Can Llama 3 8B Instruct 32k v0.1 run on RX 5600 XT 6GB?

Q: What should I upgrade first if Llama 3 8B Instruct 32k v0.1 feels slow on RX 5600 XT 6GB?

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.

YES — With NVFP4

D39Poor

Estimated from fit model

Llama 3 8B Instruct 32k v0.1 needs ~6.9 GB VRAM. RX 5600 XT 6GB has 6.0 GB. With NVFP4 quantization, expect ~20 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.

Llama 3 8B Instruct 32k v0.1 at Q4_K_M needs 7.3 GB — too much for RX 5600 XT 6GB (6.0 GB). Runs at NVFP4 (6.9 GB) with medium quality. 3 quantization levels fit.

Capabilities:

Select quantization to explore

Q4_K_M (Medium quality) — 7.3 GB, exceeds 6.0 GB available

7.3 GB required6.0 GB available

122% VRAM needed

1.3 GB over capacity — needs offload or smaller quantization

Fit status

Too heavy

Decode

15.2 tok/s

TTFT

12774 ms

Safe context

Memory

7.3 GB / 6.0 GB

Offload

20%

Memory breakdown

Weights4.9 GB

KV Cache0.9 GB

Runtime0.9 GB

Headroom0.6 GB

See how fast it feels

With memory offload — actual speed may be lower

See how fast it feelsLlama 3 8B Instruct 32k v0.1 on RX 5600 XT 6GB
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: 15.2 tok/s decode · 12.8s 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 0.6 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	D	Very compromised (needs ~0.6 GB host RAM)	17.4 tok/s	6061 ms	4K
Coding	F	Too heavy	15.2 tok/s	12774 ms	4K
Agentic Coding	F	Too heavy	11.8 tok/s	23948 ms	4K
Reasoning	F	Too heavy	15.2 tok/s	15097 ms	4K
RAG	F	Too heavy	11.8 tok/s	29935 ms	4K

Quantization options

How Llama 3 8B Instruct 32k v0.1 (8B params) fits at each quantization level on RX 5600 XT 6GB (6.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_KBest for your GPU	2	3.1 GB	Low	C54
Q3_K_S	3	3.9 GB	Low	F0

Get started

Copy-paste commands to run Llama 3 8B Instruct 32k v0.1 on your machine.

Run

lms load hf-maziyarpanahi--llama-3-8b-instruct-32k-v0-1-gguf && lms server start

Upgrade options

Hardware that runs Llama 3 8B Instruct 32k v0.1 well

RX 580 8GBBudget pick

8 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.22.6 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.

~$229 MSRP

RX 9060 8GBBest value

8 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.37.2 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.

~$249 MSRP

RX 7600 8GBAMD upgrade

8 GB VRAM (+2)

Makes the model fit on the accelerator instead of staying completely out of reach.34.2 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.

~$269 MSRP

RTX 3080 10GBBiggest leap

10 GB VRAM (+4)760 GB/s (+472)

Makes the model fit on the accelerator instead of staying completely out of reach.96 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.

~$699 MSRP

Frequently asked questions

Can RX 5600 XT 6GB run Llama 3 8B Instruct 32k v0.1?

Yes, RX 5600 XT 6GB can run Llama 3 8B Instruct 32k v0.1 at NVFP4 quantization (Very compromised (needs ~0.6 GB host RAM)). The recommended Q4_K_M requires 7.3 GB which exceeds available memory, but at NVFP4 it needs only 6.9 GB. Expected decode speed: 19.5 tok/s.

How much VRAM does Llama 3 8B Instruct 32k v0.1 need?

Llama 3 8B Instruct 32k v0.1 (8B parameters) requires approximately 7.3 GB at Q4_K_M quantization. On RX 5600 XT 6GB, it fits at NVFP4 using 6.9 GB.

What is the best quantization for Llama 3 8B Instruct 32k v0.1?

The recommended quantization is Q4_K_M, but on RX 5600 XT 6GB the best fitting quantization is NVFP4, which uses 6.9 GB.

What speed will Llama 3 8B Instruct 32k v0.1 run at on RX 5600 XT 6GB?

On RX 5600 XT 6GB, Llama 3 8B Instruct 32k v0.1 achieves approximately 19.5 tokens per second decode speed with a time-to-first-token of 9923ms using NVFP4 quantization.

Can RX 5600 XT 6GB run Llama 3 8B Instruct 32k v0.1 for coding?

For coding workloads, Llama 3 8B Instruct 32k v0.1 on RX 5600 XT 6GB receives a F grade with 15.2 tok/s and 4K context.

What context window can Llama 3 8B Instruct 32k v0.1 use on RX 5600 XT 6GB?

On RX 5600 XT 6GB, Llama 3 8B Instruct 32k v0.1 can safely use up to 4K tokens of context at NVFP4 quantization. The model's official context limit is —, but available memory constrains the safe maximum.

See all results for RX 5600 XT 6GB See all hardware for Llama 3 8B Instruct 32k v0.1

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