Can stablelm 2 1 6b chat imatrix run on RTX 3000 Ada Laptop 8GB?

YES — Runs Great

B55Good

Estimated from fit model

stablelm 2 1 6b chat imatrix needs ~6.4 GB VRAM. RTX 3000 Ada Laptop 8GB has 8.0 GB. With Q4_K_M quantization, expect ~57 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) — 6.4 GB, 57.4 tok/s, Runs well

6.4 GB required8.0 GB available

80% VRAM used

Fit status

Runs well

Decode

57.4 tok/s

TTFT

3370 ms

Safe context

53K

Memory

6.4 GB / 8.0 GB

Memory breakdown

Weights3.7 GB

KV Cache0.7 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsstablelm 2 1 6b chat imatrix on RTX 3000 Ada Laptop 8GB
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: 57.4 tok/s decode · 3.4s TTFT (warm) · 144 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	B	Runs well	57.4 tok/s	1838 ms	53K
Coding	B	Runs well	57.4 tok/s	3370 ms	53K
Agentic Coding	C	Tight fit	57.4 tok/s	4902 ms	53K
Reasoning	B	Runs well	57.4 tok/s	3983 ms	53K
RAG	C	Tight fit	57.4 tok/s	6128 ms	53K

Quantization options

How stablelm 2 1 6b chat imatrix (6B params) fits at each quantization level on RTX 3000 Ada Laptop 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.3 GB	Low	C52
Q3_K_S	3	2.9 GB	Low	C53
NVFP4	4	3.4 GB	Medium	C53
Q4_K_M	4	3.7 GB	Medium	C53
Q5_K_M	5	4.3 GB	High	C53
Q6_KBest for your GPU	6	4.9 GB	High	C53
Q8_0	8	6.4 GB	Very High	F0
F16	16	12.3 GB	Maximum	F0

Get started

Copy-paste commands to run stablelm 2 1 6b chat imatrix on your machine.

Run

lms load hf-crataco--stablelm-2-1-6b-chat-imatrix-gguf && lms server start

Opções de upgrade

Hardware que roda bem stablelm 2 1 6b chat imatrix

RTX 3080 10GBOpção econômica

10 GB VRAM (+2)760 GB/s (+472)

Raises estimated decode speed by about 46%.84 tok/s decodificação

Raises estimated decode speed by about 46%.

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

~$699 MSRP

Frequently asked questions

Can RTX 3000 Ada Laptop 8GB run stablelm 2 1 6b chat imatrix?

Yes, RTX 3000 Ada Laptop 8GB can run stablelm 2 1 6b chat imatrix with a B grade (Runs well). Expected decode speed: 57.4 tok/s.

How much VRAM does stablelm 2 1 6b chat imatrix need?

stablelm 2 1 6b chat imatrix (6B parameters) requires approximately 6.4 GB of memory with Q4_K_M quantization.

What is the best quantization for stablelm 2 1 6b chat imatrix?

The recommended quantization for stablelm 2 1 6b chat imatrix is Q4_K_M, which balances quality and memory efficiency.

What speed will stablelm 2 1 6b chat imatrix run at on RTX 3000 Ada Laptop 8GB?

On RTX 3000 Ada Laptop 8GB, stablelm 2 1 6b chat imatrix achieves approximately 57.4 tokens per second decode speed with a time-to-first-token of 3370ms using Q4_K_M quantization.

Can RTX 3000 Ada Laptop 8GB run stablelm 2 1 6b chat imatrix for coding?

For coding workloads, stablelm 2 1 6b chat imatrix on RTX 3000 Ada Laptop 8GB receives a B grade with 57.4 tok/s and 53K context.

What context window can stablelm 2 1 6b chat imatrix use on RTX 3000 Ada Laptop 8GB?

On RTX 3000 Ada Laptop 8GB, stablelm 2 1 6b chat imatrix can safely use up to 53K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RTX 3000 Ada Laptop 8GB See all hardware for stablelm 2 1 6b chat imatrix

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