Can LLaVA 1.5 7B run on RX 580 8GB?

NO — Won't Fit

F0Won't run

Estimated from fit model

LLaVA 1.5 7B needs ~13.8 GB but RX 580 8GB only has 8.0 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

Q4_K_M (Medium quality) — 13.8 GB, exceeds 8.0 GB available

13.8 GB required8.0 GB available

173% VRAM needed

5.8 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.7 tok/s

TTFT

33996 ms

Safe context

Memory

13.8 GB / 8.0 GB

Offload

40%

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsLLaVA 1.5 7B 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: 5.7 tok/s decode · 34.0s TTFT (warm) · 14 tok/s prefill

What limits this setup

Usable VRAM is the main blocker for this model.

Not enough usable memory

The model needs 13.8 GB, but this setup only exposes 8.0 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	11.8 tok/s	8920 ms	4K
Coding	F	Too heavy	5.7 tok/s	33996 ms	4K
Agentic Coding	F	Too heavy	3.9 tok/s	72822 ms	4K
Reasoning	F	Too heavy	5.7 tok/s	40177 ms	4K
RAG	F	Too heavy	3.9 tok/s	91027 ms	4K

Quantization options

How LLaVA 1.5 7B (7B params) fits at each quantization level on RX 580 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	A71
Q3_K_S	3	3.4 GB	Low	A72
NVFP4	4

Upgrade options

Hardware that runs LLaVA 1.5 7B well

RX 7600 XT 16GBBudget pick

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

Makes the model fit on the accelerator instead of staying completely out of reach.39.1 tok/s decode

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.

~$329 MSRP

RX 9060 XT 16GBBest value

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

Makes the model fit on the accelerator instead of staying completely out of reach.47.2 tok/s decode

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.

~$349 MSRP

RX 7700 XT 12GBAMD upgrade

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

Makes the model fit on the accelerator instead of staying completely out of reach.32 tok/s decode

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

Raises estimated decode speed by about 461%.

~$449 MSRP

RX 7900 XT 20GBBiggest leap

20 GB VRAM (+12)800 GB/s (+544)

Makes the model fit on the accelerator instead of staying completely out of reach.98 tok/s decode

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.

~$899 MSRP

Frequently asked questions

Can RX 580 8GB run LLaVA 1.5 7B?

No, LLaVA 1.5 7B requires more memory than RX 580 8GB provides.

How much VRAM does LLaVA 1.5 7B need?

LLaVA 1.5 7B (7B parameters) requires approximately 13.8 GB of memory with Q4_K_M quantization.

What is the best quantization for LLaVA 1.5 7B?

The recommended quantization for LLaVA 1.5 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will LLaVA 1.5 7B run at on RX 580 8GB?

On RX 580 8GB, LLaVA 1.5 7B achieves approximately 5.7 tokens per second decode speed with a time-to-first-token of 33996ms using Q4_K_M quantization.

Can RX 580 8GB run LLaVA 1.5 7B for coding?

For coding workloads, LLaVA 1.5 7B on RX 580 8GB receives a F grade with 5.7 tok/s and 4K context.

What context window can LLaVA 1.5 7B use on RX 580 8GB?

On RX 580 8GB, LLaVA 1.5 7B 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 LLaVA 1.5 7B feels slow on RX 580 8GB?

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 RX 580 8GB See all hardware for LLaVA 1.5 7B

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