speed: 32.4 tok/s.

How much VRAM does Yi Coder 9B need?

Yi Coder 9B (9B parameters) requires approximately 21.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Yi Coder 9B?

The recommended quantization for Yi Coder 9B is Q4_K_M, which balances quality and memory efficiency.

What speed will Yi Coder 9B run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Yi Coder 9B achieves approximately 32.4 tokens per second decode speed with a time-to-first-token of 5967ms using Q4_K_M quantization.

Can NVIDIA DGX Spark 128GB run Yi Coder 9B for coding?

For coding workloads, Yi Coder 9B on NVIDIA DGX Spark 128GB receives a C grade with 32.4 tok/s and 131K context.

What context window can Yi Coder 9B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, Yi Coder 9B can safely use up to 131K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for Yi Coder 9B?

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 128GB See all hardware for Yi Coder 9B

Embed this result▼

Paste this snippet into any page to show a live fit card.

<iframe src="https://willitrunai.com/embed/yi-coder-9b-on-dgx-spark-128gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview: