Mac mini M4 64GBBudget pick
64 GB Unified (+40)
D
Adds memory headroom for longer context windows and future model growth.14 tok/s decode
Adds memory headroom for longer context windows and future model growth.
~$1,099 MSRP
Falcon3 1B Instruct abliterated needs ~4.0 GB VRAM. Intel Arc Pro B60 24GB has 24.0 GB. With Q4_K_M quantization, expect ~14 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
14.0 tok/s
TTFT
13829 ms
Safe context
2.7M
Memory
4.0 GB / 24.0 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 | D | Runs well | 14.0 tok/s | 7543 ms | 1.6M |
| Coding | D | Runs well | 14.0 tok/s | 13829 ms | 2.7M |
| Agentic Coding | D | Runs well | 14.0 tok/s | 20114 ms | 2.7M |
| Reasoning | D | Runs well | 14.0 tok/s | 16343 ms | 2.7M |
| RAG | D | Runs well | 14.0 tok/s | 25143 ms | 2.7M |
How Falcon3 1B Instruct abliterated (1B params) fits at each quantization level on Intel Arc Pro B60 24GB (24.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.4 GB | Low | C43 |
Q3_K_S | 3 | 0.5 GB | Low | C43 |
NVFP4 | 4 |
Copy-paste commands to run Falcon3 1B Instruct abliterated on your machine.
Run
lms load hf-bartowski--falcon3-1b-instruct-abliterated-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, Intel Arc Pro B60 24GB can run Falcon3 1B Instruct abliterated with a D grade (Runs well). Expected decode speed: 14.0 tok/s.
Falcon3 1B Instruct abliterated (1B parameters) requires approximately 4.0 GB of memory with Q4_K_M quantization.
The recommended quantization for Falcon3 1B Instruct abliterated is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc Pro B60 24GB, Falcon3 1B Instruct abliterated achieves approximately 14.0 tokens per second decode speed with a time-to-first-token of 13829ms using Q4_K_M quantization.
For coding workloads, Falcon3 1B Instruct abliterated on Intel Arc Pro B60 24GB receives a D grade with 14.0 tok/s and 2.7M context.
On Intel Arc Pro B60 24GB, Falcon3 1B Instruct abliterated can safely use up to 2.7M 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-bartowski--falcon3-1b-instruct-abliterated-gguf-on-arc-pro-b60-24gb" width="400" height="180" frameborder="0" style="border:none;border-radius:12px;overflow:hidden;" title="Will It Run AI — fit result"></iframe>
Preview:
0.6 GB |
| Medium |
| C43 |
Q4_K_M | 4 | 0.6 GB | Medium | C43 |
Q5_K_M | 5 | 0.7 GB | High | C43 |
Q6_K | 6 | 0.8 GB | High | C43 |
Q8_0 | 8 | 1.1 GB | Very High | C43 |
F16Best for your GPU | 16 | 2.1 GB | Maximum | C44 |
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.