Can Meta Llama 3.1 8B Instruct run on RX 9060 XT 16GB?

YES — Runs Great

C51Usable

Estimated from fit model

Meta Llama 3.1 8B Instruct needs ~8.3 GB VRAM. RX 9060 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~41 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) — 8.3 GB, 41.3 tok/s, Runs well

8.3 GB required16.0 GB available

52% VRAM used

Fit status

Runs well

Decode

41.3 tok/s

TTFT

4686 ms

Safe context

147K

Memory

8.3 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsMeta Llama 3.1 8B Instruct on RX 9060 XT 16GB
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: 41.3 tok/s decode · 4.7s TTFT (warm) · 103 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

No major red flags

This recommendation has enough memory headroom and acceptable estimated speed for the selected workload.

Best improvement path

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	C	Runs well	41.3 tok/s	2556 ms	147K
Coding	C	Runs well	41.3 tok/s	4686 ms	147K
Agentic Coding	C	Runs well	41.3 tok/s	6817 ms	147K
Reasoning	C	Runs well	41.3 tok/s	5538 ms	147K
RAG	C	Runs well	41.3 tok/s	8521 ms	147K

Quantization options

How Meta Llama 3.1 8B Instruct (8B params) fits at each quantization level on RX 9060 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	C47
Q3_K_S	3	3.9 GB	Low	C48
NVFP4	4	4.5 GB	Medium	C49
Q4_K_M	4	4.9 GB	Medium	C49
Q5_K_M	5	5.8 GB	High	C50
Q6_K	6	6.6 GB	High	C51
Q8_0Best for your GPU	8	8.6 GB	Very High	C52
F16	16	16.4 GB	Maximum	F0

Get started

Copy-paste commands to run Meta Llama 3.1 8B Instruct on your machine.

Run

lms load hf-bartowski--meta-llama-3-1-8b-instruct-gguf && lms server start

Opciones de mejora

Hardware que ejecuta bien Meta Llama 3.1 8B Instruct

RX 7900 XT 20GBOpción económica

20 GB VRAM (+4)800 GB/s (+480)

Sube la velocidad estimada de decodificación alrededor de un 138%.98.4 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 138%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$899 MSRP

RTX A4500 20GBMejor relación calidad-precio

20 GB VRAM (+4)640 GB/s (+320)

Sube la velocidad estimada de decodificación alrededor de un 148%.102.3 tok/s decodificación

Sube la velocidad estimada de decodificación alrededor de un 148%.

Añade margen de memoria para más contexto y para que el modelo envejezca mejor.

~$2,000 MSRP

Frequently asked questions

Can RX 9060 XT 16GB run Meta Llama 3.1 8B Instruct?

Yes, RX 9060 XT 16GB can run Meta Llama 3.1 8B Instruct with a C grade (Runs well). Expected decode speed: 41.3 tok/s.

How much VRAM does Meta Llama 3.1 8B Instruct need?

Meta Llama 3.1 8B Instruct (8B parameters) requires approximately 8.3 GB of memory with Q4_K_M quantization.

What is the best quantization for Meta Llama 3.1 8B Instruct?

The recommended quantization for Meta Llama 3.1 8B Instruct is Q4_K_M, which balances quality and memory efficiency.

What speed will Meta Llama 3.1 8B Instruct run at on RX 9060 XT 16GB?

On RX 9060 XT 16GB, Meta Llama 3.1 8B Instruct achieves approximately 41.3 tokens per second decode speed with a time-to-first-token of 4686ms using Q4_K_M quantization.

Can RX 9060 XT 16GB run Meta Llama 3.1 8B Instruct for coding?

For coding workloads, Meta Llama 3.1 8B Instruct on RX 9060 XT 16GB receives a C grade with 41.3 tok/s and 147K context.

What context window can Meta Llama 3.1 8B Instruct use on RX 9060 XT 16GB?

On RX 9060 XT 16GB, Meta Llama 3.1 8B Instruct can safely use up to 147K tokens of context. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 9060 XT 16GB See all hardware for Meta Llama 3.1 8B Instruct

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