Will It Run AI

Can Mistral Nemo 12B run on RX 580 8GB?

YES — With Q2_K

C50Usable
Estimated from fit model

Mistral Nemo 12B needs ~8.8 GB VRAM. RX 580 8GB has 8.0 GB. With Q2_K quantization, expect ~13 tok/s.

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

Mistral Nemo 12B at Q4_K_M needs 11.5 GB — too much for RX 580 8GB (8.0 GB). Runs at Q2_K (8.8 GB) with low quality.
Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 11.5 GB, exceeds 8.0 GB available
11.5 GB required8.0 GB available
144% VRAM needed

3.5 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

5.4 tok/s

TTFT

36160 ms

Safe context

4K

Memory

11.5 GB / 8.0 GB

Offload

30%

Memory breakdown

Weights7.3 GB
KV Cache2.4 GB
Runtime0.9 GB
Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower
See how fast it feelsMistral Nemo 12B 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: 5.4 tok/s decode · 36.2s TTFT (warm) · 13 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 0.4 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatFToo heavy6.9 tok/s15402 ms4K
CodingFToo heavy5.0 tok/s38872 ms4K
Agentic CodingFToo heavy3.5 tok/s80375 ms4K
ReasoningFToo heavy5.4 tok/s42734 ms4K
RAGFToo heavy3.5 tok/s100469 ms4K

Quantization options

How Mistral Nemo 12B (12B params) fits at each quantization level on RX 580 8GB (8.0 GB usable).

QuantBitsVRAMQualityFit
Q2_KBest for your GPU
2
4.7 GB
LowB65
Q3_K_S
3
5.9 GB
LowF0
NVFP4
4
6.7 GB
MediumF0
Q4_K_M
4
7.3 GB
MediumF0
Q5_K_M
5
8.6 GB
HighF0
Q6_K
6
9.8 GB
HighF0
Q8_0
8
12.8 GB
Very HighF0
F16
16
24.6 GB
MaximumF0

Get started

Copy-paste commands to run Mistral Nemo 12B on your machine.

Run

ollama run mistral-nemo

Opciones de mejora

Hardware que ejecuta bien Mistral Nemo 12B

RX 7600 XT 16GBOpción económica
16 GB VRAM (+8)288 GB/s (+32)
B
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.24.5 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$329 MSRP

RX 9060 XT 16GBMejor relación calidad-precio
16 GB VRAM (+8)320 GB/s (+64)
B
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.29.6 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$349 MSRP

RX 7700 XT 12GBMejora AMD
12 GB VRAM (+4)432 GB/s (+176)
B
Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.38.1 tok/s decodificación

Hace que el modelo quepa en el acelerador en lugar de seguir fuera de alcance.

Elimina el offload a memoria del sistema, que suele ser la mayor mejora individual en latencia y throughput.

~$449 MSRP

Frequently asked questions

Can RX 580 8GB run Mistral Nemo 12B?

Yes, RX 580 8GB can run Mistral Nemo 12B at Q2_K quantization (Very compromised (needs ~0.4 GB host RAM)). The recommended Q4_K_M requires 11.5 GB which exceeds available memory, but at Q2_K it needs only 8.8 GB. Expected decode speed: 12.6 tok/s.

How much VRAM does Mistral Nemo 12B need?

Mistral Nemo 12B (12B parameters) requires approximately 11.5 GB at Q4_K_M quantization. On RX 580 8GB, it fits at Q2_K using 8.8 GB.

What is the best quantization for Mistral Nemo 12B?

The recommended quantization is Q4_K_M, but on RX 580 8GB the best fitting quantization is Q2_K, which uses 8.8 GB.

What speed will Mistral Nemo 12B run at on RX 580 8GB?

On RX 580 8GB, Mistral Nemo 12B achieves approximately 12.6 tokens per second decode speed with a time-to-first-token of 15306ms using Q2_K quantization.

Can RX 580 8GB run Mistral Nemo 12B for coding?

For coding workloads, Mistral Nemo 12B on RX 580 8GB receives a F grade with 5.0 tok/s and 4K context.

What context window can Mistral Nemo 12B use on RX 580 8GB?

On RX 580 8GB, Mistral Nemo 12B can safely use up to 11K tokens of context at Q2_K quantization. The model's official context limit is 128K, but available memory constrains the safe maximum.

What should I upgrade first if Mistral Nemo 12B feels slow on RX 580 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 580 8GBSee all hardware for Mistral Nemo 12B
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