Can StableLM 2 12B run on NVIDIA H100 PCIe 80GB?

YES — Runs Great

C50Usable

Estimated from fit model

StableLM 2 12B needs ~29.7 GB VRAM. NVIDIA H100 PCIe 80GB has 80.0 GB. With Q5_K_M quantization, expect ~149 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 29.7 GB, 148.7 tok/s, Runs well

29.7 GB required80.0 GB available

37% VRAM used

Fit status

Runs well

Decode

148.7 tok/s

TTFT

1302 ms

Safe context

Memory

29.7 GB / 80.0 GB

Memory breakdown

Weights8.6 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom8.0 GB

See how fast it feels

See how fast it feelsStableLM 2 12B on NVIDIA H100 PCIe 80GB
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: 148.7 tok/s decode · 1.3s TTFT (warm) · 372 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	148.7 tok/s	710 ms	4K
Coding	C	Runs well	148.7 tok/s	1302 ms	4K
Agentic Coding	C	Runs well	148.7 tok/s	1893 ms	4K
Reasoning	C	Runs well	148.7 tok/s	1538 ms	4K
RAG	C	Runs well	148.7 tok/s	2366 ms	4K

Inference speed

StableLM 2 12B inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for StableLM 2 12B at Q5_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~103 tok/s. Speed is memory-bandwidth bound, so cards that fit the whole model in VRAM run far faster than ones that offload to system RAM.

GPU / Mac	Memory	Quant	Speed (tok/s)	Fits?
RTX 5090 32GB	32 GB	Q5_K_M	103.1	Fits
Mac Studio M3 Ultra 256GB	256 GB	Q5_K_M	60.1	Fits
RTX 4090 24GB	24 GB	Q5_K_M	53.5	Offloads
Mac Studio M2 Ultra 128GB	128 GB	Q5_K_M	50.1	Fits
RX 7900 XTX 24GB	24 GB	Q5_K_M	47.8	Offloads
Mac Studio M1 Ultra 128GB	128 GB	Q5_K_M	47.5	Fits
RTX 3090 24GB	24 GB	Q5_K_M	45.3	Offloads
MacBook Pro M4 Max 128GB	128 GB	Q5_K_M	32.7	Fits
MacBook Pro M4 Max 64GB	64 GB	Q5_K_M	32.7	Fits
MacBook Pro M3 Max 64GB	64 GB	Q5_K_M	25.9	Fits
MacBook Pro M1 Max 64GB	64 GB	Q5_K_M	23.8	Fits
RTX 4080 Super 16GB	16 GB	Q5_K_M	23.4	Too big
MacBook Pro M4 Pro 48GB	48 GB	Q5_K_M	20.0	Fits
RTX 4070 12GB	12 GB	Q5_K_M	8.2	Too big
RTX 3060 12GB	12 GB	Q5_K_M	4.8	Too big
RTX 4060 8GB	8 GB	Q5_K_M	3.4	Too big

Estimates for single-stream decoding at Q5_K_M; real tokens/sec varies with prompt length, context, batch size, and runtime build. Prompt processing (prefill) is faster than the decode figures shown here.

Quantization options

How StableLM 2 12B (12B params) fits at each quantization level on NVIDIA H100 PCIe 80GB (80.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	D39
Q3_K_S	3	5.9 GB	Low	D40
NVFP4	4	6.7 GB	Medium	D40
Q4_K_M	4	7.3 GB	Medium	D40
Q5_K_M	5	8.6 GB	High	D40
Q6_K	6	9.8 GB	High	D40
Q8_0	8	12.8 GB	Very High	C40
F16Best for your GPU	16	24.6 GB	Maximum	C42

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

Frequently asked questions

Can NVIDIA H100 PCIe 80GB run StableLM 2 12B?

Yes, NVIDIA H100 PCIe 80GB can run StableLM 2 12B with a C grade (Runs well). Expected decode speed: 148.7 tok/s.

How much VRAM does StableLM 2 12B need?

StableLM 2 12B (12B parameters) requires approximately 29.7 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 H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, StableLM 2 12B achieves approximately 148.7 tokens per second decode speed with a time-to-first-token of 1302ms using Q5_K_M quantization.

Can NVIDIA H100 PCIe 80GB run StableLM 2 12B for coding?

For coding workloads, StableLM 2 12B on NVIDIA H100 PCIe 80GB receives a C grade with 148.7 tok/s and 4K context.

What context window can StableLM 2 12B use on NVIDIA H100 PCIe 80GB?

On NVIDIA H100 PCIe 80GB, 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.

See all results for NVIDIA H100 PCIe 80GB See all hardware for StableLM 2 12B

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