Falcon 7B Instruct needs ~6.3 GB VRAM. Intel Arc B570 10GB has 10.0 GB. With Q4_K_M quantization, expect ~48 tok/s.
Operating mode
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
Fit status
Runs well
Decode
52.8 tok/s
TTFT
3667 ms
Safe context
8K
Memory
6.3 GB / 10.0 GB
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.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | B | Runs well | 52.8 tok/s | 2000 ms | 8K |
| Coding | B | Runs well | 48.1 tok/s | 4029 ms | 8K |
| Agentic Coding | B | Runs well | 52.8 tok/s | 5334 ms | 8K |
| Reasoning | B | Runs well | 52.8 tok/s | 4334 ms | 8K |
| RAG | B | Runs well | 52.8 tok/s | 6667 ms | 8K |
How Falcon 7B Instruct (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 | B67 |
Q3_K_S | 3 | 3.4 GB | Low | B68 |
NVFP4 | 4 |
Copy-paste commands to run Falcon 7B Instruct on your machine.
Run
lms load falcon-7b-instruct && lms server startUpgrade options
GTX 1080 Ti 11GBBudget pickRaises estimated decode speed by about 39%.
Moves you onto CUDA, which still has the broadest local-AI runtime coverage.
This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.
~$699 MSRP
RTX 2080 Ti 11GBBest valueRaises estimated decode speed by about 86%.
Moves you onto CUDA, which still has the broadest local-AI runtime coverage.
This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.
~$999 MSRP
Yes, Intel Arc B570 10GB can run Falcon 7B Instruct with a B grade (Runs well). Expected decode speed: 48.1 tok/s.
Falcon 7B Instruct (7B parameters) requires approximately 6.3 GB of memory with Q4_K_M quantization.
The recommended quantization for Falcon 7B Instruct is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc B570 10GB, Falcon 7B Instruct achieves approximately 48.1 tokens per second decode speed with a time-to-first-token of 4029ms using Q4_K_M quantization.
For coding workloads, Falcon 7B Instruct on Intel Arc B570 10GB receives a B grade with 48.1 tok/s and 8K context.
On Intel Arc B570 10GB, Falcon 7B Instruct can safely use up to 8K tokens of context. The model's official context limit is 8K, but available memory constrains the safe maximum.
Paste this snippet into any page to show a live fit card.
<iframe src="https://willitrunai.com/embed/falcon-7b-instruct-on-arc-b570-10gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
3.9 GB |
| Medium |
| B69 |
Q4_K_M | 4 | 4.3 GB | Medium | B69 |
Q5_K_M | 5 | 5.0 GB | High | B69 |
Q6_KBest for your GPU | 6 | 5.7 GB | High | B69 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
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.
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.