Can Llama 3.1 8B run on RX 6800 16GB?

YES — Runs Great

A74Great

Estimated from fit model

Llama 3.1 8B needs ~9.3 GB VRAM. RX 6800 16GB has 16.0 GB. With Q4_K_M quantization, expect ~62 tok/s.

Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 9.3 GB, 62.0 tok/s, Runs well

9.3 GB required16.0 GB available

58% VRAM used

Fit status

Runs well

Decode

62.0 tok/s

TTFT

3121 ms

Safe context

71K

Memory

9.3 GB / 16.0 GB

Memory breakdown

Weights4.9 GB

KV Cache2.0 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsLlama 3.1 8B on RX 6800 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: 62.0 tok/s decode · 3.1s TTFT (warm) · 155 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	62.0 tok/s	1702 ms	71K
Coding	A	Runs well	62.0 tok/s	3121 ms	71K
Agentic Coding	A	Runs well	62.0 tok/s	4540 ms	71K
Reasoning	A	Runs well	62.0 tok/s	3688 ms	71K
RAG	A	Runs well	62.0 tok/s	5674 ms	71K

Quantization options

How Llama 3.1 8B (8B params) fits at each quantization level on RX 6800 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	3.1 GB	Low	B68
Q3_K_S	3	3.9 GB	Low	B69
NVFP4	4	4.5 GB	Medium	B69
Q4_K_M	4	4.9 GB	Medium	B70
Q5_K_M	5	5.8 GB	High	A71
Q6_K	6	6.6 GB	High	A71
Q8_0Best for your GPU	8	8.6 GB	Very High	A72
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 6800 16GB can run

Model	Params	Grade	Decode
Qwen 3.5 9B	9B	S	55.1 tok/s
Qwen 3 14B	14B	S	35.6 tok/s
Phi-4-reasoning-plus 14B	14.7B	S	33.8 tok/s
GPT-OSS 20B	21B	A	32.6 tok/s
Ministral 3 14B	14B	S	35.4 tok/s

Frequently asked questions

Can RX 6800 16GB run Llama 3.1 8B?

Yes, RX 6800 16GB can run Llama 3.1 8B with a A grade (Runs well). Expected decode speed: 62.0 tok/s.

How much VRAM does Llama 3.1 8B need?

Llama 3.1 8B (8B parameters) requires approximately 9.3 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 6800 16GB?

On RX 6800 16GB, Llama 3.1 8B achieves approximately 62.0 tokens per second decode speed with a time-to-first-token of 3121ms using Q4_K_M quantization.

Can RX 6800 16GB run Llama 3.1 8B for coding?

For coding workloads, Llama 3.1 8B on RX 6800 16GB receives a A grade with 62.0 tok/s and 71K context.

What context window can Llama 3.1 8B use on RX 6800 16GB?

On RX 6800 16GB, Llama 3.1 8B can safely use up to 71K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.

See all results for RX 6800 16GB See all hardware for Llama 3.1 8B

Embed this result▼

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<iframe src="https://willitrunai.com/embed/llama-3.1-8b-on-rx-6800-16gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>

Preview:

Workload

Grade

Fit

Decode

TTFT

Context

Chat

Runs well

62.0 tok/s

1702 ms

71K

Coding

Runs well

62.0 tok/s

3121 ms

71K

Agentic Coding

Runs well

62.0 tok/s

4540 ms

71K

Reasoning

Runs well

62.0 tok/s

3688 ms

71K

RAG

Runs well

62.0 tok/s

5674 ms

71K

Quant

Bits

VRAM

Quality

Fit

Q2_K

3.1 GB

Low

B68

Q3_K_S

3.9 GB

Low

B69

NVFP4

4.5 GB

Medium

B69

Q4_K_M

4.9 GB

Medium

B70

Q5_K_M

5.8 GB

High

A71

Q6_K

6.6 GB

High

A71

Q8_0Best for your GPU

8.6 GB

Very High

A72

F16

16.4 GB

Maximum

Model

Params

Grade

Decode

Capabilities

Qwen 3.5 9B

55.1 tok/s

Qwen 3 14B

14B

35.6 tok/s

Phi-4-reasoning-plus 14B

14.7B

33.8 tok/s

GPT-OSS 20B

21B

32.6 tok/s

Ministral 3 14B

14B

35.4 tok/s

Frequently asked questions

Can RX 6800 16GB run Llama 3.1 8B?

Yes, RX 6800 16GB can run Llama 3.1 8B with a A grade (Runs well). Expected decode speed: 62.0 tok/s.

How much VRAM does Llama 3.1 8B need?

Llama 3.1 8B (8B parameters) requires approximately 9.3 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 6800 16GB?

On RX 6800 16GB, Llama 3.1 8B achieves approximately 62.0 tokens per second decode speed with a time-to-first-token of 3121ms using Q4_K_M quantization.

Can RX 6800 16GB run Llama 3.1 8B for coding?

For coding workloads, Llama 3.1 8B on RX 6800 16GB receives a A grade with 62.0 tok/s and 71K context.

What context window can Llama 3.1 8B use on RX 6800 16GB?

On RX 6800 16GB, Llama 3.1 8B can safely use up to 71K tokens of context. The model's official context limit is 128K, but available memory constrains the safe maximum.