Can Llama 3.1 8B run on RX 7900 XT 20GB?

YES — Runs Great

A73Great

Estimated from fit model

Llama 3.1 8B needs ~9.7 GB VRAM. RX 7900 XT 20GB has 20.0 GB. With Q4_K_M quantization, expect ~98 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: HighStack: 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) — 9.7 GB, 105.7 tok/s, Runs well

9.7 GB required20.0 GB available

49% VRAM used

Fit status

Runs well

Decode

105.7 tok/s

TTFT

1831 ms

Safe context

100K

Memory

9.7 GB / 20.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom2.0 GB

See how fast it feels

See how fast it feelsLlama 3.1 8B on RX 7900 XT 20GB
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: 105.7 tok/s decode · 1.8s TTFT (warm) · 264 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	A	Runs well	105.7 tok/s	999 ms	100K
Coding	A	Runs well	98.4 tok/s	1968 ms	100K
Agentic Coding	A	Runs well	105.7 tok/s	2663 ms	100K
Reasoning	A	Runs well	105.7 tok/s	2164 ms	100K
RAG	A	Runs well	105.7 tok/s	3329 ms	100K

Quantization options

How Llama 3.1 8B (8B params) fits at each quantization level on RX 7900 XT 20GB (20.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	B67
Q3_K_S	3	3.9 GB	Low	B67
NVFP4	4	4.5 GB	Medium	B67
Q4_K_M	4	4.9 GB	Medium	B68
Q5_K_M	5	5.8 GB	High	B68
Q6_K	6	6.6 GB	High	B69
Q8_0Best for your GPU	8	8.6 GB	Very High	A71
F16	16	16.4 GB	Maximum	F0

Get started

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

Run

ollama run llama3.1

Your hardware

More models your RX 7900 XT 20GB can run

Model	Params	Grade	Decode
Qwen3-Coder 30B A3B Instruct	30.5B	A	40.7 tok/s
Qwen 3.5 27B	27B	A	18.3 tok/s
Qwen 3.6 27B	27B	S	17.3 tok/s
Qwen3-VL 30B A3B Instruct	30B	A	43.3 tok/s
Qwen 3.5 9B	9B	S	94 tok/s

Frequently asked questions

Can RX 7900 XT 20GB run Llama 3.1 8B?

Yes, RX 7900 XT 20GB can run Llama 3.1 8B with a A grade (Runs well). Expected decode speed: 98.4 tok/s.

How much VRAM does Llama 3.1 8B need?

Llama 3.1 8B (8B parameters) requires approximately 9.7 GB of memory with Q4_K_M quantization.

What is the best quantization for Llama 3.1 8B?

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

What speed will Llama 3.1 8B run at on RX 7900 XT 20GB?

On RX 7900 XT 20GB, Llama 3.1 8B achieves approximately 98.4 tokens per second decode speed with a time-to-first-token of 1968ms using Q4_K_M quantization.

Can RX 7900 XT 20GB run Llama 3.1 8B for coding?

For coding workloads, Llama 3.1 8B on RX 7900 XT 20GB receives a A grade with 98.4 tok/s and 100K context.

What context window can Llama 3.1 8B use on RX 7900 XT 20GB?

On RX 7900 XT 20GB, Llama 3.1 8B can safely use up to 100K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RX 7900 XT 20GB See all hardware for Llama 3.1 8B

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