Can StableLM 2 12B run on NVIDIA A16 64GB?

YES — Runs Great

C49Usable

Estimated from fit model

StableLM 2 12B needs ~28.1 GB VRAM. NVIDIA A16 64GB has 64.0 GB. With Q5_K_M quantization, expect ~51 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 28.1 GB, 50.5 tok/s, Runs well

28.1 GB required64.0 GB available

44% VRAM used

Fit status

Runs well

Decode

50.5 tok/s

TTFT

3831 ms

Safe context

Memory

28.1 GB / 64.0 GB

Memory breakdown

Weights8.6 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom6.4 GB

See how fast it feels

See how fast it feelsStableLM 2 12B on NVIDIA A16 64GB
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: 50.5 tok/s decode · 3.8s TTFT (warm) · 126 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	50.5 tok/s	2090 ms	4K
Coding	C	Runs well	50.5 tok/s	3831 ms	4K
Agentic Coding	C	Runs well	50.5 tok/s	5573 ms	4K
Reasoning	C	Runs well	50.5 tok/s	4528 ms	4K
RAG	C	Runs well	50.5 tok/s	6966 ms	4K

Quantization options

How StableLM 2 12B (12B params) fits at each quantization level on NVIDIA A16 64GB (64.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	C40
Q3_K_S	3	5.9 GB	Low	C40
NVFP4	4	6.7 GB	Medium	C40
Q4_K_M	4	7.3 GB	Medium	C41
Q5_K_M	5	8.6 GB	High	C41
Q6_K	6	9.8 GB	High	C41
Q8_0	8	12.8 GB	Very High	C41
F16Best for your GPU	16	24.6 GB	Maximum	C44

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

升级选项

能流畅运行 StableLM 2 12B 的硬件

NVIDIA A100 80GB经济之选

80 GB VRAM (+16)2039 GB/s (+1439)

Raises estimated decode speed by about 200%.151.6 tok/s 解码

Raises estimated decode speed by about 200%.

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

~$15,000 MSRP

NVIDIA A800 80GB最佳性价比

80 GB VRAM (+16)1935 GB/s (+1335)

Raises estimated decode speed by about 165%.133.6 tok/s 解码

Raises estimated decode speed by about 165%.

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

~$15,000 MSRP

NVIDIA H800 80GBNVIDIA 升级

80 GB VRAM (+16)3000 GB/s (+2400)

Raises estimated decode speed by about 233%.168 tok/s 解码

Raises estimated decode speed by about 233%.

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

~$30,000 MSRP

Frequently asked questions

Can NVIDIA A16 64GB run StableLM 2 12B?

Yes, NVIDIA A16 64GB can run StableLM 2 12B with a C grade (Runs well). Expected decode speed: 50.5 tok/s.

How much VRAM does StableLM 2 12B need?

StableLM 2 12B (12B parameters) requires approximately 28.1 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 A16 64GB?

On NVIDIA A16 64GB, StableLM 2 12B achieves approximately 50.5 tokens per second decode speed with a time-to-first-token of 3831ms using Q5_K_M quantization.

Can NVIDIA A16 64GB run StableLM 2 12B for coding?

For coding workloads, StableLM 2 12B on NVIDIA A16 64GB receives a C grade with 50.5 tok/s and 4K context.

What context window can StableLM 2 12B use on NVIDIA A16 64GB?

On NVIDIA A16 64GB, 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 A16 64GB See all hardware for StableLM 2 12B

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