MacBook Pro M4 Max 48GBBudget pick
48 GB Unified (+16)546 GB/s (+34)
B
Raises estimated decode speed by about 31%.87.8 tok/s decode
Raises estimated decode speed by about 31%.
~$2,499 MSRP
Can Baichuan 7B run on Radeon Pro W6800 32GB?
YES — Runs Great
B68Good
○Estimated from fit model
Baichuan 7B needs ~16.2 GB VRAM. Radeon Pro W6800 32GB has 32.0 GB. With Q4_K_M quantization, expect ~67 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: MediumStack: 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) — 16.2 GB, 67.1 tok/s, Runs well
16.2 GB required32.0 GB available
51% VRAM used
Fit status
Runs well
Decode
67.1 tok/s
TTFT
2883 ms
Safe context
8K
Memory
16.2 GB / 32.0 GB
Memory breakdown
Weights4.3 GB
KV Cache7.8 GB
Runtime0.9 GB
Headroom3.2 GB
See how fast it feels
See how fast it feelsBaichuan 7B on Radeon Pro W6800 32GB
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: 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 | B | Runs well | 67.1 tok/s | 1573 ms | 8K |
| Coding | B | Runs well | 67.1 tok/s | 2883 ms | 8K |
| Agentic Coding | A | Runs well | 67.1 tok/s | 4194 ms | 8K |
| Reasoning | B | Runs well | 67.1 tok/s | 3407 ms | 8K |
| RAG | A | Runs well | 67.1 tok/s | 5242 ms | 8K |
Quantization options
How Baichuan 7B (7B params) fits at each quantization level on Radeon Pro W6800 32GB (32.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B59 |
Q3_K_S | 3 | 3.4 GB | Low | B59 |
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 99Upgrade options