Can StableLM 2 12B run on NVIDIA DGX Spark 128GB?

YES — Runs Great

C43Usable

Estimated from fit model

StableLM 2 12B needs ~34.8 GB VRAM. NVIDIA DGX Spark 128GB has 108.8 GB. With Q5_K_M quantization, expect ~18 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: LowStack: StandardBottleneck: 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

Q5_K_M (High quality) — 34.8 GB, 17.7 tok/s, Runs well

34.8 GB required108.8 GB available

32% VRAM used

Fit status

Runs well

Decode

17.7 tok/s

TTFT

10946 ms

Safe context

Memory

34.8 GB / 108.8 GB

Memory breakdown

Weights8.6 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom13.1 GB

See how fast it feels

See how fast it feelsStableLM 2 12B on NVIDIA DGX Spark 128GB
1st promptCold start — includes initialization
>What is local AI inference?
Local AI inference means running an AI language model directly on your own hardware — your laptop, desktop, or server — instead of sending requests to a remote cloud API.

When you run inference locally the model weights are loaded into your GPU or unified memory. Each token you generate requires reading those weights from memory, so memory bandwidth is the main bottleneck for decode speed.

Key benefits of running locally:
  - Full privacy: your prompts never leave your machine
  - No per-token cost or rate limits
  - Works offline once the model is downloaded
  - Latency depends only on your hardware
2nd promptWarm — model ready, no init overhead
>How much VRAM do I need?
It depends on the model size and quantization level. A rough rule of thumb:

  Model size     Q4 (4-bit)    Q8 (8-bit)    FP16
  7B params      ~4.3 GB       ~7.5 GB       ~14 GB
  13B params     ~7.9 GB       ~13.9 GB      ~26 GB
  70B params     ~42.7 GB      ~74.9 GB      ~140 GB

Most people use 4-bit quantization (Q4_K_M) which gives 90-95% of full quality at a fraction of the memory. A 24 GB GPU can comfortably run most 7B-13B models.
Estimated: 17.7 tok/s decode · 10.9s TTFT (warm) · 44 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	17.7 tok/s	5971 ms	4K
Coding	C	Runs well	17.7 tok/s	10946 ms	4K
Agentic Coding	C	Runs well	17.7 tok/s	15922 ms	4K
Reasoning	C	Runs well	17.7 tok/s	12937 ms	4K
RAG	C	Runs well	17.7 tok/s	19902 ms	4K

Quantization options

How StableLM 2 12B (12B params) fits at each quantization level on NVIDIA DGX Spark 128GB (92.2 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	D39
Q3_K_S	3	5.9 GB	Low	D39
NVFP4	4	6.7 GB	Medium	D39
Q4_K_M	4	7.3 GB	Medium	D39
Q5_K_M	5	8.6 GB	High	D39
Q6_K	6	9.8 GB	High	D39
Q8_0	8	12.8 GB	Very High	D40
F16Best for your GPU	16	24.6 GB	Maximum	C41

Get started

Copy-paste commands to run StableLM 2 12B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
  --hf-repo "stabilityai/stablelm-2-12b-chat" \
  --hf-file "stablelm-2-12b-chat-Q5_K_M.gguf" \
  -c 4096 -ngl 99

アップグレードオプション

StableLM 2 12Bを快適に動かすハードウェア

NVIDIA H200 141GBコスパ重視

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

Raises estimated decode speed by about 849%.168 tok/sデコード

Raises estimated decode speed by about 849%.

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

〜$30,000 MSRP

NVIDIA H200 PCIe 141GBコスパ最良

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

Raises estimated decode speed by about 849%.168 tok/sデコード

Raises estimated decode speed by about 849%.

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)

Raises estimated decode speed by about 849%.168 tok/sデコード

Raises estimated decode speed by about 849%.

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

〜$30,000 MSRP

Frequently asked questions

Can NVIDIA DGX Spark 128GB run StableLM 2 12B?

Yes, NVIDIA DGX Spark 128GB can run StableLM 2 12B with a C grade (Runs well). Expected decode speed: 17.7 tok/s.

How much VRAM does StableLM 2 12B need?

StableLM 2 12B (12B parameters) requires approximately 34.8 GB of memory with Q5_K_M quantization.

What is the best quantization for StableLM 2 12B?

The recommended quantization for StableLM 2 12B is Q5_K_M, which balances quality and memory efficiency.

What speed will StableLM 2 12B run at on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, StableLM 2 12B achieves approximately 17.7 tokens per second decode speed with a time-to-first-token of 10946ms using Q5_K_M quantization.

Can NVIDIA DGX Spark 128GB run StableLM 2 12B for coding?

For coding workloads, StableLM 2 12B on NVIDIA DGX Spark 128GB receives a C grade with 17.7 tok/s and 4K context.

What context window can StableLM 2 12B use on NVIDIA DGX Spark 128GB?

On NVIDIA DGX Spark 128GB, StableLM 2 12B can safely use up to 4K tokens of context. The model's official context limit is 4K, but available memory constrains the safe maximum.

Is unified memory on NVIDIA DGX Spark 128GB as fast as VRAM for StableLM 2 12B?

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 StableLM 2 12B

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