Can Baichuan 7B run on Radeon RX 7900M 16GB?

YES — Tight Fit

B70Good

Estimated from fit model

Baichuan 7B needs ~14.6 GB VRAM. Radeon RX 7900M 16GB has 16.0 GB. With Q4_K_M quantization, expect ~80 tok/s.

Runtime: llama.cppCapacity: TightBandwidth: 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) — 14.6 GB, 79.6 tok/s, Tight fit

14.6 GB required16.0 GB available

91% VRAM used

Fit status

Tight fit

Decode

79.6 tok/s

TTFT

2433 ms

Safe context

Memory

14.6 GB / 16.0 GB

Memory breakdown

Weights4.3 GB

KV Cache7.8 GB

Runtime0.9 GB

Headroom1.6 GB

See how fast it feels

See how fast it feelsBaichuan 7B on Radeon RX 7900M 16GB
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: 79.6 tok/s decode · 2.4s TTFT (warm) · 199 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	79.6 tok/s	1327 ms	8K
Coding	B	Tight fit	79.6 tok/s	2433 ms	8K
Agentic Coding	F	Too heavy	29.4 tok/s	9575 ms	8K
Reasoning	B	Tight fit	79.6 tok/s	2875 ms	8K
RAG	F	Too heavy	29.4 tok/s	11968 ms	8K

Quantization options

How Baichuan 7B (7B params) fits at each quantization level on Radeon RX 7900M 16GB (16.0 GB usable).

Quant	Bits	VRAM	Quality	Fit
Q2_K	2	2.7 GB	Low	B63
Q3_K_S	3	3.4 GB	Low	B63
NVFP4	4

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

Upgrade options

Hardware that runs Baichuan 7B well

RX 7900 XT 20GBBudget pick

20 GB VRAM (+4)800 GB/s (+224)

Adds memory headroom for longer context windows and future model growth.98 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$899 MSRP

RX 7900 XTX 24GBBest value

24 GB VRAM (+8)960 GB/s (+384)

Adds memory headroom for longer context windows and future model growth.98 tok/s decode

Adds memory headroom for longer context windows and future model growth.

~$999 MSRP

Frequently asked questions

Can Radeon RX 7900M 16GB run Baichuan 7B?

Yes, Radeon RX 7900M 16GB can run Baichuan 7B with a B grade (Tight fit). Expected decode speed: 79.6 tok/s.

How much VRAM does Baichuan 7B need?

Baichuan 7B (7B parameters) requires approximately 14.6 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 Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, Baichuan 7B achieves approximately 79.6 tokens per second decode speed with a time-to-first-token of 2433ms using Q4_K_M quantization.

Can Radeon RX 7900M 16GB run Baichuan 7B for coding?

For coding workloads, Baichuan 7B on Radeon RX 7900M 16GB receives a B grade with 79.6 tok/s and 8K context.

What context window can Baichuan 7B use on Radeon RX 7900M 16GB?

On Radeon RX 7900M 16GB, 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.

See all results for Radeon RX 7900M 16GB See all hardware for Baichuan 7B

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