Can StableLM 2 12B run on GTX 1660 Super 6GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

StableLM 2 12B needs ~22.3 GB but GTX 1660 Super 6GB only has 6.0 GB. Try a smaller quantization or lighter model.

Runtime: llama.cppCapacity: No fitBandwidth: LowStack: StandardBottleneck: Memory capacity

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) — 22.3 GB, exceeds 6.0 GB available

22.3 GB required6.0 GB available

372% VRAM needed

16.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

3.0 tok/s

TTFT

64683 ms

Safe context

Memory

22.3 GB / 6.0 GB

Offload

70%

Memory breakdown

Weights8.6 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsStableLM 2 12B on GTX 1660 Super 6GB
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: 3.0 tok/s decode · 64.7s TTFT (warm) · 8 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 22.3 GB, but this setup only exposes 6.0 GB of usable VRAM.

Older PCIe generation

PCIe 3.0 is workable, but it compounds the penalty when you offload heavily or try to scale across multiple cards.

Best improvement path

Add more VRAM headroom

The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	F	Too heavy	3.0 tok/s	35282 ms	4K
Coding	F	Too heavy	3.0 tok/s	64683 ms	4K
Agentic Coding	F	Too heavy	3.0 tok/s	94084 ms	4K
Reasoning	F	Too heavy	3.0 tok/s	76443 ms	4K
RAG	F	Too heavy	3.0 tok/s	117605 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 GTX 1660 Super 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.7 GB	Low	F0
Q3_K_S	3	5.9 GB	Low	F0
NVFP4	4	6.7 GB	Medium	F0
Q4_K_M	4	7.3 GB	Medium	F0
Q5_K_M	5	8.6 GB	High	F0
Q6_K	6	9.8 GB	High	F0
Q8_0	8	12.8 GB	Very High	F0
F16	16	24.6 GB	Maximum	F0

Upgrade-Optionen

Hardware, die StableLM 2 12B gut ausführt

RTX 4000 Ada 20GBNVIDIA-Upgrade

20 GB VRAM (+14)360 GB/s (+24)

Makes the model fit on the accelerator instead of staying completely out of reach.15.8 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Raises estimated decode speed by about 427%.

ca. $1,250 MSRP

RTX 3090 24GBGünstige Wahl

24 GB VRAM (+18)936 GB/s (+600)

Makes the model fit on the accelerator instead of staying completely out of reach.45.3 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

ca. $1,499 MSRP

RTX 4090 24GBBestes Preis-Leistungs-Verhältnis

24 GB VRAM (+18)1008 GB/s (+672)

Makes the model fit on the accelerator instead of staying completely out of reach.53.5 tok/s Dekodierung

Makes the model fit on the accelerator instead of staying completely out of reach.

Removes host-memory offload, which is usually the single biggest latency and throughput win.

ca. $1,599 MSRP

Frequently asked questions

Can GTX 1660 Super 6GB run StableLM 2 12B?

No, StableLM 2 12B requires more memory than GTX 1660 Super 6GB provides.

How much VRAM does StableLM 2 12B need?

StableLM 2 12B (12B parameters) requires approximately 22.3 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 GTX 1660 Super 6GB?

On GTX 1660 Super 6GB, StableLM 2 12B achieves approximately 3.0 tokens per second decode speed with a time-to-first-token of 64683ms using Q5_K_M quantization.

Can GTX 1660 Super 6GB run StableLM 2 12B for coding?

For coding workloads, StableLM 2 12B on GTX 1660 Super 6GB receives a F grade with 3.0 tok/s and 4K context.

What context window can StableLM 2 12B use on GTX 1660 Super 6GB?

On GTX 1660 Super 6GB, 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.

What should I upgrade first if StableLM 2 12B feels slow on GTX 1660 Super 6GB?

Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.

See all results for GTX 1660 Super 6GB See all hardware for StableLM 2 12B

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