10 GB VRAM (+2)380 GB/s (+156)
B
Raises estimated decode speed by about 87%.52.2 tok/s decode
Raises estimated decode speed by about 87%.
Adds memory headroom for longer context windows and future model growth.
~$219 MSRP
Can Qwen 2.5 Math 7B run on Intel Arc A550M 8GB?
YES — Tight Fit
C54Usable
○Estimated from fit model
Qwen 2.5 Math 7B needs ~6.8 GB VRAM. Intel Arc A550M 8GB has 8.0 GB. With Q4_K_M quantization, expect ~26 tok/s.
Runtime: llama.cppCapacity: TightBandwidth: Very lowStack: StandardBottleneck: Memory bandwidth
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) — 6.8 GB, 27.9 tok/s, Tight fit
6.8 GB required8.0 GB available
85% VRAM used
Fit status
Tight fit
Decode
27.9 tok/s
TTFT
6937 ms
Safe context
4K
Memory
6.8 GB / 8.0 GB
Memory breakdown
Weights4.3 GB
KV Cache0.9 GB
Runtime0.9 GB
Headroom0.8 GB
See how fast it feels
See how fast it feelsQwen 2.5 Math 7B on Intel Arc A550M 8GB
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: 27.9 tok/s decode · 6.9s TTFT (warm) · 70 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 | B | Runs well | 27.9 tok/s | 3784 ms | 4K |
| Coding | C | Tight fit | 25.7 tok/s | 7532 ms | 4K |
| Agentic Coding | C | Runs with offload | 27.9 tok/s | 10090 ms | 4K |
| Reasoning | C | Tight fit | 27.9 tok/s | 8198 ms | 4K |
| RAG | C | Runs with offload | 27.9 tok/s | 12613 ms | 4K |
Quantization options
How Qwen 2.5 Math 7B (7B params) fits at each quantization level on Intel Arc A550M 8GB (8.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B57 |
Q3_K_S | 3 | 3.4 GB | Low | B58 |
NVFP4 | 4 |
Get started
Copy-paste commands to run Qwen 2.5 Math 7B on your machine.
Run
docker run --rm -it ghcr.io/ggerganov/llama.cpp:full \
--hf-repo "Qwen/Qwen2.5-Math-7B-Instruct" \
--hf-file "Qwen2.5-Math-7B-Instruct-Q4_K_M.gguf" \
-c 4096 -ngl 99Upgrade options
Hardware that runs Qwen 2.5 Math 7B well
12 GB VRAM (+4)456 GB/s (+232)
B
Raises estimated decode speed by about 100%.55.7 tok/s decode
Raises estimated decode speed by about 100%.
Adds memory headroom for longer context windows and future model growth.
~$249 MSRP
16 GB VRAM (+8)560 GB/s (+336)
B
Raises estimated decode speed by about 130%.64.1 tok/s decode
Raises estimated decode speed by about 130%.
Adds memory headroom for longer context windows and future model growth.
~$349 MSRP