Falcon H1 7B Instruct needs ~6.4 GB but Intel Arc A370M 4GB only has 4.0 GB. Try a smaller quantization or lighter model.
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
2.4 GB over capacity — needs offload or smaller quantization
Fit status
Too heavy
Decode
3.6 tok/s
TTFT
53845 ms
Safe context
4K
Memory
6.4 GB / 4.0 GB
Offload
40%
Usable VRAM is the main blocker for this model.
Not enough usable memory
The model needs 6.4 GB, but this setup only exposes 4.0 GB of usable VRAM.
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.
Add more VRAM headroom
The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
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 | F | Too heavy | 4.1 tok/s | 25542 ms | 4K |
| Coding | F | Too heavy | 3.6 tok/s | 53845 ms | 4K |
| Agentic Coding | F | Too heavy | 2.8 tok/s | 100991 ms | 4K |
| Reasoning | F | Too heavy | 3.6 tok/s | 63635 ms | 4K |
| RAG | F | Too heavy | 2.8 tok/s | 126239 ms | 4K |
How Falcon H1 7B Instruct (7B params) fits at each quantization level on Intel Arc A370M 4GB (4.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 2.7 GB | Low | F0 |
Q3_K_S | 3 | 3.4 GB | Low | F0 |
NVFP4 | 4 |
Upgrade options
Intel Arc A380 6GBBest valueMakes the model fit on the accelerator instead of staying completely out of reach.
Raises estimated decode speed by about 264%.
~$139 MSRP
Intel Arc A580 8GBBudget pickMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$179 MSRP
Intel Arc B570 10GBIntel upgradeMakes the model fit on the accelerator instead of staying completely out of reach.
Removes host-memory offload, which is usually the single biggest latency and throughput win.
~$219 MSRP
No, Falcon H1 7B Instruct requires more memory than Intel Arc A370M 4GB provides.
Falcon H1 7B Instruct (7B parameters) requires approximately 6.4 GB of memory with Q4_K_M quantization.
The recommended quantization for Falcon H1 7B Instruct is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A370M 4GB, Falcon H1 7B Instruct achieves approximately 3.6 tokens per second decode speed with a time-to-first-token of 53845ms using Q4_K_M quantization.
For coding workloads, Falcon H1 7B Instruct on Intel Arc A370M 4GB receives a F grade with 3.6 tok/s and 4K context.
On Intel Arc A370M 4GB, Falcon H1 7B Instruct can safely use up to 4K tokens of context. The model's official context limit is —, 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/hf-tiiuae--falcon-h1-7b-instruct-gguf-on-arc-a370m-4gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
| Medium |
| F0 |
Q4_K_M | 4 | 4.3 GB | Medium | F0 |
Q5_K_M | 5 | 5.0 GB | High | F0 |
Q6_K | 6 | 5.7 GB | High | F0 |
Q8_0 | 8 | 7.5 GB | Very High | F0 |
F16 | 16 | 14.3 GB | Maximum | F0 |
Add more VRAM headroom. The first useful upgrade is more dedicated VRAM so you can fit the model without shrinking context or dropping to a much lower quant.
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.
