Falcon H1 Tiny 90M Instruct needs ~1.5 GB VRAM. Intel Arc A370M 4GB has 4.0 GB. With Q4_K_M quantization, expect ~2 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
2.0 tok/s
TTFT
96800 ms
Safe context
423K
Memory
1.5 GB / 4.0 GB
This model fits, but memory bandwidth is the part holding decode speed back.
Throughput will feel slow
Estimated decode speed is only 2.0 tok/s, so this is more of a technical fit than a comfortable daily-driver setup.
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.
Prioritize bandwidth, not only capacity
If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
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 | D | Runs well | 2.0 tok/s | 52800 ms | 212K |
| Coding | D | Runs well | 2.0 tok/s | 96800 ms | 423K |
| Agentic Coding | D | Runs well | 2.0 tok/s | 140800 ms | 846K |
| Reasoning | D | Runs well | 2.0 tok/s | 114400 ms | 423K |
| RAG | D | Runs well | 2.0 tok/s | 176000 ms | 846K |
How Falcon H1 Tiny 90M Instruct (0.09000000357627869B params) fits at each quantization level on Intel Arc A370M 4GB (4.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.0 GB | Low | C53 |
Q3_K_S | 3 | 0.0 GB | Low | C53 |
NVFP4 | 4 |
Copy-paste commands to run Falcon H1 Tiny 90M Instruct on your machine.
Run
lms load hf-tiiuae--falcon-h1-tiny-90m-instruct-gguf && lms server startYes, Intel Arc A370M 4GB can run Falcon H1 Tiny 90M Instruct with a D grade (Runs well). Expected decode speed: 2.0 tok/s.
Falcon H1 Tiny 90M Instruct (0.09000000357627869B parameters) requires approximately 1.5 GB of memory with Q4_K_M quantization.
The recommended quantization for Falcon H1 Tiny 90M Instruct is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc A370M 4GB, Falcon H1 Tiny 90M Instruct achieves approximately 2.0 tokens per second decode speed with a time-to-first-token of 96800ms using Q4_K_M quantization.
For coding workloads, Falcon H1 Tiny 90M Instruct on Intel Arc A370M 4GB receives a D grade with 2.0 tok/s and 423K context.
On Intel Arc A370M 4GB, Falcon H1 Tiny 90M Instruct can safely use up to 423K 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-tiny-90m-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:
0.1 GB |
| Medium |
| C54 |
Q4_K_M | 4 | 0.1 GB | Medium | C54 |
Q5_K_M | 5 | 0.1 GB | High | C54 |
Q6_K | 6 | 0.1 GB | High | C54 |
Q8_0 | 8 | 0.1 GB | Very High | C54 |
F16Best for your GPU | 16 | 0.2 GB | Maximum | C54 |
Prioritize bandwidth, not only capacity. If this workload feels slow, the next useful step is often a GPU tier with materially faster memory bandwidth rather than only a small bump in capacity.
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.