RX 7900 XT 20GBBudget pick
20 GB VRAM (+4)800 GB/s (+512)
A
Raises estimated decode speed by about 151%.98 tok/s decode
Raises estimated decode speed by about 151%.
Adds memory headroom for longer context windows and future model growth.
~$899 MSRP
Can Baichuan 7B run on RX 7600 XT 16GB?
YES — Tight Fit
B67Good
○Estimated from fit model
Baichuan 7B needs ~14.6 GB VRAM. RX 7600 XT 16GB has 16.0 GB. With Q4_K_M quantization, expect ~39 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: LowStack: StandardBottleneck: Balanced
Share:
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.
Select quantization to explore
Q4_K_M (Medium quality) — 14.6 GB, 39.1 tok/s, Tight fit
14.6 GB required16.0 GB available
91% VRAM used
Fit status
Tight fit
Decode
39.1 tok/s
TTFT
4949 ms
Safe context
8K
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 RX 7600 XT 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: 39.1 tok/s decode · 4.9s TTFT (warm) · 98 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 | B | Runs well | 39.1 tok/s | 2699 ms | 8K |
| Coding | B | Tight fit | 39.1 tok/s | 4949 ms | 8K |
| Agentic Coding | F | Too heavy | 14.5 tok/s | 19479 ms | 8K |
| Reasoning | B | Tight fit | 39.1 tok/s | 5849 ms | 8K |
| RAG | F | Too heavy | 14.5 tok/s | 24349 ms | 8K |
Quantization options
How Baichuan 7B (7B params) fits at each quantization level on RX 7600 XT 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 | 3.9 GB | Medium | B64 |
Q4_K_M | 4 | 4.3 GB | Medium | B64 |
Q5_K_M | 5 | 5.0 GB | High | B65 |
Q6_K | 6 | 5.7 GB | High | B66 |
Q8_0Best for your GPU | 8 | 7.5 GB | Very High | B67 |
F16 | 16 | 14.3 GB | Maximum | F0 |
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 99Upgrade options
Hardware that runs Baichuan 7B well
RX 7900 XTX 24GBBest value
24 GB VRAM (+8)960 GB/s (+672)
A
Raises estimated decode speed by about 151%.98 tok/s decode
Raises estimated decode speed by about 151%.
Adds memory headroom for longer context windows and future model growth.
~$999 MSRP
Radeon AI PRO R9700 32GBAMD upgrade
32 GB VRAM (+16)640 GB/s (+352)
B
Raises estimated decode speed by about 126%.88.4 tok/s decode
Raises estimated decode speed by about 126%.
Adds memory headroom for longer context windows and future model growth.
~$1,899 MSRP