Can dolphin v2 8b abliterated i1 run on RX 580 8GB?

Q: What should I upgrade first if dolphin v2 8b abliterated i1 feels slow on RX 580 8GB?

Buy headroom, not only minimum fit. A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

YES — Tight Fit

C49Usable

Estimated from fit model

dolphin v2 8b abliterated i1 needs ~7.5 GB VRAM. RX 580 8GB has 8.0 GB. With Q4_K_M quantization, expect ~23 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 7.5 GB, 22.6 tok/s, Tight fit

7.5 GB required8.0 GB available

94% VRAM used

Fit status

Tight fit

Decode

22.6 tok/s

TTFT

8583 ms

Safe context

24K

Memory

7.5 GB / 8.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsdolphin v2 8b abliterated i1 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: 22.6 tok/s decode · 8.6s TTFT (warm) · 56 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

Very little memory headroom

You can run the model, but there is not much room left for longer context, bigger batches, extra apps, or future model updates.

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

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Tight fit	22.6 tok/s	4681 ms	24K
Coding	C	Tight fit	22.6 tok/s	8583 ms	24K
Agentic Coding	D	Runs with offload (needs ~0.3 GB host RAM)	14.6 tok/s	19325 ms	24K
Reasoning	C	Tight fit	22.6 tok/s	10143 ms	24K
RAG	D	Runs with offload (needs ~0.3 GB host RAM)	14.6 tok/s	24157 ms

Quantization options

How dolphin v2 8b abliterated i1 (8B params) fits at each quantization level on RX 580 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C54
Q3_K_S	3	3.9 GB	Low	C53
NVFP4	4

Get started

Copy-paste commands to run dolphin v2 8b abliterated i1 on your machine.

Run

lms load hf-mradermacher--dolphin-v2-8b-abliterated-i1-gguf && lms server start

Upgrade options

Hardware that runs dolphin v2 8b abliterated i1 well

RX 7600 XT 16GBBudget pick

16 GB VRAM (+8)288 GB/s (+32)

Raises estimated decode speed by about 51%.34.2 tok/s decode

Raises estimated decode speed by about 51%.

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

~$329 MSRP

RX 9060 XT 16GBBest value

16 GB VRAM (+8)320 GB/s (+64)

Raises estimated decode speed by about 83%.41.3 tok/s decode

Raises estimated decode speed by about 83%.

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

~$349 MSRP

RX 7700 XT 12GBAMD upgrade

12 GB VRAM (+4)432 GB/s (+176)

Raises estimated decode speed by about 135%.53.1 tok/s decode

Raises estimated decode speed by about 135%.

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

~$449 MSRP

Frequently asked questions

Can RX 580 8GB run dolphin v2 8b abliterated i1?

Yes, RX 580 8GB can run dolphin v2 8b abliterated i1 with a C grade (Tight fit). Expected decode speed: 22.6 tok/s.

How much VRAM does dolphin v2 8b abliterated i1 need?

dolphin v2 8b abliterated i1 (8B parameters) requires approximately 7.5 GB of memory with Q4_K_M quantization.

What is the best quantization for dolphin v2 8b abliterated i1?

The recommended quantization for dolphin v2 8b abliterated i1 is Q4_K_M, which balances quality and memory efficiency.

What speed will dolphin v2 8b abliterated i1 run at on RX 580 8GB?

On RX 580 8GB, dolphin v2 8b abliterated i1 achieves approximately 22.6 tokens per second decode speed with a time-to-first-token of 8583ms using Q4_K_M quantization.

Can RX 580 8GB run dolphin v2 8b abliterated i1 for coding?

For coding workloads, dolphin v2 8b abliterated i1 on RX 580 8GB receives a C grade with 22.6 tok/s and 24K context.

What context window can dolphin v2 8b abliterated i1 use on RX 580 8GB?

On RX 580 8GB, dolphin v2 8b abliterated i1 can safely use up to 24K 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 dolphin v2 8b abliterated i1

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