Can Ministral 8B run on RX 590 8GB?

BARELY — Tight on Memory

C47Usable

Estimated from fit model

Ministral 8B needs ~8.8 GB VRAM. RX 590 8GB has 8.0 GB. With Q4_K_M quantization, expect ~13 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload

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) — 8.8 GB, 14.4 tok/s, Very compromised (needs ~0.4 GB host RAM)

8.8 GB required8.0 GB available

110% VRAM needed

0.8 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.4 GB host RAM)

Decode

14.4 tok/s

TTFT

13417 ms

Safe context

10K

Memory

8.8 GB / 8.0 GB

Offload

10%

Memory breakdown

Weights4.9 GB

KV Cache2.2 GB

Runtime0.9 GB

Headroom0.8 GB

See how fast it feels

See how fast it feelsMinistral 8B on RX 590 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: 14.4 tok/s decode · 13.4s TTFT (warm) · 36 tok/s prefill

What limits this setup

It fits through host-memory offload, and offload is the main reason performance drops.

CPU or host-memory offload is active

About 10% of the working set spills out of accelerator memory, which usually hurts latency and sustained decode throughput.

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

Remove offload with more accelerator memory

Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

Buy headroom, not only minimum fit

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

Increase host RAM if you keep offloading

This setup may need roughly {ram} GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	B	Runs with offload	24.2 tok/s	4355 ms	10K
Coding	C	Very compromised	13.4 tok/s	14424 ms	10K
Agentic Coding	F	Too heavy	8.8 tok/s	31876 ms	10K
Reasoning	C	Very compromised (needs ~0.4 GB host RAM)	14.4 tok/s	15857 ms	10K
RAG	F	Too heavy	8.8 tok/s	39845 ms	10K

Quantization options

How Ministral 8B (8B params) fits at each quantization level on RX 590 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	B63
Q3_K_S	3	3.9 GB	Low	B63
NVFP4	4

Get started

Copy-paste commands to run Ministral 8B on your machine.

Run

ollama run ministral

Upgrade options

Hardware that runs Ministral 8B well

RX 7600 XT 16GBBudget pick

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

Removes host-memory offload, which is usually the single biggest latency and throughput win.36.8 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 156%.

~$329 MSRP

RX 9060 XT 16GBBest value

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

Removes host-memory offload, which is usually the single biggest latency and throughput win.44.4 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 208%.

~$349 MSRP

RX 7700 XT 12GBAMD upgrade

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

Removes host-memory offload, which is usually the single biggest latency and throughput win.57.1 tok/s decode

Removes host-memory offload, which is usually the single biggest latency and throughput win.

Raises estimated decode speed by about 297%.

~$449 MSRP

Frequently asked questions

Can RX 590 8GB run Ministral 8B?

Yes, RX 590 8GB can run Ministral 8B with a C grade (Very compromised). Expected decode speed: 13.4 tok/s.

How much VRAM does Ministral 8B need?

Ministral 8B (8B parameters) requires approximately 8.8 GB of memory with Q4_K_M quantization.

What is the best quantization for Ministral 8B?

The recommended quantization for Ministral 8B is Q4_K_M, which balances quality and memory efficiency.

What speed will Ministral 8B run at on RX 590 8GB?

On RX 590 8GB, Ministral 8B achieves approximately 13.4 tokens per second decode speed with a time-to-first-token of 14424ms using Q4_K_M quantization.

Can RX 590 8GB run Ministral 8B for coding?

For coding workloads, Ministral 8B on RX 590 8GB receives a C grade with 13.4 tok/s and 10K context.

What context window can Ministral 8B use on RX 590 8GB?

On RX 590 8GB, Ministral 8B can safely use up to 10K tokens of context. The model's official context limit is 131K, but available memory constrains the safe maximum.

What should I upgrade first if Ministral 8B feels slow on RX 590 8GB?

Remove offload with more accelerator memory. Prioritize a GPU or unified-memory tier that fits the whole model natively. Removing offload usually helps more than small compute gains.

See all results for RX 590 8GB See all hardware for Ministral 8B

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