Can stablelm 2 1 6b chat imatrix run on RX 580 8GB?

YES — Runs Great

C53Usable

Estimated from fit model

stablelm 2 1 6b chat imatrix needs ~6.1 GB VRAM. RX 580 8GB has 8.0 GB. With Q4_K_M quantization, expect ~30 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

Q4_K_M (Medium quality) — 6.1 GB, 30.1 tok/s, Runs well

6.1 GB required8.0 GB available

76% VRAM used

Fit status

Runs well

Decode

30.1 tok/s

TTFT

6437 ms

Safe context

60K

Memory

6.1 GB / 8.0 GB

Memory breakdown

Weights3.7 GB

KV Cache0.7 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsstablelm 2 1 6b chat imatrix on RX 580 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: 30.1 tok/s decode · 6.4s TTFT (warm) · 75 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	30.1 tok/s	3511 ms	60K
Coding	C	Runs well	30.1 tok/s	6437 ms	60K
Agentic Coding	C	Tight fit	30.1 tok/s	9363 ms	60K
Reasoning	C	Runs well	30.1 tok/s	7607 ms	60K
RAG	C	Tight fit	30.1 tok/s	11703 ms	60K

Quantization options

How stablelm 2 1 6b chat imatrix (6B params) fits at each quantization level on RX 580 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

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

Upgrade options

Hardware that runs stablelm 2 1 6b chat imatrix well

Intel Arc B570 10GBBudget pick

10 GB VRAM (+2)380 GB/s (+124)

Raises estimated decode speed by about 86%.56.1 tok/s decode

Raises estimated decode speed by about 86%.

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

~$219 MSRP

RTX 5070 12GBBest value

12 GB VRAM (+4)672 GB/s (+416)

Raises estimated decode speed by about 279%.114 tok/s decode

Raises estimated decode speed by about 279%.

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

~$549 MSRP

Frequently asked questions

Can RX 580 8GB run stablelm 2 1 6b chat imatrix?

Yes, RX 580 8GB can run stablelm 2 1 6b chat imatrix with a C grade (Runs well). Expected decode speed: 30.1 tok/s.

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

stablelm 2 1 6b chat imatrix (6B parameters) requires approximately 6.1 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 RX 580 8GB?

On RX 580 8GB, stablelm 2 1 6b chat imatrix achieves approximately 30.1 tokens per second decode speed with a time-to-first-token of 6437ms using Q4_K_M quantization.

Can RX 580 8GB run stablelm 2 1 6b chat imatrix for coding?

For coding workloads, stablelm 2 1 6b chat imatrix on RX 580 8GB receives a C grade with 30.1 tok/s and 60K context.

What context window can stablelm 2 1 6b chat imatrix use on RX 580 8GB?

On RX 580 8GB, stablelm 2 1 6b chat imatrix can safely use up to 60K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 580 8GB See all hardware for stablelm 2 1 6b chat imatrix

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