Will It Run AI

Can Baichuan 7B run on RX 6750 XT 12GB?

BARELY — Tight on Memory

C50Usable
Estimated from fit model

Baichuan 7B needs ~14.2 GB VRAM. RX 6750 XT 12GB has 12.0 GB. With Q4_K_M quantization, expect ~28 tok/s.

Runtime: llama.cppCapacity: OffloadBandwidth: LowStack: StandardBottleneck: Host offload
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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) 14.2 GB, 28.3 tok/s, Very compromised (needs ~0.7 GB host RAM)
14.2 GB required12.0 GB available
118% VRAM needed

2.2 GB over capacity — needs offload or smaller quantization

Fit status

Very compromised (needs ~0.7 GB host RAM)

Decode

28.3 tok/s

TTFT

6843 ms

Safe context

8K

Memory

14.2 GB / 12.0 GB

Offload

20%

Memory breakdown

Weights4.3 GB
KV Cache7.8 GB
Runtime0.9 GB
Headroom1.2 GB

See how fast it feels

See how fast it feelsBaichuan 7B on RX 6750 XT 12GB
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: 28.3 tok/s decode · 6.8s TTFT (warm) · 71 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 20% 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 0.7 GB of extra host RAM just for the offloaded portion, before OS and other tools.

Performance by workload

WorkloadGradeFitDecodeTTFTContext
ChatBTight fit53.6 tok/s1969 ms8K
CodingCVery compromised (needs ~0.7 GB host RAM)28.3 tok/s6843 ms8K
Agentic CodingFToo heavy11.2 tok/s25070 ms8K
ReasoningCVery compromised (needs ~0.7 GB host RAM)28.3 tok/s8088 ms8K
RAGFToo heavy11.2 tok/s31338 ms8K

Quantization options

How Baichuan 7B (7B params) fits at each quantization level on RX 6750 XT 12GB (12.0 GB usable).

QuantBitsVRAMQualityFit
Q2_K
2
2.7 GB
LowB65
Q3_K_S
3
3.4 GB
LowB66
NVFP4
4
3.9 GB
MediumB67
Q4_K_M
4
4.3 GB
MediumB67
Q5_K_M
5
5.0 GB
HighB68
Q6_K
6
5.7 GB
HighB69
Q8_0Best for your GPU
8
7.5 GB
Very HighB68
F16
16
14.3 GB
MaximumF0

Get started

Copy-paste commands to run Baichuan 7B on your machine.

Run

docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \ --hf-repo "baichuan-inc/Baichuan-7B" \ --hf-file "Baichuan-7B-Q4_K_M.gguf" \ -c 4096 -ngl 99

Opções de upgrade

Hardware que roda bem Baichuan 7B

Frequently asked questions

Can RX 6750 XT 12GB run Baichuan 7B?

Yes, RX 6750 XT 12GB can run Baichuan 7B with a C grade (Very compromised (needs ~0.7 GB host RAM)). Expected decode speed: 28.3 tok/s.

How much VRAM does Baichuan 7B need?

Baichuan 7B (7B parameters) requires approximately 14.2 GB of memory with Q4_K_M quantization.

What is the best quantization for Baichuan 7B?

The recommended quantization for Baichuan 7B is Q4_K_M, which balances quality and memory efficiency.

What speed will Baichuan 7B run at on RX 6750 XT 12GB?

On RX 6750 XT 12GB, Baichuan 7B achieves approximately 28.3 tokens per second decode speed with a time-to-first-token of 6843ms using Q4_K_M quantization.

Can RX 6750 XT 12GB run Baichuan 7B for coding?

For coding workloads, Baichuan 7B on RX 6750 XT 12GB receives a C grade with 28.3 tok/s and 8K context.

What context window can Baichuan 7B use on RX 6750 XT 12GB?

On RX 6750 XT 12GB, Baichuan 7B can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.

What should I upgrade first if Baichuan 7B feels slow on RX 6750 XT 12GB?

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.

See all results for RX 6750 XT 12GBSee all hardware for Baichuan 7B
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