TinyLlama 1.1B Chat v1.0 imatrix needs ~5.6 GB VRAM. MacBook Pro M3 Pro 36GB has 25.9 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
2.5M
Memory
5.6 GB / 25.9 GB
This setup is broadly balanced for this model.
Shared-memory contention still exists
The OS, browser, and inference runtime all compete for the same physical memory pool, so real-world headroom is less forgiving than raw capacity suggests.
| Workload | Grade | Fit | Decode | TTFT | Context |
|---|---|---|---|---|---|
| Chat | C | Runs well | 15.4 tok/s | 6857 ms | 1.6M |
| Coding | C | Runs well | 15.4 tok/s | 12571 ms | 2.5M |
| Agentic Coding | C | Runs well | 15.4 tok/s | 18286 ms | 2.5M |
| Reasoning | C | Runs well | 15.4 tok/s | 14857 ms | 2.5M |
| RAG | C | Runs well | 15.4 tok/s | 22857 ms | 2.5M |
How TinyLlama 1.1B Chat v1.0 imatrix (1.100000023841858B params) fits at each quantization level on MacBook Pro M3 Pro 36GB (25.9 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 TinyLlama 1.1B Chat v1.0 imatrix on your machine.
Run
lms load hf-duyntnet--tinyllama-1-1b-chat-v1-0-imatrix-gguf && lms server startYes, MacBook Pro M3 Pro 36GB can run TinyLlama 1.1B Chat v1.0 imatrix with a C grade (Runs well). Expected decode speed: 15.4 tok/s.
TinyLlama 1.1B Chat v1.0 imatrix (1.100000023841858B parameters) requires approximately 5.6 GB of memory with Q4_K_M quantization.
The recommended quantization for TinyLlama 1.1B Chat v1.0 imatrix is Q4_K_M, which balances quality and memory efficiency.
On MacBook Pro M3 Pro 36GB, TinyLlama 1.1B Chat v1.0 imatrix 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 imatrix on MacBook Pro M3 Pro 36GB receives a C grade with 15.4 tok/s and 2.5M context.
On MacBook Pro M3 Pro 36GB, TinyLlama 1.1B Chat v1.0 imatrix can safely use up to 2.5M 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-duyntnet--tinyllama-1-1b-chat-v1-0-imatrix-gguf-on-m3-pro-36gb" 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 | 0.7 GB | Medium | C43 |
Q5_K_M | 5 | 0.8 GB | High | C43 |
Q6_K | 6 | 0.9 GB | High | C43 |
Q8_0 | 8 | 1.2 GB | Very High | C43 |
F16Best for your GPU | 16 | 2.3 GB | Maximum | C43 |
Not always. MacBook Pro M3 Pro 36GB can often fit larger models thanks to unified memory, but a discrete GPU with dedicated high-bandwidth VRAM may still decode faster once the model fits. For this combination, the important distinction is capacity versus sustained throughput.