Can baichuan2 7b chat run on RX 6800 XT 16GB?

YES — Runs Great

C51Usable

○Estimated from fit model

baichuan2 7b chat needs ~7.6 GB VRAM. RX 6800 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~67 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) — 7.6 GB, 67.1 tok/s, Runs well

7.6 GB required16.0 GB available

48% VRAM used

Fit status

Runs well

Decode

67.1 tok/s

TTFT

2883 ms

Safe context

180K

Memory

7.6 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache0.8 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsbaichuan2 7b chat on RX 6800 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: 67.1 tok/s decode · 2.9s TTFT (warm) · 168 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	67.1 tok/s	1573 ms	180K
Coding	C	Runs well	67.1 tok/s	2883 ms	180K
Agentic Coding	C	Runs well	67.1 tok/s	4194 ms	180K
Reasoning	C	Runs well	67.1 tok/s	3407 ms	180K
RAG	C	Runs well	67.1 tok/s	5242 ms	180K

Quantization options

How baichuan2 7b chat (7B params) fits at each quantization level on RX 6800 XT 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	C46
Q3_K_S	3	3.4 GB	Low	C47
NVFP4	4	3.9 GB	Medium	C47
Q4_K_M	4	4.3 GB	Medium	C48
Q5_K_M	5	5.0 GB	High	C48
Q6_K	6	5.7 GB	High	C49
Q8_0Best for your GPU	8	7.5 GB	Very High	C51
F16	16	14.3 GB	Maximum	F0

Get started

Copy-paste commands to run baichuan2 7b chat on your machine.

Run

lms load hf-shaowenchen--baichuan2-7b-chat-gguf && lms server start

Frequently asked questions

See all results for RX 6800 XT 16GB See all hardware for baichuan2 7b chat

URL: https://willitrunai.com/can-run/hf-shaowenchen--baichuan2-7b-chat-gguf-on-rx-6800-xt-16gb

⇱ baichuan2 7b chat on RX 6800 XT 16GB? YES