Can Llama 3.2 11B Vision run on RX 5700 XT 8GB?

YES — With Q3_K_S

C54Usable

Estimated from fit model

Llama 3.2 11B Vision needs ~9.3 GB VRAM. RX 5700 XT 8GB has 8.0 GB. With Q3_K_S quantization, expect ~23 tok/s.

Runtime: OllamaCapacity: OffloadBandwidth: LowStack: BasicBottleneck: 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.

Llama 3.2 11B Vision at Q4_K_M needs 10.7 GB — too much for RX 5700 XT 8GB (8.0 GB). Runs at Q3_K_S (9.3 GB) with low quality. 2 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 10.7 GB, exceeds 8.0 GB available

10.7 GB required8.0 GB available

134% VRAM needed

2.7 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

15.3 tok/s

TTFT

12664 ms

Safe context

Memory

10.7 GB / 8.0 GB

Offload

20%

Memory breakdown

Weights6.7 GB

KV Cache2.0 GB

Runtime1.2 GB

Headroom0.8 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsLlama 3.2 11B Vision on RX 5700 XT 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: 15.3 tok/s decode · 12.7s TTFT (warm) · 38 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.

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 0.8 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	F	Too heavy	18.7 tok/s	5643 ms	4K
Coding	F	Too heavy	15.3 tok/s	12664 ms	4K
Agentic Coding	F	Too heavy	10.7 tok/s	26246 ms	4K
Reasoning	F	Too heavy	15.3 tok/s	14966 ms	4K
RAG	F	Too heavy	10.7 tok/s	32807 ms	4K

Quantization options

How Llama 3.2 11B Vision (11B params) fits at each quantization level on RX 5700 XT 8GB (8.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	4.3 GB	Low	B68
Q3_K_SBest for your GPU	3	5.4 GB	Low	B67
NVFP4	4	6.2 GB	Medium	F0
Q4_K_M	4	6.7 GB	Medium	F0
Q5_K_M	5	7.9 GB	High	F0
Q6_K	6	9.0 GB	High	F0
Q8_0	8	11.8 GB	Very High	F0
F16	16	22.5 GB	Maximum	F0

Get started

Copy-paste commands to run Llama 3.2 11B Vision on your machine.

Run

ollama run llama3.2-vision:11b

Upgrade-Optionen

Hardware, die Llama 3.2 11B Vision gut ausführt

RX 7600 XT 16GBGünstige Wahl

16 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.26.8 tok/s Dekodierung

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.

ca. $329 MSRP

RX 9060 XT 16GBBestes Preis-Leistungs-Verhältnis

16 GB VRAM (+8)

Makes the model fit on the accelerator instead of staying completely out of reach.32.3 tok/s Dekodierung

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.

ca. $349 MSRP

RX 7700 XT 12GBAMD-Upgrade

12 GB VRAM (+4)

Makes the model fit on the accelerator instead of staying completely out of reach.41.5 tok/s Dekodierung

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.

ca. $449 MSRP

Frequently asked questions

Can RX 5700 XT 8GB run Llama 3.2 11B Vision?

Yes, RX 5700 XT 8GB can run Llama 3.2 11B Vision at Q3_K_S quantization (Very compromised (needs ~0.8 GB host RAM)). The recommended Q4_K_M requires 10.7 GB which exceeds available memory, but at Q3_K_S it needs only 9.3 GB. Expected decode speed: 23.4 tok/s.

How much VRAM does Llama 3.2 11B Vision need?

Llama 3.2 11B Vision (11B parameters) requires approximately 10.7 GB at Q4_K_M quantization. On RX 5700 XT 8GB, it fits at Q3_K_S using 9.3 GB.

What is the best quantization for Llama 3.2 11B Vision?

The recommended quantization is Q4_K_M, but on RX 5700 XT 8GB the best fitting quantization is Q3_K_S, which uses 9.3 GB.

What speed will Llama 3.2 11B Vision run at on RX 5700 XT 8GB?

On RX 5700 XT 8GB, Llama 3.2 11B Vision achieves approximately 23.4 tokens per second decode speed with a time-to-first-token of 8282ms using Q3_K_S quantization.

Can RX 5700 XT 8GB run Llama 3.2 11B Vision for coding?

For coding workloads, Llama 3.2 11B Vision on RX 5700 XT 8GB receives a F grade with 15.3 tok/s and 4K context.

What context window can Llama 3.2 11B Vision use on RX 5700 XT 8GB?

On RX 5700 XT 8GB, Llama 3.2 11B Vision can safely use up to 5K tokens of context at Q3_K_S quantization. The model's official context limit is 16K, but available memory constrains the safe maximum.

What should I upgrade first if Llama 3.2 11B Vision feels slow on RX 5700 XT 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 5700 XT 8GB See all hardware for Llama 3.2 11B Vision

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