Mac mini M4 64GBBudget pick
64 GB Unified (+40)
D
Adds memory headroom for longer context windows and future model growth.2 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Falcon H1 Tiny 90M Instruct needs ~3.8 GB VRAM. RTX 5090 Laptop 24GB has 24.0 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
3.3M
Memory
3.8 GB / 24.0 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.
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.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | D | Runs well | 2.0 tok/s | 52800 ms | 1.6M |
| Coding | D | Runs well | 2.0 tok/s | 96800 ms | 3.3M |
| Agentic Coding | D | Runs well | 2.0 tok/s | 140800 ms | 6.5M |
| Reasoning | D | Runs well | 2.0 tok/s | 114400 ms | 3.3M |
| RAG | D | Runs well | 2.0 tok/s | 176000 ms | 6.5M |
How Falcon H1 Tiny 90M Instruct (0.09000000357627869B params) fits at each quantization level on RTX 5090 Laptop 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.0 GB | Low | C43 |
Q3_K_S | 3 | 0.0 GB | Low | C43 |
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 startUpgrade options
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Adds memory headroom for longer context windows and future model growth.
~$1,599 MSRP
Yes, RTX 5090 Laptop 24GB 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 3.8 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 RTX 5090 Laptop 24GB, 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 RTX 5090 Laptop 24GB receives a D grade with 2.0 tok/s and 3.3M context.
On RTX 5090 Laptop 24GB, Falcon H1 Tiny 90M Instruct can safely use up to 3.3M 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-rtx-5090-laptop-24gb" 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 |
| C43 |
Q4_K_M | 4 | 0.1 GB | Medium | C43 |
Q5_K_M | 5 | 0.1 GB | High | C43 |
Q6_K | 6 | 0.1 GB | High | C43 |
Q8_0 | 8 | 0.1 GB | Very High | C43 |
F16Best for your GPU | 16 | 0.2 GB | Maximum | C43 |
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.