Intel Arc Pro B60 24GBGünstige Wahl24 GB VRAM (+8)
A
Adds memory headroom for longer context windows and future model growth.57.7 tok/s Dekodierung
Adds memory headroom for longer context windows and future model growth.
ca. $599 MSRP
Can Baichuan 7B run on Intel Arc A770 16GB?
YES — Tight Fit
B69Good
Estimated from fit model
Baichuan 7B needs ~14.6 GB VRAM. Intel Arc A770 16GB has 16.0 GB. With Q4_K_M quantization, expect ~59 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.
Select quantization to explore
Q4_K_M (Medium quality) — 14.6 GB, 59.0 tok/s, Tight fit
14.6 GB required16.0 GB available
91% VRAM used
Fit status
Tight fit
Decode
59.0 tok/s
TTFT
3280 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 Intel Arc A770 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: 59.0 tok/s decode · 3.3s TTFT (warm) · 148 tok/s prefill
What limits this setup
The raw memory story may look fine, but the software ecosystem is still a constraint here.
Runtime ecosystem is narrower than CUDA
Intel GPUs can look attractive on memory per dollar, but local AI tooling, kernels, and model coverage are still broader and easier on CUDA today.
Best improvement path
Prefer CUDA if you want the path of least resistance
If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Performance by workload
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | A | Runs well | 59.0 tok/s | 1789 ms | 8K |
| Coding | B | Tight fit | 59.0 tok/s | 3280 ms | 8K |
| Agentic Coding | F | Too heavy | 21.8 tok/s | 12912 ms | 8K |
| Reasoning | B | Tight fit | 59.0 tok/s | 3877 ms | 8K |
| RAG | F | Too heavy | 21.8 tok/s | 16140 ms | 8K |
Quantization options
How Baichuan 7B (7B params) fits at each quantization level on Intel Arc A770 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-Optionen
Hardware, die Baichuan 7B gut ausführt
Intel Arc Pro B60 24GBGünstige WahlRX 7900 XT 20GBBestes Preis-Leistungs-Verhältnis
20 GB VRAM (+4)800 GB/s (+240)
A
Raises estimated decode speed by about 66%.98 tok/s Dekodierung
Raises estimated decode speed by about 66%.
Adds memory headroom for longer context windows and future model growth.
ca. $899 MSRP
Frequently asked questions
Can Intel Arc A770 16GB run Baichuan 7B?
Yes, Intel Arc A770 16GB can run Baichuan 7B with a B grade (Tight fit). Expected decode speed: 59.0 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 Intel Arc A770 16GB?
On Intel Arc A770 16GB, Baichuan 7B achieves approximately 59.0 tokens per second decode speed with a time-to-first-token of 3280ms using Q4_K_M quantization.
Can Intel Arc A770 16GB run Baichuan 7B for coding?
For coding workloads, Baichuan 7B on Intel Arc A770 16GB receives a B grade with 59.0 tok/s and 8K context.
What context window can Baichuan 7B use on Intel Arc A770 16GB?
On Intel Arc A770 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.
What should I upgrade first if Baichuan 7B feels slow on Intel Arc A770 16GB?
Prefer CUDA if you want the path of least resistance. If your goal is maximum runtime coverage, easier troubleshooting, and better support for new local AI releases, CUDA is usually still the safer upgrade path.
Would CUDA be a better path than Intel Arc A770 16GB for Baichuan 7B?
Often yes, if your goal is the easiest setup and the widest runtime support. Intel can offer attractive memory capacity, but CUDA still tends to win on tooling maturity, guides, kernels, and model coverage for local AI.
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