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

YES — Runs Great

C53Usable

Estimated from fit model

stablelm 2 zephyr 1 6b 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 zephyr 1 6b 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

Inference speed

stablelm 2 zephyr 1 6b inference speed — tokens per second by GPU & Mac

Estimated decode speed (tokens/sec) for stablelm 2 zephyr 1 6b at Q4_K_M across popular GPUs and Apple Silicon, using the fastest local runtime per device. Fastest is RTX 5090 32GB at ~114 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	Q4_K_M	114.0	Fits
RTX 4090 24GB	24 GB	Q4_K_M	84.0	Fits
RTX 4080 Super 16GB	16 GB	Q4_K_M	84.0

Quantization options

How stablelm 2 zephyr 1 6b (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	C54
NVFP4	4

Get started

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

Run

lms load hf-stabilityai--stablelm-2-zephyr-1-6b && lms server start

Upgrade options

Hardware that runs stablelm 2 zephyr 1 6b 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 zephyr 1 6b?

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

How much VRAM does stablelm 2 zephyr 1 6b need?

stablelm 2 zephyr 1 6b (6B parameters) requires approximately 6.1 GB of memory with Q4_K_M quantization.

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

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

What speed will stablelm 2 zephyr 1 6b run at on RX 580 8GB?

On RX 580 8GB, stablelm 2 zephyr 1 6b 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 zephyr 1 6b for coding?

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

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

On RX 580 8GB, stablelm 2 zephyr 1 6b 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 zephyr 1 6b

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