Can Baichuan 13B run on RX 7900 XTX 24GB?

YES — With Offload

B68Good

○Estimated from fit model

Baichuan 13B needs ~24.9 GB VRAM. RX 7900 XTX 24GB has 24.0 GB. With Q5_K_M quantization, expect ~52 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: 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

Q5_K_M (High quality) — 24.9 GB, 52.4 tok/s, Runs with offload (needs ~0.3 GB host RAM)

24.9 GB required24.0 GB available

104% VRAM needed

0.9 GB over capacity — needs offload or smaller quantization

Fit status

Runs with offload (needs ~0.3 GB host RAM)

Decode

52.4 tok/s

TTFT

3693 ms

Safe context

Memory

24.9 GB / 24.0 GB

Memory breakdown

Weights9.4 GB

KV Cache12.2 GB

Runtime0.9 GB

Headroom2.4 GB

See how fast it feels

See how fast it feelsBaichuan 13B on RX 7900 XTX 24GB
1st promptCold start — includes initialization
>Solve: If a train travels at 60 mph for 2.5 hours, how far does it go?
Let me work through this step by step.

Given information:
 - Speed = 60 mph (miles per hour)
 - Time = 2.5 hours

Formula: Distance = Speed x Time

Calculation:
 Distance = 60 mph x 2.5 h
 = 150 miles

The train travels 150 miles.

Double-check: at 60 mph the train covers 60 miles each hour. In half an hour it covers 30 miles. So in 2.5 hours: 60 + 60 + 30 = 150 miles. Confirmed.
2nd promptWarm — model ready, no init overhead
>What if the train stops for 20 minutes halfway?
The stop doesn't change the total distance covered while moving, but it affects total trip time.

Breakdown:
 - First half: 1.25 hours at 60 mph = 75 miles
 - Stop: 20 minutes (no distance)
 - Second half: 1.25 hours at 60 mph = 75 miles

Total distance: 150 miles (same)
Total time: 2.5 h + 0.33 h = 2.83 hours
Average speed: 150 / 2.83 = 53 mph

The distance stays the same but average speed drops to 53 mph because of the stop.
Estimated: 52.4 tok/s decode · 3.7s TTFT (warm) · 131 tok/s prefill

What limits this setup

This setup is broadly balanced for this model.

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

Buy headroom, not only minimum fit

A slightly larger memory tier gives you safer context growth and makes the recommendation more future-proof.

Performance by workload

Workload	Grade	Fit	Decode	TTFT	Context
Chat	A	Runs well	75.3 tok/s	1402 ms	8K
Coding	B	Runs with offload (needs ~0.3 GB host RAM)	52.4 tok/s	3693 ms	8K
Agentic Coding	F	Too heavy	22.6 tok/s	12451 ms	8K
Reasoning	B	Runs with offload (needs ~0.3 GB host RAM)	52.4 tok/s	4364 ms	8K
RAG	F	Too heavy	22.6 tok/s	15563 ms	8K

Quantization options

How Baichuan 13B (13B params) fits at each quantization level on RX 7900 XTX 24GB (24.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	5.1 GB	Low	B62
Q3_K_S	3	6.4 GB	Low	B62
NVFP4	4	7.3 GB	Medium	B63
Q4_K_M	4	7.9 GB	Medium	B63
Q5_K_M	5	9.4 GB	High	B64
Q6_K	6	10.7 GB	High	B65
Q8_0Best for your GPU	8	13.9 GB	Very High	B66
F16	16	26.7 GB	Maximum	F0

Get started

Copy-paste commands to run Baichuan 13B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
 --hf-repo "baichuan-inc/Baichuan-13B-Chat" \
 --hf-file "Baichuan-13B-Chat-Q5_K_M.gguf" \
 -c 4096 -ngl 99

Upgrade options