12 GB VRAM (+2)456 GB/s (+76)
B
The raw memory story may look fine, but the software ecosystem is still a constraint here.55.1 tok/s decode
~$249 MSRP
Can Mistral 7B Instruct v0.3 run on Intel Arc B570 10GB?
YES — Runs Great
B67Good
○Estimated from fit model
Mistral 7B Instruct v0.3 needs ~8.1 GB VRAM. Intel Arc B570 10GB has 10.0 GB. With Q4_K_M quantization, expect ~52 tok/s.
Runtime: llama.cppCapacity: RoomyBandwidth: 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) — 8.1 GB, 51.7 tok/s, Runs well
8.1 GB required10.0 GB available
81% VRAM used
Fit status
Runs well
Decode
51.7 tok/s
TTFT
3748 ms
Safe context
8K
Memory
8.1 GB / 10.0 GB
Memory breakdown
Weights4.3 GB
KV Cache2.0 GB
Runtime0.9 GB
Headroom1.0 GB
See how fast it feels
See how fast it feelsMistral 7B Instruct v0.3 on Intel Arc B570 10GB
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: 51.7 tok/s decode · 3.7s TTFT (warm) · 129 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 | 51.7 tok/s | 2044 ms | 8K |
| Coding | B | Runs well | 51.7 tok/s | 3748 ms | 8K |
| Agentic Coding | B | Runs with offload (needs ~0 GB host RAM) | 38.9 tok/s | 7240 ms | 8K |
| Reasoning | B | Runs well | 51.7 tok/s | 4429 ms | 8K |
| RAG | B | Runs with offload (needs ~0 GB host RAM) | 38.9 tok/s | 9049 ms | 8K |
Quantization options
How Mistral 7B Instruct v0.3 (7B params) fits at each quantization level on Intel Arc B570 10GB (10.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | B63 |
Q3_K_S | 3 | 3.4 GB | Low | B64 |
NVFP4 | 4 | 3.9 GB | Medium | B65 |
Q4_K_M | 4 | 4.3 GB | Medium | B65 |
Q5_K_M | 5 | 5.0 GB | High | B65 |
Q6_KBest for your GPU | 6 | 5.7 GB | High | B65 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Get started
Copy-paste commands to run Mistral 7B Instruct v0.3 on your machine.
Run
lms load Mistral-7B-Instruct-v0.3 && lms server startUpgrade options