TinyLlama 1.1B Chat v1.0 needs ~2.9 GB VRAM. Intel Arc Pro A60 12GB has 12.0 GB. With Q4_K_M quantization, expect ~15 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
15.4 tok/s
TTFT
12571 ms
Safe context
1.1M
Memory
2.9 GB / 12.0 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 | C | Runs well | 15.4 tok/s | 6857 ms | 738K |
| Coding | C | Runs well | 15.4 tok/s | 12571 ms | 1.1M |
| Agentic Coding | C | Runs well | 15.4 tok/s | 18286 ms | 1.1M |
| Reasoning | C | Runs well | 15.4 tok/s | 14857 ms | 1.1M |
| RAG | C | Runs well | 15.4 tok/s | 22857 ms | 1.1M |
How TinyLlama 1.1B Chat v1.0 (1.100000023841858B params) fits at each quantization level on Intel Arc Pro A60 12GB (12.0 GB usable).
| Quant | Bits | VRAM | Quality | Fit |
|---|---|---|---|---|
Q2_K | 2 | 0.4 GB | Low | C47 |
Q3_K_S | 3 | 0.5 GB | Low | C47 |
NVFP4 | 4 |
Copy-paste commands to run TinyLlama 1.1B Chat v1.0 on your machine.
Run
lms load hf-thebloke--tinyllama-1-1b-chat-v1-0-gguf && lms server startUpgrade options
RTX 5060 Ti 16GBBudget pickRaises estimated decode speed by about 36%.
Adds memory headroom for longer context windows and future model growth.
This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.
~$449 MSRP
RTX 5070 Ti 16GBBest valueRaises estimated decode speed by about 36%.
Adds memory headroom for longer context windows and future model growth.
This is not only a hardware jump. It also gives you a cleaner runtime ecosystem for local LLM tooling.
~$749 MSRP
Yes, Intel Arc Pro A60 12GB can run TinyLlama 1.1B Chat v1.0 with a C grade (Runs well). Expected decode speed: 15.4 tok/s.
TinyLlama 1.1B Chat v1.0 (1.100000023841858B parameters) requires approximately 2.9 GB of memory with Q4_K_M quantization.
The recommended quantization for TinyLlama 1.1B Chat v1.0 is Q4_K_M, which balances quality and memory efficiency.
On Intel Arc Pro A60 12GB, TinyLlama 1.1B Chat v1.0 achieves approximately 15.4 tokens per second decode speed with a time-to-first-token of 12571ms using Q4_K_M quantization.
For coding workloads, TinyLlama 1.1B Chat v1.0 on Intel Arc Pro A60 12GB receives a C grade with 15.4 tok/s and 1.1M context.
On Intel Arc Pro A60 12GB, TinyLlama 1.1B Chat v1.0 can safely use up to 1.1M 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-thebloke--tinyllama-1-1b-chat-v1-0-gguf-on-arc-pro-a60-12gb" 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 |
| C47 |
Q4_K_M | 4 | 0.7 GB | Medium | C47 |
Q5_K_M | 5 | 0.8 GB | High | C47 |
Q6_K | 6 | 0.9 GB | High | C47 |
Q8_0 | 8 | 1.2 GB | Very High | C48 |
F16Best for your GPU | 16 | 2.3 GB | Maximum | C49 |
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.